a meta-analysis of supply chain integration and … · a meta-analysis of supply chain integration...

A META-ANALYSIS OF SUPPLY CHAIN INTEGRATIONAND FIRM PERFORMANCE

RUDOLF LEUSCHNER, DALE S. ROGERSRutgers University

FRANÇOIS F. CHARVETStaples, Inc., Northeastern University

As supply chain activities become more dispersed among customers,suppliers and service providers, there is an increased need for customersand suppliers to work together more closely. Supply chain integration(SCI) has been a highly researched topic during the last 20 years. A meta-analytic approach is used to provide a quantitative review of the empiricalliterature in SCI, and examines relevant design and contextual factors.Eighty independent samples across 86 peer-reviewed journal articles, yield-ing a total of 17,467 observations, were obtained and analyzed. Whilegeneral support exists in favor of the positive impact of SCI on firm per-formance in the literature, this research helps clarify mixed findings thatpresently exist. Our results indicate that there is a positive and significantcorrelation between SCI and firm performance. Additional subgroups andmoderators are tested and provide nuanced views of the scope and specificdimensions of SCI, firm performance and their relationships.

Keywords: supply chain integration; performance measurement; meta-analysis;archival research; resource-based view; resource-advantage theory; relational view

INTRODUCTIONAs supply chains mature, their complexity increases.

Managers are asked to improve productivity whileincreasing customer service. Shareholders expect prof-itability to grow quarter over quarter. These internaland external forces have the effect that often tasks thatpreviously were performed internally become outsour-ced (Williamson, 2008). This results in increasedinteraction among firms in a supply chain andrequires closer relationships to ensure that the flowsof product, information and payments operate effi-ciently (Flynn, Huo, & Zhao, 2010; Frohlich & West-brook, 2001; Thun, 2010; Wagner, 2003). Managingthese relationships requires cross-functional and cross-firm business processes with appropriate levels ofinformation sharing, operational coordination andselect close partnerships (Charvet, 2008; Lambert &Cooper, 2000; Rai, Patnayakuni, & Seth, 2006;Sanders, 2007).

The term “supply chain integration” (SCI) has beendefined as the extent of engagement with suppliersand customers (Frohlich & Westbrook, 2001). Theterms “supply chain collaboration” (Stank, Keller, &Daugherty, 2001) and “supply chain coordination”(Carr, Kaynak, & Muthusamy, 2008) are used todescribe elements of SCI. As “collaboration beginswith customers and extends back through the firm(…), integration is needed both internally andexternally (Stank et al., 2001, p. 29).” In addition,“integration involves coordinating (…) the forwardphysical flow of deliveries” and “the backward coordi-nation of information technology” (Frohlich &Westbrook, 2001). Therefore, it is believed that collab-oration and coordination are elements of SCI(Mackelprang, Robinson, & Webb, 2012).The focus of this research is on SCI. To integrate all

of the studies we collected into one framework, weprovide the following definition of SCI for thisresearch. SCI is the scope and strength of linkages insupply chain processes across firms. Information,operational and relational integration facilitate thelinkages in supply chain processes between firms. The

Acknowledgment: We would like to thank Senay Demirkan Delice

for her help in the initial data collection.

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scope of SCI can be integration with customers, sup-pliers, internal or external. The overall premise of ourresearch is to test whether tighter integration leads tobetter firm performance.A large increase in research investigating SCI has

been observed in the SCM discipline, as shown inFigure 1. Until now, only a few qualitative reviews ofthe SCI literature can be found (Chen, Daugherty, &Landry, 2009b; Fabbe-Costes & Jahre, 2008; Simatup-ang & Sridharan, 2005; Van der Vaart & van Donk,2008). While such studies have a substantial contribu-tion to the field, they do have inherent drawbacksbecause it is challenging to objectively tie together pri-mary research. As the debate between Hanushek(1989, 1994) and Hedges, Laine, and Greenwald(1994a,b) has shown, a subjective review of existingliterature may be just that. In addition, the results of ameta-analysis can be used subsequently to suggestareas in need of further investigation. SCI has oftenbeen operationalized and measured differently, andthis adds to the challenge of integrating the findings.Overall, empirical evidence seems to support the posi-tive impact of SCI on firm performance, howevermixed findings are not uncommon (Flynn et al.,2010). In addition, the selection of firm performance

as the dependent variable is a natural link and hasbeen critical in the literature (Fabbe-Costes & Jahre,2008; Van der Vaart & van Donk, 2008). The moreimportant decision is how to measure and evaluatefirm performance, which is multi-dimensional. It hasbeen show that a single study does not have enoughpower, due to the relatively small sample size, toexplain the magnitude of a statistical relationship(Hunter, 2001; Lipsey & Wilson, 2001). Therefore,aggregating several studies into a meta-analysis is ofcritical importance in order to draw conclusions thatare valid beyond the limited situations in which theywere obtained and make empirical generalizations(Leone & Schulz, 1980).The primary purpose for this study is thus to provide

the first comprehensive, quantitative and integrativereview of empirical research linking SCI to overall firmperformance. The methodological advantage of a meta-analytic study is that statistical artifacts such as sam-pling or measurement error can be accounted for (Hun-ter & Schmidt, 1990). Another advantage is the abilityto examine how various study design factors may affectthe relationship between SCI and firm performance:(1) Is there evidence of a positive correlation betweenSCI and firm performance? (2) Does the correlation

FIGURE 1Published Supply Chain Integration Articles

Note: Articles were identified via a keyword search on “supply chain integration”and “supply chain collaboration” among peer-reviewed articles in the EBSCOBusiness Source Complete database where the search was performed on title,abstract and keywords.

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A Meta-Analysis of SCI and Firm Performance

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between SCI and firm performance vary across differentdimensions and operationalizations of SCI? and (3)Does the correlation between SCI and firm perfor-mance vary across different performance dimensions?The remainder of the study is organized as follows.

The theoretical background and research hypothesesare developed in the following section. Following thatsection, the research methodology is described andresults of the meta-analysis are reported. Last, conclu-sions are presented, including theoretical implications,managerial implications, limitations and recommen-dations for future research.

THEORETICAL DEVELOPMENTThe process of achieving and maintaining higher

levels of integration is complex and may demandunwarranted resources. To add structure to the rela-tionship of SCI to firm performance, researchers havegrounded their studies in a variety of organizationaltheories. An overview of the most commonly usedtheoretical bases is provided next and highlighted inTable 1. Our primary theoretical focus in this meta-analysis is on the resource-based view (RBV) of thefirm with the extensions of resource-advantage (R-A)theory and the relational view (RV) of the firm. Wealso use secondary but important theoretical lenses,which are described later.

Resource-Based View of the FirmThe RBV posits that firms can be viewed as collec-

tions of resources, some of which can be consideredstrategic resources (Penrose, 1959; Wernerfelt, 1984).Strategic resources are valuable, rare and imperfectlyimitable and substitutable (Barney, 1991). As they aredistributed heterogeneously across firms, they canresult in a sustained competitive advantage (Barney,1991; Peteraf, 1993). Supply chain scholars haveacknowledged that internal/cross-functional and exter-nal integration with customers and suppliers can becomplex and requires unique capabilities that may bedifficult or costly to implement (Barney, 2012; Chen,Daugherty, & Roath, 2009a; Chen et al., 2009b). SCIcan be seen as an internal strategic resource that couldresult in a competitive advantage and improved firmperformance (Barney, 2012).

Resource-Advantage Theory and the RV of the Firm.An extension of RBV, R-A theory, focuses not just onresources per se, but more specifically on advanta-geous resources, which give firms a competitive advan-tage (Hunt & Davis, 2008). Resources under R-Atheory are tied to their contribution in producing amarket offering that has value as perceived by custom-ers and the degree to which they are available areused to create a competitive advantage (Hunt & Davis,2012; Priem & Swink, 2012). Another extension of

the RBV, the RV, postulates that firms can benefitfrom inter-firm integration and strategic partnershipsto acquire valuable resources they lack in-house (Dyer& Singh, 1998). Whereas the RBV focuses on internalstrategic resources, the RV contends that a competitiveadvantage also originates from inter-firm resourcesthat cannot be captured or owned by one firm in iso-lation (Dyer & Singh, 1998; Lavie, 2006; Lorenzoni &Lipparini, 1999). Inter-firm integration can oftenresult in win–win situations where the total supplychain benefits are increased due to the use of hard toimitate specialized assets, skills and information.Mesquita, Anand, and Brush (2008) argue that theRBV and RV can be seen as complementary ratherthan competitive theories. They present empirical evi-dence showing that joint knowledge acquisition, sup-pliers’ investment in dyad-specific assets andcapabilities, and buyer-supplier alliance relational gov-ernance are partnership-specific resources that cannotbe explained by the RBV alone.

Secondary Theoretical LensesIn addition to the primary theories, four secondary

theories were identified among the articles in the sam-ple. With roots in both the RBV and RV, the knowl-edge-based view (KBV; Argote, 1999; Grant, 1996;Kogut & Zander, 1992) states that SCI can help firmscoordinate and deploy knowledge resources byexchanging valuable information across the organiza-tional boundary with key suppliers and customers.Social exchange theory (SET), with origins in sociology(Blau, 1964; Emerson, 1962) and relational marketing(Dwyer, Schur, & Oh, 1987; Morgan & Hunt, 1994),has been used to explain the need for closer interactionbetween organizations, which posits that the basicmotivation for integration is the seeking of rewards andavoidance of punishments (Emerson, 1962). Transac-tion cost economics (TCE) also highlighted some of theintegration benefits (Williamson, 1975). Transactioncosts are the expenses generated by identifying fairmarket prices, negotiating and carrying out economicexchange. With respect to SCI, TCE predicts that firmsshould fare better if they appropriately adjust theirgovernance mechanisms to the underlying transactions(Williamson, 1975, 1991, 2008). The informationprocessing theory (IPT) posits that coping with infor-mation is an organization’s main task and that moreinformation has a positive link with performance(Galbraith, 1973). This, however, is not a constanteffect as an inflection point can be reached at whichmore information does not lead to better performance.

Hypothesis DevelopmentThe theoretical bases provide a lens for examining

the 80 independent samples included in this meta-analysis. Researchers in our sample have used different

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definitions, dimensions and operationalizations toexamine SCI (for a more thorough review see: Fabbe-Costes & Jahre, 2008; Van der Vaart & van Donk,2008). While there was divergence among researchers,an aggregate view of SCI is important and is used as astarting point for evidence that SCI indeed has aneffect on firm performance. Generally, a meta-analysis

can be utilized effectively not only to examine narrow,well-defined constructs, but also to assess relation-ships involving more broadly defined constructs(Crook, Ketchen, Combs, & Todd, 2008; Nair, 2006).As mentioned earlier, integration among companieswithin the supply chain can be complex and requiresunique capabilities that may be difficult or costly to

TABLE 1

Theoretical Bases for Supply Chain Integration

Theory Relevant Themes Sample Empirical Studies

Resource-basedview (RBV)Barney (1991), andWernerfelt (1984)

Firms can develop a unique capability andexcel in integrating with firms in thesupply chain. Supply chain integrationas a strategic resource can lead to asustained competitive advantage andsuperior firm performance.

