managerial views of supply chain collaboration an empirical study

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Sridharan & SimatupangManagerial Views of Supply Chain Collaboration

Gadjah Mada International Journal of BusinessMay-August 2009, Vol. 11, No. 2, pp. 253273

MANAGERIAL VIEWS OF SUPPLY CHAINCOLLABORATION

An Empirical Study

Ramaswami SridharanUniversity of Newcastle in Australia

Togar M. SimatupangSchool of Business and Management, Bandung Institute of Technology, Bandung, Indonesia

This paper is carried out to empirically examine managerialperceptions on the relationship between supply chain collaborationpractice and operational performance. The framework suggests thatcollaborative practice is characterised by three distinct factors: (1)decision synchronisation, (2) information sharing, and (3) incentivealignment, which enable the chain members to effectively matchsupply with customer demand. An important question is whether ornot collaborative practice leads to better operational performance.A survey research was employed to assess the relationship betweencollaborative practice and operational performance of New Zealandcompanies. The survey results show significant positive impacts ofkey factors of collaborative practice on operational performance.The findings suggest that information sharing, decisionsynchronisation, and incentive alignment are important determi-nants of operational performance. This study demonstrates that thechain members need to understand the role of different key factorsof collaborative practice that can be redesigned to leverage opera-tional performance.

Keywords: channel relationships; collaboration; incentive alignment; informationsharing; New Zealand; supply chain management; survey research

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Gadjah Mada International Journal of Business, May-August 2009, Vol. 11, No. 2

IntroductionGlobal competition has fundamen-

tally changed the economic environ-ment of firms along the supply chain.End customers have greater controlover the buying process and the finan-cial ability to make choices of productfeatures. It is not surprising that theglobal market results in higher de-mand uncertainty with shorter productlife cycles and greater variety (Fisher1997). As interdependence increasesbetween firms, they need to collabo-rate to effectively manage flows ofproducts along the entire value-addedsupply chain to be available for endcustomers (Whipple and Russell 2007).Collaboration enables both parties tocombine knowledge and capabilitybetter than acting in isolation (Dyerand Singh 1998). Retailers, for in-stance, know consumer preferencesdue to their direct access to end cus-tomers, but are lacking in knowledgeof product design and delivery.Partnering suppliers, on the other hand,have better knowledge of product de-sign, production capability, and deliv-ery capability.

Supply chain collaboration bringsadvantages to participating members,and enables them to experience in-creases in their common market sharesand profitability (Parks 1999). Theseadvantages can be realised only if bothparties work together to speed up thedecision-making process in deliveringthe right product to the right place atthe right time in the right condition forthe right cost (Fisher 1997). As an

illustration, the cooperation betweenK-Mart and Lee Apparel shows thatboth parties reap the advantages ofcollaboration to match supply and de-mand. K-Mart shares points-of-sale(POS) data with Lee. Lee uses this datato monitor the exact products sold,including color, size, and style, in eachK-Mart store. With this information inhand, Lee knows which products needto be restocked at each K-Mart loca-tion, and is thus able to coordinate itsproduction and distribution plans toaccommodate its major customersneeds. Lee can also identify early warn-ing signs of merchandising problemsfor Lees products at particular K-Mart locations. Early warning signshelp both parties to devise quick re-sponses that lead to reductions in stockouts and markdowns, thereby improv-ing customer service and sales.

Supply chain collaboration hasbecome a central issue in supply chainmanagement as it facilitates close co-operations amongst chain members(Spekman et al. 1998). Although thebasic tenet of supply chain manage-ment is the integration of key businessprocesses along the supply chain thatcreate value for end customers andother stakeholders, the main key ismanaging the interface process of de-cision making amongst interdependentfirms that voluntarily work together asa supply chain (Stank et al. 2001; Zhaoet al. 2001). Previous researchers haveaddressed the issue of supply chaincollaboration as the central part ofsupply chain management. Bowersoxet al. (2000) emphasize that the con-

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cept of integrated supply chain man-agement is a collaborative-based strat-egy to link cross-enterprise businessoperations to achieve a shared visionof market opportunity. In a similarvein, Ballou et al. (2000) argue thatsupply chain management includesinterfirm cooperation that coordinatesproduct movements across the legalboundaries of independent firms. Stanket al. (2001) also emphasize the factthat supply chain management involvessome levels of coordination of activi-ties and processes both intra- and in-terfirm.