Chen et al. (2009b),and Mesquita et al.(2008)

Relational view (RV)Dyer and Singh (1998),Lavie (2006), and Lorenzoniand Lipparini (1999)

Strategic resources can also arise atthe inter-firm level. The achievementof a competitive advantage via supplychain integration is dependent on thegeneration of relational rentsbetween multiple firms.

Deveraj, Krajewski,and Wei (2007),and Mesquitaet al. (2008)

Knowledge-based view (KBV)Argote (1999), Grant (1996),and Kogut and Zander (1992)

Supply chain integration helpsdeploy knowledge resources byexchanging valuable information(operational and strategic information)across the organizational boundarywith supply chain partners.

Rosenzweig et al.(2003), Paulraj et al.(2008), Swinket al. (2007), and Raiet al. (2006)

Social exchange theory (SET)Blau (1964), Emerson (1962),Dwyer et al. (1987), MacNeil(1980), and Morganand Hunt (1994)

Relational governance mechanismssuch as trust and commitment canbe used to achieve a higher degreeof integration between supply chainpartners. Relational exchangerelationships can be more effectiveand efficient, though, the risk ofopportunism can dampenthese benefits.

Johnston et al. (2004),Prahinski and Benton(2004), Golicic andMentzer (2005),Griffith, Harvey, and Lusch(2006), Gulati and Sytch(2007), and Nyaga,Whipple, & Lynch (2010)

Transaction costeconomics (TCE)Coase (1937), Rindfleischand Heide (1997), andWilliamson (1975, 1991,2008)

Supply chain integration may helpfirms reduce the burden of transactioncost, and implement safeguardmechanisms to mitigate the threatof opportunism. Asset specificityand uncertainty are importantfactors to consider when selectingthe most appropriate inter-organizational governance form.

Lee, Kwon, andSeverance (2007)

Information processingtheory (IPT)Lawrence and Lorsch (1967),Thompson (1967), Galbraith(1973), and Huber (1991)

Increased flow and quantityof information can lead decision-makers to be able to improvethe performance of the firm itselfand has positive effects on theperformance of the supplychain.

Swink et al. (2007),Wong, Boon-itt, andWong (2011)

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imitate (Barney, 2012; Chen et al., 2009a,b). Beingable to manage these integrative relationships betterthan the firm’s competitors is a valuable internal stra-tegic resource. As such, we predict that SCI enablesmanagement to achieve a sustainable competitiveadvantage which can be viewed by improved firm per-formance (Barney, 2012). Therefore, the first hypothe-sis for this research is the following.

H1: Supply Chain Integration is positively relatedto firm performance.

Dimensions of SCI. After the evaluation of theoverall effect of SCI on firm performance, we have theopportunity to directly evaluate whether diverging con-struct measurement types alter the nature, or magni-tude, of the broader relationship. Researchers made thedistinction between different dimensions of integra-tion. Frohlich and Westbrook (2001, p. 187) explicitlyfocus on the operational aspect in their definition:“The development of shared operational activities withcustomers and/or suppliers.” Flynn et al. (2010, p. 59)define SCI as “the degree to which a manufacturer stra-tegically collaborates with its supply chain partnersand collaboratively manages intra- and inter-organiza-tion processes,” emphasizing the strategic nature ofSCI. Because of such divergent definitions, a morecomprehensive classification of constructs was neces-sary. This classification was developed based on a syn-thesis of the classifications shown in Table 2, with thegoal of succinctly classifying all retained articles.The linkage between integration efforts and firm per-

formance is a central tenant of this research. BecauseSCI requires investment, the objective of managementis to see a return on that investment. All articles inour sample tested this linkage. In line with ourtheoretical lenses, we view SCI as a resource, whichenables the firm to achieve a competitive advantageand thus leads to comparably better performance.After reviewing these diverging, though related, views

and analyzing the vast sample of empirical studies col-lected for the meta-analysis, three dimensions weredeveloped to compare and contrast the specific effectsof SCI on firm performance. This classification encom-passes a wide range of prior conceptualizations which isnecessary in a comprehensive summary of the literature.When management in two firms first engages in SCI,they share data and information (Kim & Lee, 2010; Lee,2000; Olorunniwo, & Li, 2011; Saeed, Malhotra, & Gro-ver, 2005). Thus, (1) information integration refers to thecoordination of information transfer, collaborativecommunication and supporting technology amongfirms in the supply chain. The next dimension in theprogression is when management integrates activities inaddition to the sharing of information (Ireland &Webb, 2007; Kim & Lee, 2010; Kulp, Lee, & Ofek, 2004;

Lee, 2000; Saeed et al., 2005; Van der Vaart & van Donk,2008): (2) Operational integration refers to the collabora-tive joint activity development, work processes andcoordinated decision making among firms in the supplychain. The last dimension builds on the previous twoand goes beyond activities focusing on attitudes (Ire-land & Webb, 2007; Lee, 2000; Saeed et al., 2005; Vander Vaart & van Donk, 2008): (3) Relational integrationrefers to the adoption of a strategic connection betweenfirms in the supply chain characterized by trust, com-mitment and long-term orientation (Chen, Paulraj, &Lado, 2004; Dyer & Hatch, 2006; Hult, Ketchen, & Sla-ter, 2004; Johnson, 1999).

H2a: Information Integration is positively relatedto firm performance.

H2b: Operational Integration is positively relatedto firm performance.

H2c: Relational Integration is positively related tofirm performance.

Dimensions of Firm Performance. As the focus ofthis research, the linkage between SCI and firm perfor-mance is evaluated in more detail. While most empiri-cal studies find a significant positive associationbetween SCI and firm performance, some also revealsignificant negative effects, and the magnitude of theassociation varies considerably. To better understandthis relationship, performance effects collected in themeta-analysis were summarized and evaluated acrossthree categories. Financial firm performance was mea-sured using either revenue minus cost-based measures,such as profitability and return on assets, or purelyrevenue-based measures, like sales and market share.Customer-oriented performance consists of measuresrelated to an improvement in customer satisfaction andcustomer loyalty, or closely related constructs. Finally,operational performance consists of improvements inkey competitive capabilities including cost, quality,delivery, flexibility and innovation (Hill, 1994; Ward,McCreery, Rizman, & Sharma, 1998). Analyses wereconducted both on aggregate firm performance andeach separate dimension. Several studies found a signif-icant relationship between SCI and firm performance.Thus, we hypothesize that SCI is positively correlatedwith different measures of firm performance.

H3a: Supply Chain Integration is positively relatedto business performance.

H3b: Supply Chain Integration is positively relatedto relational performance.

H3c: Supply Chain Integration is positively relatedto operational performance.

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TABLE2

Supply

Chain

IntegrationDim

ensions

Authors

SCITypes

(Dim

ensions)

Description

Lee(2000)

Inform

ationIntegration

Thesharingofinform

ationandkn

owledgeamongand

members

ofthesupply

chain

(demandinform

ationand

inve

ntory

status,

capacity

plans,

productionschedules,

andpromotionplans,

demandforecastsandshipment

schedules).

Coordinationand

reso

urcesharing

Refers

totheredeploym

entofdecisionrights,work

andreso

urcesto

thebest-positionedsupply

chain

member(e.g.,VMI,CRPprograms,

shared

warehousing,inve

ntory

poolin

g).

Organizationaland

relationship

linka

ge

Tightorganizationalrelationshipsbetw

eenco

mpanies

(integratedco

mmunicationch

annels,perform

ance

measuresandince

ntive

s).

IrelandandWebb(2007)

Strategic

Intentionofthefirm

swithin

thesupply

chain

tointegrate

theiractionsandinteractively

adjust

their

behaviors

while

pursuingopportunitiesove

rtime.

Includesboth

short-term

(e.g.,supplie

rschedulin

g,

visibility)andlong-term

goals

andefforts(e.g.,joint

flexibility,adaptation).

Operational

Product

andproce

ssintegrationacross

firm

swithin

strategic

supply

chains(e.g.,allo

wingsupplie

rsto

assumeresp

onsibility

forproduct

engineeringactivities

andproduct

deve

lopment;includingsupplie

rsto

understandtheco

mplexity

andscopeofco

ordinated

proce

sses).

Tech

nological

Sharingofkn

owledgeandcapabilitieswithin

the

strategic

supply

chain.

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Table

2(Continued).

Authors

SCITypes

(Dim

ensions)

Description

VanderVaart

and

andvanDonk(2008)

Practices

Tangible

activitiesortech

nologiesthatplayan

importantrole

intheco

llaborationofafocalfirm

withitssupplie

rsand/orcu

stomers

(e.g.,use

of

EDI,VMI,integratedproductionplanning,delivery

synch

ronization).

Attitudes

Measuresattitudeofbuye

rsandsupplie

rstowards

each

other(intangible),e.g.,long-term

orientation,

jointproblem

sharingandplanning,trust.

Patterns

Includeactivitieslikefrequentvisits,face

-to-face

,meetings/co

mmunication,form

alperiodic

evaluations

ofsupplie

rs/customers.

Kim

andLe

e(2010)

Strategic

Theextentto

whichsupply

chain

partners

actually

forecast

demandandplanbusiness

activitiesjointly

while

takinginto

accounteach

other’s

long-term

success.

Systems

Theextentto

whichsupply

chain

partners

strive

tomake

andke

eptheirco

mmunicationsystems

compatible

witheach

otherto

bereadyforinter-firm

forecastingandplanningin

additionto

routineelectronic

transactionsandinform

ationexchange

within

thesupply

chain.

Saeed,Malhotra,and

andGrove

r(2005)

Strategic

Theextentto

whichmembers

ofthesupply

chain

have

deve

lopedjointkn

owledgesharingroutinesthat

facilitate

use

ofinnovative

practices,

sharingof

new

ideas,

andworkingtogetherin

identifying

andim

plementingim

prove

mentinitiative

s.Operational

Theextentto

whichsupply

chain

members

linkdecisions

atdifferentstagesofthesupply

chain

byroutinely

coordinatingvariousoperationalproce

ssesandactivities

throughinform

ationsharing.

Financial

Theextentto

whichsupply

chain

members

jointly

inve

stin

projectsofmutualinterest.

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40

Moderator AnalysisOne of the advantages of a meta-analysis is that it

enables the researcher to examine theoretically rele-vant measurement characteristics that may explain thevariability in effect sizes (Hunter & Schmidt, 1990).These moderators enable examination of a moredetailed and specific view of SCI and of firm perfor-mance. The moderators were evaluated by construct-ing specific subgroups that can then be comparedagainst main effects to determine the impact of thatspecific moderator.In addition to the previously described hypotheses,

we examined four scopes of SCI: supplier integration,customer integration, external integration and internalintegration. These were mutually exclusive constructsthat appeared in the sample articles. While someresearchers focused on specific aspects, like customerand supplier integration (Cousins & Menguc, 2006;Homburg & Stock, 2004; Koufteros, Cheng, & Lai,2007), others used more expansive constructs toillustrate the scope of integration efforts (Frohlich &Westbrook, 2001; Thun, 2010). Supplier integration(Chen, Tian, Ellinger, & Daugherty, 2010a; Corsten &Felde, 2004; Flynn et al., 2010; Lee, Kwon, & Severance,2007) moves beyond just buying and selling activitiesand involves close relationships that involve suppliersin activities like product development and manufactur-ing support (Croxton, Garc�ıa-Dastugue, Lambert, &Rogers, 2001). Customer integration (Germain & Iyer,2006; Sanders, 2008) is the mirror image of supplierintegration and it depends on proactively determiningthe requirements of the customer and ensuring to meetthose requirements (Powell, 1995). External integrationis integration with customers and suppliers simulta-neously (Jayaram, Kannan, & Tan, 2004; Stank et al.,2001). While not a core SCI construct for this study,several articles included internal integration into theirresearch models, some see internal integration betweenthe four walls of the company as an implicit compo-nent of SCI (Flynn et al., 2010; Rosenzweig, Roth, &Dean, 2003), while others operationalize it as an ante-cedent or complement to external integration (Nara-simhan, Swink, & Viswanathan, 2010; Sanders, 2007).We refer to it as the integration between functions ordepartments within a single firm (Braunscheidel & Sur-esh, 2009; Closs & Savitskie, 2003; Koufteros, Rawski,& Rupak, 2010).There were also several types of firm performance

that were examined individually. Within business per-formance, we specifically examined financial perfor-mance and customer-oriented performance. Financialperformance is an important measure of firm perfor-mance and has been used in several studies withinour sample (Germain, Davis-Sramek, Lonial, & Raju,2011; Vickery, Jayaram, Droge, & Calantone, 2003).Customer-oriented performance is a more perception-

based measure that includes attitudes like satisfactionand loyalty (Johnston, McCutcheon, Stuart, & Ker-wood, 2004; Narasimhan, Jayaram, & Carter, 2001).Within operational performance, there were enoughstudies to evaluate the specific effects of cost, quality,delivery, innovation and flexibility. The importance ofevaluating these relationships is to enable us to gain adeeper understanding of where exactly the perfor-mance benefits from better SCI arise. The conceptualframework for this research is depicted in Figure 2.

METHODOLOGYIn this section, we first describe sample selection.

Next, the coding of the studies is explained. Then, wedetail the meta-analytic procedures that were used totest the hypotheses.

Sample SelectionTo test our research model, we gathered correlations

between relevant constructs and followed the randomcoefficient meta-analysis approach suggested by Hunterand Schmidt (2004). Relevant articles for the meta-analysis were identified via a literature search using theEBSCO Business Source Complete database includingappropriate keywords. An overview of the search termsand search results is shown in Table 3. The search wasrestricted to academic peer-reviewed journals, and itwas ensured that the search results were indeedresearch articles and not editorials or book reviews. Noother limitations were placed on the search. This pro-cedure yielded 552 articles that were further inspected.Individual items of the constructs were assessed toensure that the authors used measures of the constructsof interest that had face validity and that inter-con-struct zero-order correlations were obtainable. To beconsidered usable, the articles had to employ anempirical research methodology and include at leastone measure of SCI and one measure of firm perfor-mance, as shown in Table 3. Forty-eight articles wereretained. In addition to the keyword search, a “snow-balling” approach was also used to retrieve additionalstudies and it required us to inspect articles that werecited by our retained articles or that cited our retainedarticles. This procedure yielded another 38 usable arti-cles. In total, 86 articles using 80 independent samples(k) and representing 17,467 observations (N) were suc-cessfully identified, coded and used for further analy-sis. They are shown in Appendix A.

CodingA formal coding framework, based on the theoretical

framework and potential moderators, was establishedand employed independently by two of the authors.All articles were double-coded and any discrepancieswere resolved via discussion. The inspection of articles

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required a thorough assessment of the scale items toidentify several characteristics of the scale: (1) we deter-mined whether the scale was consistent with any ofour definitions of SCI; (2) we assessed whether theconstruct was consistent with any of the SCI dimen-sions in Figure 2; (3) we examined whether the con-struct was consistent with any of types of firmperformance; and (4) we identified whether there wereany moderators (Figure 2). To ensure that the items ofeach construct should reflect the respective subgroup,75% of the items should closely match our definitionfor that construct (Hunter & Schmidt, 2004). Each arti-cle was evaluated in the above manner, and weobtained correlations and reliabilities for all constructsof interest. The independent coding exposed fourteen

differences, yielding an inter–rater reliability of95.93 percent (14 differences/(86 studies * 4 codingsper study)). Multiple publications based on the samesample were treated as a single sample to maintain theassumption of independence among correlations(Hunter & Schmidt, 2004). In the case of multiple cor-relations for one relationship, the composite of thecorrelation coefficients was computed using aggrega-tion methods described in the meta-analytic proce-dures section. If zero-order inter-construct correlationsor reliabilities were not reported in the article, we solic-ited the required information via e-mail. If we werenot successful, the tracing rule was used to reproducethe correlations of interest (Kenny, 1979). In the casethat only item-level correlations were reported, a

TABLE 3

Search Terms and Results

Search Terms Results Empirical Usable Retained

“supply chain integration” 256 86 35 30“supply chain collaboration” 123 27 14 12“supplier integration” 64 8 2 1“customer integration” 58 14 3 2“supplier collaboration” 21 2 1 0“customer collaboration” 15 8 4 3Snowballing 38Total Articles 552 145 59 86

FIGURE 2Research Framework

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confirmatory factor analysis was used to derive theinter-construct correlations (Droge, Jayaram, & Vickery,2004). Each article was evaluated and the constructswere classified into a-priori categories (Figure 2), whichwere then used to test the hypotheses, by splitting thestudies into different sub-groups depending on theoperationalization of the constructs and the use of thepreviously mentioned moderators.

Meta-Analytic ProceduresThe widely employed meta-analytic procedures

described by Hunter and Schmidt (1990, 2004) werefollowed for the hypothesis testing via three stages:(1) the main effect testing; (2) the moderator exis-tence testing; and (3) the moderating effects testing.In the first stage, the overall relationship correlationbetween SCI and firm performance was assessed. Thecorrelation (r) was used to assess the relationshipsbetween the constructs of interest (see Geyskens,Krishnan, Steenkamp, & Cunha, 2009; and Shadish &Haddock, 1994 for discussion on different effectsizes). Corrections were applied for measurementerror (Hedges & Olkin, 1985; Hunter & Schmidt,2004; Rosenthal, 1991). If no scale reliability wasreported or if a single-item scale was used, the com-mon practice of substituting the mean reliability wasfollowed (Chen, Damanpour, & Reilly, 2010b; Crooket al., 2008; Mackelprang & Nair, 2010; Nair 2006).Each sample was weighed by its compound attenua-tion factor, which consists of the reliability of thescale and the sample size. Several articles had morethan one correlation of interest and those were com-bined into a composite (Arthur, Bennett, & Huffcutt,2001) following Hunter and Schmidt (1990, pp. 457–460). Publication bias, also referred to as the “filedrawer problem,” may occur because studies that pro-duce statistically nonsignificant findings are less likelyto be submitted to journals or be accepted for publi-cation (Rosenthal, 1979). Therefore, the “fail safenumber” was assessed for each group and sub-group,which indicates how many additional studies wouldhave to be found to obtain a nonsignificant result(Rosenberg, 2005).

RESULTSTo test our study’s hypotheses, the correlation

between our constructs of interest was evaluated. Theresults are shown in Tables 4 and 5. To determine thestrength and significance of the relationship, severalmeasures were calculated. For each relationship, thenumber of independent samples (k) and the overallsample size (N) are provided. The observed correla-tion (ro) and the corrected correlation (rc) were com-puted (Hunter & Schmidt, 2004). These measuresprovide a point estimate of the sample correlation

that is then assessed as to whether it is significantlydifferent from zero. The range of uncorrected correla-tions and the 90% credibility interval are alsoreported (Hunter & Schmidt, 2004, p. 83). The Q Sta-tistic is a measure of heterogeneity and a large signifi-cant value points to unexplained variance beingpresent in the sample or subsample (Hunter &Schmidt, 1990, p. 111). The fail safe numbers foreach subgroup range from 107 to 61,995, and thus,we conclude there is little risk of additional studieschanging the results we obtained.The relationships between SCI and firm performance

were evaluated, and the results provide evidence thatthe link is positive and significant (Table 4). To exam-ine the more specific types of firm performance, anoverall view of SCI is necessary, and thus, we exam-ined the aggregate effect of SCI on overall firm perfor-mance first. The other relationships can then becompared subsequently. The corrected correlationbetween SCI and firm performance was 0.36, which issignificant at the 0.05 level, and thus, we concludethere is support for H1. In addition to the maineffects testing, several moderators, based on the opera-tionalization of the SCI construct, were tested and areshown in Table 3. Three types of integration wereevaluated specifically: information (H2a), operational(H2b) and relational (H2c). While information inte-gration and relational integration show significantcorrelation with firm performance, operational inte-gration does not have a significant correlation withfirm performance. Drawing on the RBV, it can beargued that these types of integration are moredifficult to imitate and as such can lead to better firmperformance. The nominal correlation does not differwidely from the others, but the variance and the cred-ibility interval explain why it cannot be concludedthat it is significantly different from zero. Fouradditional scopes of SCI were evaluated: supplier,customer, external and internal integration. There isweak support for a significant correlation betweensupplier integration because the corrected correlationhas a larger standard deviation that prevents us fromconcluding it is larger than zero. There is no supportto conclude that a significant correlation between cus-tomer integration and firm performance exists in thissample. External and internal integration show a posi-tive and significant correlation with firm performance.In addition to the different operationalizations of

SCI, we also evaluated whether the correlationbetween SCI and firm performance is impacted bymeasurement differences in the dependent variables.The results are shown in Table 5. We found weak sup-port for H3a, but strong support for H3b and H3c.While business performance, which is conceptualizedas the top line benefits of SCI, has weak support,additional subgroups were evaluated, such as financial

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performance and customer-oriented performance. Thecorrelation between SCI and financial performancewas not significant. The evaluation of the linkbetween SCI and customer-oriented performance washighly significant and did not show any heterogeneity,so that we can conclude that there is no evidence forsignificant moderators being present in the sample.This was the only subgroup where it was possible toresolve all the heterogeneity. Additional subgroups ofoperational performance were evaluated: cost, quality,delivery, innovation and flexibility. The relationshipsbetween SCI and quality, delivery, and innovationwere significant. No significant relationship betweenSCI and cost and SCI and flexibility could be found.The implications of these results are described in thenext section in addition to limitations and suggestionsfor future research.