Given the significance of supplychain collaboration, this research con-tributes to the literature of supply chaincollaboration through characterizingcollaborative practice into three inter-related enabling factors: (1) decisionsynchronization, (2) information shar-ing, and (3) incentive alignment. Theresearch also tests hypotheses onwhether or not the three factors ofcollaborative practice positively con-tribute to operational performance. Sur-vey research was carried out to assessthe relationship between collaborativepractice and performance.

The study is organized as follows.First, related research as the founda-tion for this paper is presented. Thenext section proposes an operatingdefinition for collaborative practicethat consists of three enabling factors,namely decision synchronization, in-formation sharing, and incentive align-ment. Research hypotheses are alsodeveloped in this section to describethe influence of collaborative practice

on operational performance. After-wards, the research method, compris-ing data collection, development ofmeasures, and analysis is given. Find-ings and discussion are subsequentlypresented. Finally, the paper providesconcluding remarks and recommenda-tions for further research.

Related ResearchA review of related research re-

veals an increased interest in supplychain collaboration. The literature canbe classified into the development ofthe concept of supply chain collabora-tion and empirical research. The con-ceptual study on supply chain collabo-ration deals with the definitions andcomponents or success factors of sup-ply chain collaboration. The empiricalresearch provides evidence obtainedfrom the survey and case studies thatdescribe the extent to which firms haveadopted the concept of collaborationand discussions of factors that facili-tate or hamper the implementation ofsupply chain collaboration. This sec-tion presents the previous work thatrelates to the conceptualization, suc-cess factors, and empirical evidence ofsupply chain collaboration.

The literature provides diversedefinitions of the concept of supplychain collaboration. The term supplychain collaboration has been used todescribe partnership, logistics alliance,and coordination. Johnston andLawrence (1988) define partnership inthe supply chain as independent firmsthat work closely together to manage

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the flow of goods and services alongthe entire value-added chain. Simi-larly, Buzzell and Ortmeyer (1995)propose partnership as the coopera-tion between suppliers and retailers inwhich the parties agree on objectives,policies, and procedures for orderingand physical distribution of suppliersproducts to end customers. Narus andAnderson (1996) use partnership todescribe the cooperations amongst in-dependent but related firms to shareresources and capabilities to meet theircustomers most extraordinary needs.Bowersox (1990) uses logistics alli-ance to characterize the cooperationsamongst independent firms along asupply chain that share resources indelivering products to ultimate cus-tomers. Although previous research-ers have used different terms for col-laboration, it is important to note thatcollaboration is an evolving processrather than a static process that liesbetween adversarial relationships andjoint ventures (Lambert et al. 1999).Therefore, a useful definition of sup-ply chain collaboration is the processof independent firms working togetherto deliver products and services to endcustomers for the basic purpose ofoptimizing higher long-range profit forall chain members than can be achievedby acting alone (Simatupang andSridharan 2002).

Besides definitions, the successfactors of supply chain collaborationalso vary amongst previous research-ers. Buzzell and Ortmeyer (1995) pro-pose key elements of improvementopportunities from collaboration,

namely better assortment planning,faster new product development, moreeffective replenishment, faster orderprocessing, better inventory control,more effective receipt and distribu-tion, and better store selling tasks.According to Spekman et al. (1998),supply chain collaboration occurs whenparticipating members share informa-tion freely, work together to solve com-mon problems, devise joint planning,and make their success interdepen-dent. Ballou et al. (2000) emphasizethree components of inter-organiza-tional coordination: (1) performancemetrics, (2) information sharing, and(3) benefits allocation. Lee (2000) pro-poses the concept of supply chain inte-gration that incorporates informationsharing, logistics coordination, and or-ganizational relationship linkages.Mentzer et al. (2000) argue that supplychain collaboration is characterizedby the sharing of information, knowl-edge, risk, and profit. In addition,Simatupang and Sridharan (2005) as-sert that information sharing, decisionsynchronization, incentive alignment,collaborative performance systems,and process improvements are instru-ments used to enable supply chaincollaboration.