CONCLUSIONSIn this study, we accumulated and integrated the

results of empirical research on the relationshipbetween SCI and firm performance that may lead togeneralizable evidence for advancement of theory andpractice on SCI. Inconsistencies in original research

results may be due to artifacts such as sample sizesand measurement errors in the original studies. Sub-group analysis of moderators showed that the major-ity of samples have a significant relationship betweendifferent operationalizations of SCI and operational-izations of firm performance along theoretical expecta-tions. A necessary implication of this meta-analysis forfuture research is that when results are contradictingor nonsignificant, it may be due to the study’s hetero-geneous factors, for example, the industry, the type ofcompanies that were considered, or even the time per-iod in which the research was conducted. In that case,a detailed assessment of significant differences in cor-relation coefficients for various subgroups may explaindeviating results.

Implications for TheoryIn initiating this study, we encountered an expansive

literature base that appeared to use an array of per-spectives and different theories in investigating SCI.We examine SCI under the lens of the RBV of the firmand the extensions of the RBV that were R-A theoryand the RV. Our primary objective for this meta-analysis was to investigate whether SCI as a firmresource is related to better firm overall performance.

TABLE 4

Results for Specific Supply Chain Integration Subgroups

Relationship(Impact on FirmPerformance) k N ro rc Range

CredibilityInterval Q Fail Safe

1. H1: SupplyChainIntegration

80 17,248 0.32* 0.36* �0.18 0.84 0.06 0.66 526.02** 61994.80

2. H2a:InformationIntegration

33 6,723 0.33* 0.38* 0.09 0.78 0.07 0.69 201.00** 17995.80

3. H2b:OperationalIntegration

33 6,700 0.30 0.34 0.09 0.90 �0.01 0.70 260.78** 5236.25

4. H2c:RelationalIntegration

14 2,651 0.36* 0.41* 0.15 0.79 0.06 0.75 103.69** 3282.30

5. SupplierIntegration

48 10,601 0.29* 0.33 �0.05 0.79 0.03 0.63 312.92** 3600.88

6. CustomerIntegration

31 7,003 0.25 0.29 �0.18 0.73 �0.01 0.59 207.22** 10086.52

7. ExternalIntegration

15 3,949 0.35* 0.42* 0.16 0.69 0.10 0.73 121.14** 8159.23

8. InternalIntegration

22 4,627 0.30* 0.34* 0.09 0.64 0.07 0.60 111.69** 1235.56

*p-value < 0.05; **p-value < 0.01.

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This can help determine whether SCI should beviewed as a source of competitive advantage. In thisarticle, we have presented a theoretical framework toaid in providing parsimony and to distinguishbetween three dimensions of SCI (information, opera-tional and relational). Our classification of SCI can beused to interpret past research on SCI and clarifies theconcept for future research. Setting a baseline via thismeta-analysis synthesizes existing knowledge and aidsscholars in scoping new research.The overall positive and significant relationship

between SCI and firm performance (H1) is a signifi-cant, but not surprising result (Christopher, 2005).Closer integration is significantly correlated with bet-ter firm performance. This finding fits with the theo-retical bases that we highlighted in this research.While this result does not specifically point to causal-ity, it should be expected that firms engaging in inte-gration efforts should experience higher firmperformance as a result. It is also apparent that thisgeneral relationship is characterized by considerableheterogeneity (Q = 526.02**), which was thenreduced in subsequent subgroups.Prior research provides support for the notion that

firms have the opportunity to leverage integrationmechanisms with customers and suppliers to achieve

organizational performance benefits (Cooper, Lambert& Pagh, 1997; Lambert, Cooper, & Pagh, 1998; Ste-vens, 1989; Lee, 2000). At the same time, researchershave noted that firms working toward higher levels ofSCI are likely to face a number of challenges (Fabbe-Costes & Jahre, 2007; Fawcett & Magnan, 2002). Inthe next step, three separate operationalizations of SCIwere analyzed. Information integration is significantlycorrelated with firm performance (H2a). This resultcan be explained by arguing that sharing of informa-tion will enable both companies to operate moreefficiently (Zhou & Benton, 2007). It can also beexplained with IPT, where better information for theright parts of a firm can lead to a competitive advan-tage. In addition, internal integration also has a signif-icant correlation with firm performance. These twotypes of integration are generally considered as requir-ing the least involvement and effort.For the relationship between operational integra-

tion and firm performance, we did not find a signifi-cant correlation (H2b). A higher level of integrationlikely causes temporarily higher costs, and it is possi-ble that the resulting increase in performance is notlarge enough to recoup those higher costs. Firmsthat were surveyed in the original studies may alsonot have been able to yet recognize the positive

TABLE 5

Results for Specific Firm Performance Subgroups

Relationship(Impact ofSupply ChainIntegration) k N ro rc Range

CredibilityInterval Q Fail Safe

9. H3a: BusinessPerformance

33 7,768 0.29* 0.33 0.06 0.72 0.03 0.63 226.66** 29399.73

10. FinancialPerformance

18 4,498 0.24 0.27 0.06 0.69 0.02 0.52 101.97** 1794.81

11. Customer-orientedPerformance

5 832 0.31** 0.37** 0.25 0.37 0.26 0.48 1.56 106.97

12. H3b:RelationalPerformance

6 1,378 0.64** 0.72** 0.37 0.86 0.44 1.01 33.70** 2825.34

13. H3c:OperationalPerformance

60 12,072 0.31* 0.35* �0.18 0.84 0.09 0.60 274.38** 8870.43

14. Cost 24 4,070 0.17 0.21 �0.18 0.58 �0.07 0.48 98.74** 967.5815. Quality 11 2,483 0.23* 0.26 0.10 0.53 0.04 0.48 42.06** 365.1916. Delivery 22 4,671 0.25* 0.30* 0.10 0.42 0.14 0.46 50.55** 2623.7017. Innovation 11 2,186 0.22* 0.26* 0.09 0.43 0.11 0.41 22.60* 655.5818. Flexibility 16 3,274 0.19 0.22 �0.09 0.44 0.04 0.40 42.33** 353.14

*p-value < 0.05; **p-value < 0.01.

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results of operational integration, as these benefitsmay take longer to realize. It is not possible toobtain a firm-specific perspective with our research,but we find it likely that the risk of failure at thisstage of SCI is significant and therefore the resultswe obtain have such a large variance. This interpreta-tion is additionally supported by the two modera-tors, supplier integration and customer integration,having a nonsignificant correlation with firm perfor-mance. This is not to say that an improvement infirm performance does not occur, but our resultsseem to indicate that the variance, and therefore theassociated risk, is higher regarding the return oninvestment.Relational integration, which mainly draws on the

RV as its theoretical base (Dyer & Singh, 1998), doeshave a significant correlation with firm performance(H2c). It is understood that only firms that have along-term relationship will be able to attain such alevel of integration. Therefore, it is not surprising thatthe payoff from closer integration is higher and thereis less risk involved with that type of integration. It isalso reasonable to assume that firms that are seekingthis level of integration do have more experience withintegration efforts and the chance for success isincreased even more. As such we believe that it shouldnot be uniformly assumed that tighter integration isbetter in all situations. It comes with a higher level ofmanagement effort, and there must be a business rea-son for investing resources into the relationship. Thisview is supported by Lambert, Emmelhainz, andGardner (1996) who present a model that describeshow firm relationships should be structured at theappropriate level. Both companies should take intoaccount the drivers, facilitators and managementresources available for managing the relationship.The evaluation of different measures of firm perfor-

mance revealed several noteworthy findings. Theimpact of SCI on business performance, which is gen-erally associated with revenue generation and profit-ability, is not significant (H3a). The weak link torevenue generation and the bottom line is not surpris-ing because most of the benefits from SCI are expectedto be in the form of cost savings (Madhok, & Tallman,1998). Such efficiencies often do not have the profit-ability impact to warrant a significant change in thebottom line as the savings do not have the same profitleverage as revenue increases (Marn & Rosiello, 1992).More specifically, financial performance also had anonsignificant effect, which is additional evidence. Theimpact of SCI on customer-oriented performance,which specifically is related to relational outcomessuch as satisfaction, trust and commitment, has a highand significant correlation. This result points to thefact that closer integration with customers and to a les-ser extent with suppliers can have intangible benefits

that can improve the relationship. These benefits maynot be immediately measurable in financial or busi-ness terms, but marketing researchers argue that laggedfinancial benefits occur as a result of customer-orientedperformance (Guo, Kumar, & Jiraporn, 2004).A few studies used relational performance as an out-

come measure, and the correlation was the highest weobtained in this study (H3b). Most of the studies(four of six) used relational SCI as the antecedent.While this result is impressive, we must also cautionthat we cannot exclude cognitive bias as one of theexplanations for this result. It would be more impres-sive if such high correlations would be obtained withfinancially based performance measures. We generallyadvise researchers against the use of fully perceptualmeasures because this often overlooked concern ofcognitive bias can inflate the relationship.Most studies (k = 60) in our sample used opera-

tional performance as one of the outcome variablesand a positive and significant aggregate correlationwas found (H3c). While this aggregate view of perfor-mance is important, the specific subgroups of opera-tional performance should be evaluated further. Withthree of five subgroups not having significant correla-tions, several conclusions can be drawn. Costimprovements are a not significant outcome of SCI,and this result, while surprising to some, highlightsthe fact that substantial resource commitment is nec-essary when undertaking integrative activities betweencustomers and suppliers. The impact of SCI on qualitywas also not significant, and we attribute this to qual-ity being an imprecise measure that can be interpretedin several ways. As such, it may not be possible toaccurately trace the improvements to quality gains. Inaddition, quality is always a moving target, and theoverall customer perception of quality will be influ-enced by the competitors in the marketplace. The cor-relation between SCI and delivery performance waspositive and significant. One of the main areas wherefirms integrate is how products are delivered, thus it isnot surprising that delivery performance is signifi-cantly affected by SCI. Another significant effect is thelink between SCI and innovation. We draw on SET toexplain that managers from customers and suppliersworking together might create opportunities for inno-vation, purely based on the fact that they are interact-ing with each other. Therefore, SCI has the effect thatpeople from different companies can likely encounteropportunities for improvement in their daily interac-tions as part of a larger integration initiative. The lastsubgroup that was evaluated was the impact of SCIon flexibility, and we attribute this result to the factthat different companies may have different con-straints (Christopher & Holweg, 2011). While the con-straints become clearer through integration, it maynot be possible to overcome them.

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Implications for ManagersA meta-analysis, such as the one described in this arti-

cle, can help managers understand the level and signifi-cance of the relationship between SCI and firmperformance. More specifically, this study can helpanswer questions such as what type of SCI has thechance to lead to the highest benefits in terms of firmperformance. There is evidence that SCI leads to higherfirm performance, in general; however, more impor-tantly managers should understand what will be mostbeneficial to their organization. On the basis of ourresults, all levels of integration can be beneficial forfirm performance; however, operational integrationcan have varying results. The operational benefits ofSCI are only found in the areas of delivery performanceand innovation. This result provides support to thenotion that managers should not expect quick payoffsfrom their integration initiatives, like cost savings andquality improvement, but it is more likely that longerterm, more durable performance gains can beobtained.

Limitations of the ResearchAs with any research, there are several limitations

that must be pointed out. By definition, a meta-analy-sis relies on available studies. While we performed athorough literature search and “snowballing” to iden-tify all suitable articles, there still is the possibility thatsome studies were missed. However, due to the num-ber of samples that we were able to obtain (80 inde-pendent samples) and the high fail-safe numbers(Tables 4 and 5), we are confident that any additionalstudies would be unlikely to change the results. Whileevery effort was made to obtain all the informationnecessary for each suitable study, we did encountersome difficulties in retrieving correlations and reliabil-ities for some studies. If that information was notavailable from the authors and we could not imputeit otherwise, we had to drop those studies from thismeta-analysis. We were able to obtain a significantlylarger number of samples than some other recentlypublished articles using this methodology (Mackelp-rang & Nair, 2010; Nair, 2006). However, due torestrictions in the sample, we were not able to exam-ine more sample-specific moderators.

Suggestions for Future ResearchIt is of critical importance for our discipline to

assess a phenomenon over an extended period oftime. Once there are sufficient empirical studies, ameta-analysis can be performed to aggregate theresults. This methodology should be employed tostudy other phenomena in the supply chain manage-ment domain. It would be interesting to see how SCIis viewed over an extended period of time. While such

longitudinal studies are difficult to operationalize,they would add significantly to our understanding ofthis important phenomenon.Due to the small sample size of the SCI customer-ori-

ented subgroup, it is clear that additional studies arenecessary in this area. The nonsignificant correlationsbetween SCI and cost performance, and SCI and qual-ity performance might be be due to the focus of opera-tional-level management primarily on delivery issues.As such, we suspect that in the future we may find dif-ferent results and as such it is worth investigating thiseffect more.At this point, we would like to encourage authors,

referees and editors to agree to a consistent standard ofreporting for empirical survey-based research, whichcan only improve methodological rigor. At a mini-mum, the correlations between the latent variables andthe reliabilities of the constructs should be reported. Inaddition, detailed information on how the scales weredeveloped should be required to trace the origin of ascale and to enable other researchers in the field toassess the quality of the constructs. Such a standardwould not just make it easier to aggregate studies in ameta-analysis, but also enable readers to evaluate stud-ies more quickly and objectively. As we have seen insome of the studies we evaluated, some constructs thatwere based on previously developed measures weremodified without explanation. Therefore, we call onour colleagues to adhere to this standard in order toincrease the methodological rigor of our field.Supply chain integration has been a highly

researched topic in the past 20 years. In this meta-analysis, we examined this significant body of litera-ture to quantitatively summarize the results. The mainbenefit of our analysis is that we were able to estimatethe overall population effect of SCI on firm perfor-mance and within the relevant subgroups. The posi-tive association reinforces the importance of thisconstruct, but the significant amount of heterogeneityin most subgroups is evidence that additional researchis necessary before we can make generalizable state-ments. As such we call for more research on the rela-tionship between SCI and firm performance.

REFERENCESArgote, L. (1999). Organizational learning: Creating,

retaining and transferring knowledge. Norwell, MA:Kluwer Academic Publishers.

Arthur, W., Bennett, W., & Huffcutt, A. I. (2001).Conducting meta-analysis using SAS. Mahwah, NJ:Lawrence Erlbaum.

Bagchi, P. K., & Skjoett-Larsen, T. (2005). Supplychain Integration: A European Survey. The Interna-tional Journal of Logistics Management, 16 (2), 275–294.

April 2013


47

Barney, J. B. (1991). Firm resources and sustainedcompetitive advantage. Journal of Management, 17(1), 99–120.

Barney, J. B. (2012). Purchasing, supply chain man-agement and sustained competitive advantage:The relevance of resource-based theory. Journal ofSupply Chain Management, 48 (2), 3–6.

Benton, W. C., & Maloni, M. (2005). The influence ofpower driven buyer/seller relationships on supplychain satisfaction. Journal of Operations Manage-ment, 23, 1–22.

Blau, P. (1964). Exchange and power in social life. NewYork: Wiley.

Boon-Itt, S., & Paul, H. (2006). A study of supplychain integration in thai automotive industry: Atheoretical framework and measurement. Manage-ment Research News, 29 (4), 194–205.

Braunscheidel, M. J., & Suresh, N. C. (2009). Theorganizational antecedents of a firm’s supplychain agility for risk mitigation and response.Journal of Operations Management, 27, 119–140.

Braunscheidel, M. J., Suresh, N. C., & Boisnier, A. D.(2010). Investigating the impact of organizationalculture on supply chain integration. HumanResource Management, 49 (5), 883–911.

Cao, M., Vonderembse, M. A., Zhang, Q., & Ragu-Nathan, T. S. (2010). Supply chain collaboration:Conceptualisation and instrument development.International Journal of Production Research, 48(22), 6613–6635.

Carr, A. S., & Kaynak, H. (2007). Communicationmethods, information sharing, supplier develop-ment and performance: An empirical study oftheir relationships. International Journal of Opera-tions and Production Management, 27 (4), 346–370.

Carr, A. S., Kaynak, H., & Muthusamy, S. (2008). Thecross�functionaI coordination between opera-tions, marketing, purchasing and engineering andthe impact on performance. International Journalof Manufacturing Technology and Management, 13(1), 55–77.

Carr, A. S., & Pearson, J. N. (1999). Strategically man-aged buyer–supplier relationships and perfor-mance outcomes. Journal of OperationsManagement, 17, 497–519.

Charvet, F. F. (2008). Supply Chain Collaboration: TheRole of Key Contact Employees. Unpublished disser-tation. Columbus, OH: The Ohio State University.

Chen, J., Damanpour, F., & Reilly, R. R. (2010b).Understanding antecedents of new product devel-opment speed: A meta-analysis. Journal of Opera-tions Management, 28 (1), 17–33.

Chen, H., Daugherty, P. J., & Landry, T. D. (2009b). Sup-ply chain process integration: A theoretical frame-work. Journal of Business Logistics, 30 (2), 27–46.

Chen, H., Daugherty, P. J., & Roath, A. S. (2009a).Defining and operationalizing supply chain pro-cess integration. Journal of Business Logistics, 30(1), 63–84.

Chen, I. J., Paulraj, A., & Lado, A. A. (2004). Strategicpurchasing, supply management, and firm perfor-

mance. Journal of Operations Management, 22, 505–523.

Chen, H., Tian, Y., Ellinger, A. E., & Daugherty, P. J.(2010a). Managing logistics outsourcing relation-ships: An empirical investigation in china. Journalof Business Logistics, 31 (2), 279–299.

Christopher, M. (2005). Logistics and supply chain man-agement: Creating value-added networks. Upper Sad-dle River, NJ: Prentice Hall.

Christopher, M., & Holweg, M. (2011). Supply chain2.0: Managing supply chains in the era of turbu-lence. The International Journal of Physical Distribu-tion and Logistics Management, 41 (1), 63–82.

Closs, D. J., & Savitskie, K. (2003). Internal and exter-nal logistics information technology integration.The International Journal of Logistics Management,14 (1), 63–76.

Coase, R. H. (1937). The nature of the firm. Economi-ca, 4 (16), 386–405.

Cooper, M. C., Lambert, D. M., & Pagh, J. (1997).Supply chain management: More than a newname for logistics. The International Journal ofLogistics Management, 8 (1), 1–14.

Corsten, D., & Felde, J. (2004). Exploring the perfor-mance effects of key-supplier collaboration: Anempirical investigation into Swiss buyer-supplierrelationships. The The International Journal of Physi-cal Distribution and Logistics Management, 35 (6),445–461.

Cousins, P. D., Handfield, R. B., Lawson, B., & Peter-sen, K. J. (2006). Creating supply chain relationalcapital: The impact of formal and informal social-ization processes. Journal of Operations Manage-ment, 24, 851–863.

Cousins, P. D., & Lawson, B. (2007). The effect ofsocialization mechanisms and performance mea-surement on supplier integration in new productdevelopment. British Journal of Management, 18,311–326.

Cousins, P. D., & Menguc, B. (2006). The implica-tions of socialization and integration in supplychain management. Journal of Operations Manage-ment, 24 (5), 604–620.

Crook, T. R., Ketchen, D. J., Combs, J. G., & Todd, S.Y. (2008). Strategic resources and performance: Ameta-analysis. Strategic Management Journal, 29,1141–1154.

Croxton, K. L., Garc�ıa-Dastugue, S. J., Lambert, D. M.,& Rogers, D. S. (2001). The supply chain manage-ment processes. The International Journal of Logis-tics Management, 12 (2), 13–36.

Dabhilkar, M., Bengtsson, L., von Haartman, R., &Ahlstro, P. (2009). Supplier selection or collabora-tion? Determining factors of performance improve-ment when outsourcing manufacturing. Journal ofPurchasing and Supply Management, 15, 143–153.

Deveraj, S., Krajewski, L., & Wei, J. C. (2007). Impactof eBusiness technologies on operational perfor-mance: The role of production information inte-gration in the supply chain. Journal of OperationsManagement, 25, 1199–1216.

Volume 49, Number 2


48

Dong, Y., Carter, C. R., & Dresner, M. E. (2001). JITpurchasing and performance: An exploratory anal-ysis of buyer and supplier perspectives. Journal ofOperations Management, 19, 471–483.

Droge, C., Jayaram, J., & Vickery, S. K. (2004). Theeffects of internal versus external integration prac-tices on time-based performance and overall firmperformance. Journal of Operations Management, 22(6), 557–573.

Dwyer, F. R., Schur, P. H., & Oh, S. (1987). Develop-ing buyer–seller relationships. Journal of Marketing,51 (2), 11–27.