The bulk of empirical researchshows that more and more firms areattracted to implementing supply chaincollaboration (Bowersox 1990;Whipple et al. 2007), and concludesthat collaboration brings positive ben-efits to participating members (Buzzelland Ortmeyer 1995; Stank et al. 2001;Zhao et al. 2001). According to Mohr

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and Spekman (1994), the success ofcollaboration depends on commitmentand trust, effective communications tospecify roles, responsibilities, and ex-pectations, and the conflict resolutiontechniques of joint problem solving.Stank et al. (2001) find that internalcollaboration mediates the relation-ship between external collaborationand logistical service performance.Spekman et al. (1998) discover thatinformation sharing is a key ingredientto reducing costs and improving cus-tomer satisfaction. Stank et al. (1999)also find the positive effect of thecollaborative practice with key cus-tomers on logistics cost and customerservice. Furthermore, Sheu et al. (2006)recently find from a case study thatsupply chain architecture in informa-tion sharing, inventory systems, coor-dination, and IT capabilities affectsthe level of collaboration.

Another stream of empirical re-search relies on the use of a collabora-tive management process to facilitatesupply chain collaboration (Ireland andBruce 2000). Kurt Salmon Associatespromotes efficient-consumer-response(ECR) that facilitates planning andexecution for efficient promotion, re-plenishment, store assortment, andproduct introduction (Barratt andOliveira 2001; Frankel et al. 2002).Another initiative called vendor-man-aged inventory (VMI) delegates stock-ing decisions to main suppliers in sucha way the suppliers are responsible formonitoring stock levels and replenish-ing products sold at the retailer stores(Lee et al. 1997; Whipple et al. 2007).

Sherman (1998) reports a recent move-ment in Collaborative Planning, Fore-casting, and Replenishment (CPFR).CPFR is proposed to enable participat-ing members across the supply chainto remain competitive by taking a ho-listic approach to delivering productsto ultimate customers. This approachhas the potential to deliver increasedsales, interorganizational streamliningand alignment, administrative and op-erational efficiency, improved cashflows, and improved return on assets.

Conceptual ModelAs the nature of collaboration is

to optimise profitability, the chainmembers need to plan, execute, andcontrol key decisions at the interfaceboundaries related to defining and de-livering products to ultimate custom-ers that lead to mutual advantage. Thecollaborative supply chain assumes thatthe chain members synchronize deci-sion making across a supply chain,share information to make effectivedecisions that improve performance,and employ incentive schemes forspecifying reward and punishmentmechanisms (Lee et al. 1997;Simatupang and Sridharan 2002). As aconsequence, the structure of ongoingcollaboration can be characterized bythree enabling factors of collaborativepractice: (1) information sharing, (2)decision synchronization, and (3) in-centive alignment (Simatupang andSridharan 2005).

The three factors of collaborativepractice are expected to facilitate the

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chain members into cross-organiza-tional cooperation in realizing collabo-rative benefits. To operationalize thisconcept, a hypothetical framework isdeveloped to link the three factors ofcollaborative practice to operationalperformance. The framework consistsof two variables: (1) collaborative prac-tice and (2) consequences of collabo-rative practice. Briefly, it suggests thatcollaborative practice positively af-fects operational performance. Hypoth-eses are developed based on thisconceptualization and previous workin supply chain collaboration. The re-maining part of this section presentsthese hypotheses.

Information sharing can be de-fined as a process that facilitates thechain members to capture and dis-seminate timely, relevant, and accu-rate information such that the recipi-ent is able to plan, execute, and controlsupply chain operations. Effective in-formation sharing provides a sharedbasis for concerted actions by differ-ent functions across interdependentfirms (Whipple et al. 2002). Examplesof shared information include points-of-sale (POS) data, updated forecasts,production and delivery schedules,inventory levels, delivery lead-times,and inventory carrying costs. Informa-tion sharing also facilitates clarity aboutdemand, the fulfillments process, andcommon performance for all partici-pating members (Zhao et al. 2001).Collaborative initiatives, such as Effi-cient Consumer Response (ECR),Quick Response (QR), and Vendor-Managed Inventory (VMI), are based