Dyer, J. H., & Hatch, N. (2006). Relation-specificcapabilities and barriers to knowledge transfers:Creating advantage through network relationships.Strategic Management Journal, 27, 701–719.

Dyer, J. H., & Singh, H. (1998). The relational view:Cooperative strategy and sources of interorganiza-tional competitive advantage. Academy of Manage-ment Review, 23 (4), 660–679.

Eltantawy, R. A., Giunipero, L., & Fox, G. L. (2009). Astrategic skill based model of supplier integrationand its effect on supply management perfor-mance. Industrial Marketing Management, 38, 925–936.

Emerson, R. (1962). Power–dependence relations.American Sociological Review, 27 (1), 31–41.

Fabbe-Costes, N., & Jahre, M. (2007). Supply chainintegration improves performance: The Emperor’snew suit? The International Journal of Physical Dis-tribution and Logistics Management, 37 (10), 835–855.

Fabbe-Costes, N., & Jahre, M. (2008). Supply chainintegration and performance: A review of the evi-dence. The International Journal of Logistics Manage-ment, 19 (2), 130–154.

Fawcett, S. E., & Magnan, G. M. (2002). The rhetoricand reality of supply chain integration. The Inter-national Journal of Physical Distribution and LogisticsManagement, 32 (5), 339–361.

Flynn, B. B., Huo, B., & Zhao, X. (2010). The impactof supply chain integration on performance: Acontingency and configuration approach. Journalof Operations Management, 28 (1), 58–71.

Forza, C. (1996). Achieving superior operating perfor-mance from integrated pipeline management: Anempirical study. The International Journal of Physi-cal Distribution and Logistics Management, 26 (9),36–63.

Frohlich, M. T., & Westbrook, R. (2001). Arcs of inte-gration: An international study of supply chainstrategies. Journal of Operations Management, 19(2), 185–200.

Fynes, B., de Burca, S., & Voss, C. (2005). Supplychain relationship quality, the competitive envi-ronment and performance. International Journal ofProduction Research, 43 (16), 3303–3320.

Galbraith, J. R. (1973). Designing complex organizations.Addison-Wesley.

Germain, R., Davis-Sramek, B., Lonial, S. C., & Raju,P. S. (2011). The impact of relational supplier

exchange on financial performance: A study of thehospital sector. Journal of Business Logistics, 32 (3),240–253.

Germain, R., & Iyer, K. N. (2006). The interaction ofinternal and downstream integration and its asso-ciation with performance. Journal of Business Logis-tics, 27 (2), 29–52.

Geyskens, I., Krishnan, R., Steenkamp, J. E. M., &Cunha, P. V. (2009). A review and evaluation ofmeta-analysis practices in management research.Journal of Management, 35 (2), 393–419.

Gimenez, C., & Ventura, E. (2005). Logistics-produc-tion, logistics-marketing and external integration:Their impact on performance. International Journalof Operations and Production Management, 25 (1),20–38.

Golicic, S. L., & Mentzer, J. T. (2005). Exploring thedrivers of interorganizational relationship magni-tude. Journal of Business Logistics, 26 (2), 47–71.

Grant, R. M. (1996). Toward a knowledge-based the-ory of the firm. Strategic Management Journal, 17,109–122.

Griffith, D. A., Harvey, M. G., & Lusch, R. F. (2006).Social exchange in supply chain relationships: Theresulting benefits of procedural and distributive jus-tice. Journal of Operations Management, 24, 85–98.

Gulati, R., & Sytch, M. (2007). Dependence asymme-try and joint dependence in interorganizationalrelationships: Effects of embeddedness on a man-ufacturer’s performance in procurement relation-ships. Administrative Science Quarterly, 52, 32–69.

Guo, C., Kumar, A., & Jiraporn, P. (2004). Customersatisfaction and profitability: Is there a laggedeffect? Journal of Strategic Marketing, 12 (3), 129–144.

Ha, B., Park, Y., & Cho, S. (2011). Suppliers’ affectivetrust and trust in competency in buyers: Its effecton collaboration and logistics efficiency. Interna-tional Journal of Operations and Production Manage-ment, 31 (1), 56–77.

Handfield, R. B., Petersen, K., Cousins, P., & Lawson,B. (2009). An organizational entrepreneurshipmodel of supply management integration andperformance outcomes. International Journal ofOperations and Production Management, 29 (2),100–126.

Hanushek, E. A. (1989). The impact of differentialexpenditures on school performance. EducationalResearcher, 18 (4), 45–62.

Hanushek, E. A. (1994). Money might matter some-where: A response to hedges, laine, and green-wald. Educational Researcher, 23 (4), 5–8.

Hedges, L. V., Laine, R. D., & Greenwald, R. (1994a).Does money matter? A meta-analysis of studies ofthe effects of differential school inputs on studentoutcomes. Educational Researcher, 23 (3), 5–14.

Hedges, L. V., Laine, R. D., & Greenwald, R. (1994b).Money does matter somewhere: A response toHanushek. Educational Researcher, 23 (4), 9–10.

Hedges, L. V., & Olkin, I. (1985). Statistical methods formeta-analysis. Orlando, FL: Academic Press, Inc.

April 2013


49

Hill, T. (1994). Manufacturing strategy – Text and cases.Irwin, Homewood, IL: Richard D.

Homburg, C., & Stock, R. M. (2004). The linkbetween salespeople’s job satisfaction and cus-tomer satisfaction in a business-to-business con-text: A dyadic analysis. Journal of Academy ofMarketing Science, 32 (2), 144–158.

Huber, G. P. (1991). Organizational learning: Thecontributing processes and the literatures. Organi-zation Science, 2, 88–115.

Hult, G. T., Ketchen, D. J., & Slater, S. F. (2004).Information processing, knowledge development,and strategic supply chain performance. Academyof Management Journal, 47 (2), 241–253.

Hunt, S. D., & Davis, D. F. (2008). Grounding supplychain management in resource-advantage theory.Journal of Supply Chain Management, 44 (1), 10–21.

Hunt, S. D., & Davis, D. F. (2012). Grounding supplychain management in resource-advantage theory:In defense of a resource-based view of the firm.Journal of Supply Chain Management, 48 (2), 14–20.

Hunter, J. E. (2001). Re-inquiries: The desperate needfor replications. Journal of Consumer Research, 28(2), 149–158.

Hunter, J. E., & Schmidt, F. L. (1990). Methods formeta-analysis. Thousand Oaks, CA: Sage.

Hunter, J. E., & Schmidt, F. L. (2004). Methods formeta-analysis: Correcting error and bias in researchfindings. Thousand Oaks, CA: Sage.

Ireland, R. D., & Webb, J. W. (2007). A multi-theo-retic perspective on trust and power in strategicsupply chains. Journal of Operations Management,25 (2), 482–497.

Iyer, K. N., Germain, R., & Claycomb, V. A. (2009).B2B e-commerce supply chain integration andperformance: A contingency fit perspective on therole of environment. Information and Management,46 (6), 313–322.

Jayaram, J., Kannan, V. R., & Tan, K. C. (2004). Influ-ence of initiators on supply chain value creation.Journal of Production Research, 42 (20), 4377–4399.

Jayaram, J., & Tan, K. C. (2010). Supply chain integra-tion with third-party logistics providers. Interna-tional Journal of Production Economics, 125, 262–271.

Johnson, J. L. (1999). Strategic integration in indus-trial distribution channels: Managing the interfirmrelationship as a strategic asset. Journal of the Acad-emy of Marketing Science, 27 (1), 4–18.

Johnston, D. A., McCutcheon, D. A., Stuart, F. I., &Kerwood, H. (2004). Effects of supplier trust onperformance of cooperative supplier relationships.Journal of Operations Management, 22, 23–38.

Kenny, D. A. (1979). Correlation and causation. NewYork: John Wiley.

Kim, D., & Cavusgil, E. (2009). The impact of supplychain integration on brand equity. Journal of Busi-ness and Industrial Marketing, 24 (7), 496–505.

Kim, D., & Lee, R. P. (2010). Systems collaborationand strategic collaboration: Their impacts on sup-

ply chain responsiveness and market performance.Decision Sciences, 41 (4), 955–981.

Kogut, B., & Zander, U. (1992). Knowledge of thefirm, combinative capabilities, and the replicationof technology. Organization Science, 3, 383–397.

Koufteros, X., Cheng, T. C. E., & Lai, K. H. (2007).Black-box and gray box supplier integration inproduct development: Antecedents, consequencesand the moderating role of firm size. Journal ofOperations Management, 25 (4), 847–870.

Koufteros, X., Rawski, G. E., & Rupak, R. (2010).Organizational integration for product develop-ment: The effects on glitches, on-time executionof engineering change orders, and market success.Decision Sciences, 41 (1), 49–80.

Koufteros, X., Vonderembse, M., & Jayaram, J. (2005).Internal and external integration for productdevelopment: The contingency effects of uncer-tainty, equivocality, and platform strategy. Deci-sion Sciences, 36 (1), 97–133.

Krause, D. R., Handfield, R. B., & Tyler, B. B. (2007).The relationships between supplier development,commitment, social capital accumulation and per-formance improvement. Journal of Operations Man-agement, 25, 528–545.

Kulp, S. C., Lee, H. L., & Ofek, E. (2004). Manufac-turer benefits from information integration withretail customers. Management Science, 50 (4), 431–444.

Lambert, D. M., & Cooper, M. C. (2000). Issues insupply chain management. Industrial MarketingManagement, 29 (1), 65–83.

Lambert, D. M., Cooper, M. C., & Pagh, J. D. (1998).Supply chain management: Implementation issuesand research opportunities. The International Jour-nal of Logistics Management, 9 (2), 1–19.

Lambert, D. M., Emmelhainz, M. A., & Gardner, J. T.(1996). Developing and implementing supplychain partnerships. The International Journal ofLogistics Management, 7 (2), 1–18.

Lau, A. K., Tang, E. P., & Yam, R. C. (2010). Effects ofsupplier and customer integration on productinnovation and performance: Empirical evidencein Hong Kong manufacturers. Journal of ProductInnovation Management, 27, 761–777.

Lau, A. K., Yam, R. C., & Tang, E. P. (2007). Supplychain product co-development, product modular-ity and product performance: Empirical evidencefrom Hong Kong manufacturers. Industrial Man-agement and Data Systems, 107 (7), 1036–1065.

Lavie, D. (2006). The competitive advantage of inter-connected firms: An extension of the resource-based view. Academy of Management Review, 31(3), 638–658.

Lawrence, P. R., & Lorsch, J. W. (1967). Differentia-tion and integration in complex organizations.Organizational Science, 12 (1), 1–47.

Lawson, B., Tyler, B. B., & Cousins, P. D. (2008).Antecedents and consequences of social capital onbuyer performance improvement. Journal of Opera-tions Management, 26, 446–460.

Volume 49, Number 2


50

Lee, H. L. (2000). Creating value through supplychain integration. Supply Chain ManagementReview, 4 (4), 30–36.

Lee, G. J. (2010). Employee flow as an integrated andqualitative system: Impact on business-to-businessservice quality. Journal of Business-to-Business Mar-keting, 17, 1–28.

Lee, C. H., Huang, S. Y., Barnes, F. B., & Kao, L.(2010). Business performance and customer rela-tionship management: The effect of IT, organisa-tional contingency and business process onTaiwanese manufacturers. Total Quality Manage-ment, 21 (1), 43–65.

Lee, C. W., Kwon, I. W., & Severance, D. (2007). Rela-tionship between supply chain performance anddegree of linkage among supplier, internal inte-gration, and customer. Supply Chain Management:An International Journal, 12 (6), 444–452.

Leone, R. P., & Schulz, R. L. (1980). A study of market-ing generalizations. Journal of Marketing, 44, 10–18.

Li, G., Yang, H., Sun, L., & Sohal, A. S. (2009). Theimpact of IT implementation on supply chainintegration and performance. International Journalof Production Economics, 120, 125–138.

Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.

Lorenzoni, G., & Lipparini, A. (1999). The leveragingof interfirm relationships as a distinctive organiza-tional capability: A longitudinal study. StrategicManagement Journal, 20, 317–338.

Mackelprang, A. W., & Nair, A. (2010). Relationshipbetween just-in-time manufacturing practices andperformance: A meta-analytic investigation. Jour-nal of Operations Management, 28, 283–302.

Mackelprang, A. W., Robinson, J. L., & Webb, G. S.(2012). Supply Chain Integration: A Meta-Analy-sis and Future Directions. Presented at the CSCMPSupply Chain Management Educators’ Conference,Atlanta, GA.

MacNeil, I. R. (1980). Power, contract, and the eco-nomic model. Journal of Economic Issues, 14 (4),909–923.

Madhok, A., & Tallman, S. B. (1998). Resources,transactions and rents: Managing value throughinterfirm collaborative relationships. OrganizationScience, 9 (3), 326–339.

Maloni, M., & Benton, W. C. (2000). Power influencesin the supply chain. Journal of Business Logistics,21 (1), 49–73.

Marn, M. V., & Rosiello, R. L. (1992). Managingprice, gaining profit. Harvard Business Review, 70(5), 84–94.

Mesquita, L. F., Anand, J., & Brush, T. H. (2008). Com-paring the resource-based and relational views:Knowledge transfer and spillover in vertical alli-ances. Strategic Management Journal, 29, 913–941.

Moberg, C. R., Whipple, T. W., Cutler, B. D., & Speh,T. W. (2004). Do the management componentsof supply chain management affect logistics per-formance? The International Journal of LogisticsManagement, 15 (2), 15–30.

Mollenkopf, D., & Dapiran, G. P. (2005). The impor-tance of developing logistics competencies: Astudy of Australian and New Zealand firms. Inter-national Journal of Logistics: Research and Applica-tions, 8 (1), 1–14.

Morgan, R. M., & Hunt, S. D. (1994). The commit-ment-trust theory of relationship marketing. Jour-nal of Marketing, 58 (3), 20–38.

Nair, A. (2006). Meta-analysis of the relationshipbetween quality management practices and firmperformance — Implications for quality manage-ment theory development. Journal of OperationsManagement, 24, 948–975.

Nakano, M. (2009). Collaborative forecasting andplanning in supply chains: The impact on perfor-mance in Japanese manufacturers. The Interna-tional Journal of Physical Distribution and LogisticsManagement, 39 (2), 84–105.

Narasimhan, R., Jayaram, J., & Carter, J. R. (2001). Anempirical examination of the underlying dimen-sions of purchasing competence. Production andOperations Management, 10 (1), 1–15.

Narasimhan, R., & Kim, S. W. (2002). Effect of supplychain integration on the relationship betweendiversification and performance: Evidence fromJapanese and Korean firms. Journal of OperationsManagement, 20 (3), 303–323.

Narasimhan, R., & Nair, A. (2005). The antecedentrole of quality, information sharing and supplychain proximity on strategic alliance formationand performance. International Journal of Produc-tion Economics, 96, 301–313.

Narasimhan, R., Swink, M., & Viswanathan, S. (2010).On decisions for integration implementation: Anexamination of complementarities between prod-uct-process technology integration and supply chainintegration.Decision Sciences, 41 (2), 197–227.

Narayanan, S., Jayaraman, V., Luo, Y., & Swamina-than, J. M. (2011). The antecedents of processintegration in business process outsourcing andits effect on firm performance. Journal of Opera-tions Management, 29 (1–2), 3–16.

Nyaga, G. N., Whipple, J. M., & Lynch, D. F. (2010).Examining supply chain relationships: Do buyerand supplier perspectives on collaborative rela-tionships differ? Journal of Operations Management,28, 101–114.

Olorunniwo, F. O., & Li, X. (2011). Information shar-ing and collaboration practices in reverse logistics.Supply Chain Management: An International Journal,15 (6), 454–462.

Paulraj, A., Lado, A. A., & Chen, I. J. (2008). Inter-organizational communication as a relationalcompetency, antecedents and performance out-comes in collaborative buyer–supplier relationships.Journal of OperationsManagement, 26 (1), 45–64.

Penrose, E. T. (1959). The theory of the growth of thefirm. New York: Oxford University Press.

Peteraf, M. A. (1993). The cornerstones of competitiveadvantage: A resource-based view. Strategic Man-agement Journal, 14 (3), 179–191.

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51

Powell, T. C. (1995). Total quality management ascompetitive advantage: A review and empiri-cal study. Strategic Management Journal, 16 (1), 15–38.

Prahinski, C., & Benton, W. C. (2004). Supplier evalu-ations: Communication strategies to improve sup-plier performance. Journal of OperationsManagement, 22, 39–62.

Priem, R. L., & Swink, M. (2012). A demand-side per-spective on supply chain management. Journal ofSupply Chain Management, 48 (2), 7–13.

Quesada, G., Rachamadugu, R., Gonzalez, M., & Mar-tinez, J. L. (2008). Linking order winning andexternal supply chain integration strategies. SupplyChain Management: An International Journal, 13(4), 296–303.

Rai, A., Patnayakuni, R., & Seth, N. (2006). Firm per-formance impacts of digitally-enabled supplychain integration capabilities. MIS Quarterly, 30(2), 225–246.

Rindfleisch, A., & Heide, J. B. (1997). Transaction costanalysis: Past, present, and future applications.Journal of Marketing, 61 (4), 30–54.

Rodrigues, A. M., Stank, T. P., & Lynch, D. F. (2004).Linking strategy, structure, process, and perfor-mance in integrated logistics. Journal of BusinessLogistics, 25, 65–94.

Rosenberg, M. S. (2005). The file-drawer problemrevisited: A general weighted method for calculat-ing fail-safe numbers in meta-analysis. Evolution,59 (2), 464–468.

Rosenthal, R. (1979). The “File Drawer Problem” andthe tolerance for null results. Psychological Bulletin,86 (3), 638–641.

Rosenthal, R. (1991). Meta-analytic procedures forsocial research, Newbury Park, CA: Sage.

Rosenzweig, E. D., Roth, A. V., & Dean, J. W. (2003).The influence of an integration strategy on com-petitive capabilities and business performance: Anexploratory study of consumer products manufac-turers. Journal of Operations Management, 21, 437–456.

Saeed, K. A., Malhotra, M. K., & Grover, V. (2005).Examining the impact of interorganizational sys-tems on process efficiency and sourcing leveragein buyer–supplier dyads. Decision Sciences, 36 (3),365–396.

Sanders, N. R. (2007). An empirical study of theimpact of e-business technologies on organiza-tional collaboration and performance. Journal ofOperations Management, 25 (6), 1332–1347.

Sanders, N. R. (2008). Pattern of informationtechnology use: The impact on buyer–supplercoordination and performance. Journal of Opera-tions Management, 26 (3), 349–367.

Sanders, N. R., & Premus, R. (2005). Modeling therelationship between firm IT capability, collabora-tion, and performance. Journal of Business Logistics,26 (1), 1–23.

Scannell, T. V., Vickery, S. K., & Droge, C. L. (2000).Upstream supply chain management and compet-

itive performance in the automotive supply indus-try. Journal of Business Logistics, 21 (1), 23–48.

Sezen, B. (2008). Relative effects of design, integrationand information sharing on supply chain perfor-mance. Supply Chain Management: An InternationalJournal, 13 (3), 233–240.

Shadish, W. R., & Haddock, C. K. (1994). Combiningestimates of effect size. In H. M. Cooper & L. V.Hedges (Eds.), The handbook of research synthesis(pp. 261–281). New York: Russell Sage Founda-tion.

Shin, H., Collier, D. A., & Wilson, D. D. (2000). Sup-ply management orientation and supplier/buyerperformance. Journal of Operations Management, 18(3), 317–333.

Simatupang, T. M., & Sridharan, R. (2005). Supplychain discontent. Business Process ManagementJournal, 11 (4), 349–369.

Squire, B., Cousins, P. D., Lawson, B., & Brown, S.(2009). Benefiting from suppliers’ capabilities:Empirical evidence for the extended resource-based view of the firm. International Journal ofOperations and Production Management, 29 (8),766–788.

Stank, T. P., Keller, S. B., & Daugherty, P. J. (2001).Supply chain collaboration and logistical serviceperformance. Journal of Business Logistics, 22 (1),29–48.

Stevens, G. C. (1989). Integrating the supply chain.The International Journal of Physical Distribution andLogistics Management, 19 (8), 3–8.

Swink, M., Narasimhan, R., & Wang, C. (2007). Man-aging beyond the factory walls: Effects of fourtypes of strategic integration on manufacturingplant performance. Journal of Operations Manage-ment, 25 (1), 148–164.

Tai, Y. M., Ho, C. F., & Wu, W. H. (2010). The perfor-mance impact of implementing web-based e-pro-curement systems. International Journal ofProduction Research, 48 (18), 5397–5414.

Thompson, J. D. (1967). Organizations in action. NewYork: McGraw-Hill.

Thun, J. (2010). Angles of integration: An empiricalanalysis of the alignment of internet-based infor-mation technology and global supply chain inte-gration. Journal of Supply Chain Management, 46(2), 30–44.

Van der Vaart, T., & van Donk, D. (2008). A criticalreview on survey-based research in supply chainintegration. International Journal of Production Eco-nomics, 111 (1), 42–55.

Vereecke, A., & Muylle, S. (2006). Performanceimprovement through supply chain collaborationin Europe. International Journal of Operations andProduction Management, 26 (11), 1176–1198.