on the concept of information sharingamongst the chain members to matchsupply and demand (Simchi-Levi et al.2007; Sherman 1998). Fisher (1997)finds that information sharing can yieldsignificant performance improvementsin all chain members, such as cohesivemarket focus, better coordination ofsales and demand fulfillment, and mini-mum risks associated with demanduncertainty. Information sharing pro-vides a unifying visibility for the ef-forts of chain members to improveoperational performance, thereby en-abling the chain members to forecastaccurately, reduce order variability,shorten delivery lead time, and reduceinventory levels (Fisher 1997; Lee etal. 1997). Companies like Ford and itsvendors, Dell and its suppliers, Wal-Mart and Procter and Gamble (P&G)are widely known to practice informa-tion sharing to reduce working capitaland inventories (Simchi-Levi et al.2007). Therefore, there is a direct linkbetween the availability and the qual-ity of timely information and the per-formance of a supply chain. Relevantto this conceptualization as well as onthe basis of this discussion, the follow-ing hypothesis is proposed.Hypothesis 1. Information sharing is

positively related to op-erational performance.

Decision synchronization refersto a joint initiative of collaborativedecision making within planning andoperational contexts for identifying keydecision points, distributing responsi-bilities, reconciling conflicting goals,sharing resources, handling exceptions,

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and solving problems (Bowersox et al.2003; Mohr and Spekman 1994). Theplanning context integrates decisionson long-term planning and measures,such as selecting target markets, prod-uct assortments, customer service level,delivery schedule, promotion, and fore-casting. The operational context inte-grates order generation and deliveryprocess that can ship schedules andreplenish products to the stores. Deci-sion synchronization encourages thechain members to have a sense ofbelonging in which all decisions worktoward a common goal of serving endcustomers (Lee et al. 1997; Morashand Clinton 1998). This reduces thegap between delivery requirements andactual delivery, thereby improvingcustomers perceptions of fulfillmentperformance (Ramdas and Spekman2000). Customers are satisfied as theyfind products suited to their prefer-ences and tastes at the right time andright price. Many improvements aremade possible by employing decisionsynchronization and the associateddynamic control amongst autonomousmembers of the supply chain to aligndifferent decision sharing options withvarying flexibility requirements (Leeet al. 1997). Decision synchronizationfacilitates the chain members to reas-sign decision rights in order to be ableto identify exceptions and make effec-tive decisions like stocking, distribu-tion, outsourcing, and shipping, therebyproviding responsibilities for improv-ing the performance of the supply chain(Holweg et al. 2005; Simatupang andSridharan 2005). Bowersox et al.

(2000) report that decision synchroni-zation contributes to a reputation ofon-time delivery and consistent prod-uct availability. This discussion sug-gests the following hypothesis.Hypothesis 2. Decision synchroniza-

tion is positively relatedto operational perfor-mance.

Incentive alignment refers to thedegree to which chain members sharecosts, risks, and benefits (Narayananand Raman 2004; Simatupang andSridharan 2005). The costs, such asadministration and technology invest-ment, need to be shared fairly amongstthe chain members in order to main-tain the commitment of each party tothe collaborative efforts (Narus andAnderson 1996). Moreover, chainmembers commit to the collaborativeefforts if they can realize and capturerelevant benefits that contribute to theirfuture survival. Benefits of collabora-tion include both commercial gains,such as increased sales, and perfor-mance improvements, such as low-ered inventory costs (Corbett et al.1999). Incentive alignment also in-volves risk sharing among the chainmembers in managing demand, sup-ply, and price uncertainties. Settingand applying appropriate incentives,such as revenue sharing, transfer pric-ing, consignment, shortage reimburse-ment, and backlog penalty, motivatethe chain members to take decisionscompatible with the achievement ofhigher performance (Giunipero et al.2001; Lee and Whang 1999). The chainmembers are encouraged to ensure on-

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time delivery and responsiveness inthe presence of incentive alignmenttied to customer service at a just-in-time level (Narayanan and Raman2004). It can be stated that incentivealignment facilitates the chain mem-bers to act consistently with improv-ing the performance of the supply chain(Lee and Whang 1999). This observa-tion suggests the following hypoth-esis.Hypothesis 3. Incentive alignment is

positively related to op-erational performance.