Vickery, S. K., Jayaram, J., Droge, C., & Calantone, R.(2003). The effects of an integrative supply chainstrategy on customer service and financial perfor-mance: An analysis of direct versus indirect rela-tionships. Journal of Operations Management, 21(5), 523–539.

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Villena, V. H., Gomez-Mejia, L. R., & Revilla, E.(2009). The decision of the supply chain execu-tive to support or impede supply chain integra-tion: A multidisciplinary behavioral agencyperspective. Decision Sciences, 40 (4), 635–665.

Vlachos, I., & Bourlakis, M. (2006). Supply chain col-laboration between retailers and manufacturers:Do they trust each other? Supply Chain Forum, 7(1), 70–81.

Wagner, S. M. (2003). Intensity and managerial scopeof supplier integration. Journal of Supply ChainManagement, 39 (4), 4–15.

Wang, E. T., Tai, J. C., & Wei, H. L. (2006). A virtualintegration theory of improved supply-chain per-formance. Journal of Management Information Sys-tems, 23 (2), 41–64.

Ward, P. T., McCreery, J. K., Rizman, L. P., & Sharma,D. (1998). Competitive priorities in operationsmanagement. Decision Sciences, 29 (4), 1035–1046.

Wernerfelt, B. (1984). The resource-based view of thefirm. Strategic Management Journal, 5 (2), 171–180.

Wiengarten, F., Humphreys, P., Cao, G., Fynes, B., &McKittrick, A. (2010). Collaborative supply chainpractices and performance: Exploring the key roleof information quality. Supply Chain Management:An International Journal, 15 (6), 463–473.

Williamson, O. E. (1975). Markets and hierarchy: Anal-ysis and antitrust implications. New York, NY: FreePress.

Williamson, O. E. (1991). Comparative economicorganization: The analysis of discrete structuralalternatives. Administrative Science Quarterly, 36(2), 269–296.

Williamson, O. E. (2008). Outsourcing: Transactioncost economics and supply chain management.Journal of Supply Chain Management, 44 (2), 5–16.

Wong, C. Y., Boon-itt, S., & Wong, C. (2011). Thecontingency effects of environmental uncertaintyon the relationship between supply chain integra-tion and operational performance. Journal of Oper-ations Management, 29, 604–615.

Zacharia, Z. G., Nix, N. W., & Lusch, R. F. (2011).Capabilities that enhance outcomes of an episodicsupply chain collaboration. Journal of OperationsManagement, 29, 591–603.

Zhou, H., & Benton, W. C. (2007). Supply chain prac-tice and information sharing. Journal of OperationsManagement, 25, 1348–1365.

Rudolf Leuschner (Ph.D., The Ohio State Univer-sity) is an assistant professor in the Department ofSupply Chain Management and Marketing Sciences atRutgers University in Newark, New Jersey. Hisresearch interests include logistics customer service,

supply chain management, and sustainability. Dr. Le-uschner also is pursuing studies of the generalizabilityof research and replication, with a specific focus onmeta-analysis. His work has appeared in many outlets,including the Journal of Business Logistics and the Jour-nal of Supply Chain Management.

Dale S. Rogers (Ph.D., Michigan State University) isa professor of logistics and supply chain management,and Co-Director of the Center for Supply Chain Man-agement, at Rutgers University in Newark, New Jersey.He also serves as the Leader in Sustainability andReverse Logistics Practices for ILOS (Instituto de Logis-tics e Supply Chain) in Rio de Janeiro, Brazil. In2012, Dr. Rogers became the first academic recipientof the International Warehouse and Logistics Associa-tion Distinguished Service Award in its 120-yearhistory.

François F. Charvet (Ph.D., The Ohio State Univer-sity) is the Logistics Network Strategist for Staples,Inc. In this role, he is responsible for setting the stra-tegic direction governing Staples’ network of fulfill-ment centers, distribution centers and deliveryorganizations. Dr. Charvet currently is leading theintroduction of network optimization tools and pro-moting analytics-driven methods to support strategicsupply chain design initiatives at Staples. Prior to join-ing the private sector, Dr. Charvet was an assistantprofessor of supply chain management at Northeast-ern University. He has published the results of hisresearch into supply chain analytics, supply chainintegration and collaboration, and logistics customerservice in outlets that include the Journal of BusinessLogistics, the Journal of Supply Chain Management, andSupply Chain Forum.

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53

APPEN

DIX

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Volume 49, Number 2


54

1(Continued).

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April 2013


55

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0.10

15

36

41

Sezen,B.

SCM

2008

125

0.12

0.35

0.27

637

42

Wong,C.Y.,Boon-itt,S.,

Wong,C.

JOM

2011

151

0.23

0.46

0.38

12

38

43

Frohlich,M.T.,

Westbrook,

R.,

JOM

2002

485

0.44

0.45

0.45

2

39

44

Boon-Itt,S.,Paul,H.

MRN

2006

28

�0.27

0.50

0.24

15

40

45

Braunscheidel,M.J.,

Suresh,N.C.

JOM

2009

218

0.08

0.52

0.31

12

46

Braunscheidel,M.J.,

Suresh,N.C.,

Boisnier,A.D.

HRM

2010

41

47

Lau,A.K.,Yam,R.C.,

Tang,E.P.

IMDS

2007

251

0.07

0.24

0.17

11

48

Lau,A.K.,Tang,E.P.,

Yam,R.C.

JPIM

2010

42

49

Kim

,D.,Cavu

sgil,

E.

JBIM

2009

184

0.31

0.42

0.37

243

50

Villena,V.H.,Gomez-Mejia,L.

R.,

Revilla,E.

DS

2009

133

0.16

0.22

0.19

2

44

51

Molle

nko

pf,D.,Dapiran,G.P.

IJLR

A2005

194

0.16

0.25

0.21

10

45

52

Squire,B.,Cousins,

P.D.,

Lawso

n,B.,Brown,S.

IJOPM

2009

104

0.04

0.04

0.04

1

46

53

Vlach

os,

I.,Bourlakis,

M.

SCFIJ

2006

97

0.18

0.43

0.31

247

54

Wang,E.T.,Tai,J.

C.,

Wei,H.L.

JMIS

2006

149

0.19

0.32

0.26

2

48

55

Sanders,N.R.

JOM

2008

241

0.21

0.34

0.28

849

56

Narayanan,S.,Jayaraman,V.,

Luo,Y.,Swaminathan,J.

M.

JOM

2011

205

0.60

0.64

0.62

2

50

57

Olorunniwo,F.O.,Li,X.

SCM

2010

65

0.30

0.66

0.47

451

58

Wiengarten,F.,Humphreys,P.,

Cao,G.Fyn

es,

B.,McK

ittrick,

A.

SCM

2010

153

0.15

0.53

0.33

3

Appen

dix

A(C

ontinued

).

Volume 49, Number 2


56

1(Continued).

Sample

Article

Author(s)

Journal

Year

Sample

Size

Min

rMaxr

Meanr

No.ofr’s

52

59

Gim

enez,

C.,Ventura,E.

IJOPM

2005

64

0.29

0.73

0.50

353

60

Rai,A.,Patnayaku

ni,R.,Seth,N.

MISQ

2006

110

�0.04

0.40

0.24

12

54

61

Lee,C.H.,Huang,S.Y.,

Barnes,

F.B.,Kao,L.

TQM

2010

132

0.34

0.39

0.37

2

55

62

Jayaram,J.,Tan,K.C.

IJPE

2010

411

0.28

0.28

0.28

156

63

Lee,G.J.

JBBM

2010

170

0.37

0.37

0.37

157

64

Tai,Y.M.,Ho,C.F.,Wu,W.H.

IJPR

2010

137

0.42

0.42

0.42

158

65

Cao,M.,Vonderembse,M.A.,

Zhang,Q.,Ragu-N

athan,T.S.

IJPR

2010

211

0.69

0.69

0.69

1

59

66

Zach

aria,Z.G.,Nix,N.W.,Lu

sch,R.F.

JOM

2011

473

0.54

0.62

0.58

260

67

Ha,B.,Park,Y.,Cho,S.

IJOPM

2011

265

�0.15

0.73

0.37

361

68

Naka

no,M.

IJPDLM

2009

65

0.53

0.70

0.62

362

69

Vereecke,A.,Muylle,S.

IJOPM

2006

374

0.06

0.31

0.15

24

63

70

Closs,D.J.,Savitskie,K.

IJLM

2003

306

0.08

0.45

0.24

364

71

Dabhilkar,M.,Bengtsso

n,L.,

vonHaartman,R.,Ahlstro,P.

JPSM

2009

136

�0.06

0.31

0.09

9

65

72

Eltantawy,

R.A.,Giunipero,L.,Fox,

G.L.

IMM

2009

152

0.72

0.72

0.72

166

73

Koufteros,

X.,Rawski,G.E.,Rupak,

R.

DS

2010

191

0.11

0.16

0.14

367

74

Koufteros,

X.,Vonderembse,M.,Jayaram,J.

DS

2005

244

0.03

0.38

0.19

968

75

Chen,H.,Daugherty,

P.J.,Roath,A.S.

JBL

2009

124

0.58

0.69

0.64

269

76

Chen,H.,Tian,Y.,Ellinger,A.E.,Daugherty,

P.J.

JBL

2010

124

0.43

0.43

0.43

170

77

Lee,C.W.,Kwon,I.W.,Seve

rance

,D.

SCM

2007

122

0.54

0.61

0.58

671

78

Nyaga,G.N.,Whipple,J.

M.,Lynch

,D.F.

JOM

2010

370

0.30

0.35

0.33

272

78

Nyaga,G.N.,Whipple,J.

M.,Lynch

,D.F.

JOM

2010

255

0.43

0.49

0.46

273

79

Griffith,D.A.,Harvey,

M.G.,Lu

sch,R.F.

JOM

2006

290

0.17

0.23

0.20

274

80

Gulati,R.,Sytch

,M.

ASQ

2007

151

0.18

0.60

0.40

375

81

Handfield,R.B.,Petersen,K.,Cousins,

P.,La

wso

n,B.

IJOPM

2009

151

0.13

0.51

0.31

476

82

Germ

ain,R.Davis-Sramek,

B.,Lo

nial,S.C.,Raju,P.S.

JBL

2011

175

0.10

0.10

0.10

177

83

Zhou,H.,Benton,W.C.

JOM

2006

125

0.28

0.36

0.32

278

84

Rodrigues,

A.M.,Stank,

T.P.,Lynch

,D.F.

JBL

2004

284

0.34

0.48

0.42

379

85

Mesq

uita,L.

F.,Anand,J.,Brush,T.H.

SMJ

2008

253

0.31

0.31

0.31

180

86

Forza,C.

IJPDLM

1996

43

0.17

0.28

0.22

4

Appen

dix

A(C

ontinued

).

April 2013


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