Research MethodA survey method is utilized to

gain responses from sample that re-flects various degrees of collaborationbetween suppliers and retailers. In thissetting, survey research is useful toaccommodate diverse respondents andthereby has a high level of gene-ralizability (Pinsonneault and Kraemer1993). In addition, linking interfacecoordination and key performanceoutcomes provides additional insightsinto current practice of collaborationthat affects performance. Empiricalfindings, which confirm this relation-ship, extend the validity of collabora-tive practice.

The development of research in-struments follows three steps: (1) lit-erature review, (2) conceptualization,and (3) pre-test. The first phase con-sists of reviewing literature on supplychain collaboration. The literature in-dicates that the issue of supply chaincollaboration is of substantial interest

to many firms, and serves as an activeresearch arena with the advent of in-formation technology. Second, a pre-liminary conceptual framework is de-veloped to link the collaborative prac-tice and operational performance.There are three interface dimensionsbased on the interface processes be-tween the chain members: (1) decisionsynchronization, (2) information shar-ing, and (3) incentive alignment. A setof questionnaires was developed tocapture and represent the concept ofsupply chain collaboration. Scales forthe study comprised newly generateditems and items that have been usedpreviously in the literature. To de-velop a scale, the domain of a variablewas itemized into a set of activities(Cavana et al. 2001). Five items weredeveloped to measure each dimensionof supply chain collaboration. A panelconsisting of practitioners and aca-demics was asked to review and modifyinitial items. These experts clarifiedand suggested useful terms and wereconfident that the items posed in thequestionnaires accurately reflected theconcept of collaboration.

Finally, a pre-test was carried outto confirm the stability with which theitems measure the concept of supplychain collaboration. A panel of practi-tioners and researchers was asked toidentify ambiguous items, poorlyworded questions, and poor instruc-tions to answer the questionnaires.Several items were rewritten afterevaluation by the panel. The panel alsofound no major problems with any ofthe response formats, directions, and

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other survey procedures. Additionalevaluation was also made to ensure theconsistency of the measures used inprior research. Several items weremodified slightly after this evaluation.The final set of questionnaires reflectsthe changes.

The final set of questionnairescontains general characteristics of re-spondent, items related to the mea-surement of the three dimensions ofcollaboration, and items intertwinedwith the measurement of performancevariables. The measures of scale aredescribed as follows. The informationsharing (IS) scale describes the extentto which the chain members share pri-vate information required for plan-ning, executing, and controlling sup-ply chain operations. Items of the ISscale include data exchange about pro-motional events, demand forecasts,inventory holding costs, on-hand in-ventory levels, and order tracking.These measurement items are adaptedfrom Zhao et al. (2001) and Whipple etal. (2002). A five-point format (1 =strongly disagree, 5 = strongly agree)is used for each item.

The decision synchronization(DS) scale describes the extent to whichthe parties make decisions jointly ratherthan independently. The chainmembers perception on DS is mea-sured using five parallel items. Theseitems capture joint decisions on reduc-ing demand fluctuations, developingjoint forecasts, co-managing inventoryrequirements, ensuring on-time deliv-ery, and improving product availabil-ity. The five items are adapted from

previous researchers (Morash andClinton 1998; Ramdas and Spekman2000). Each item is assessed on a five-point format ranging from 1 (stronglydisagree) to 5 (strongly agree), with adefined neutral point at 3.

The incentive alignment (IA) scaledescribes the extent to which the chainmembers share costs, risks, and ben-efits to encourage continuous improve-ment. The IA scale is adapted fromGiunipero et al. (2001) and Morashand Clinton (1998), and measured by afive-item scale assessing risks sharingassociated with demand uncertainties,shared savings of lowering inventorycosts, investment sharing of collabo-rative efforts, joint effort for increas-ing sales, and benefit sharing. Eachitem is assessed with the range from 1(strongly disagree) to 5 (stronglyagree), with a defined neutral point at3.

The measures of supply chainperformance used in this study includefulfillment, inventory, and responsive-ness (Ramdas and Spekman 2000).Fulfillment measures the extent towhich the collaborative practice af-fects the ability of the chain membersto satisfy consumer delivery dates(Croxton 2003; Morash and Clinton1998; Ramdas and Spekman 2000).This includes on-time delivery (i.e.,the percentage of all orders sent on orbefore the promised delivery date),accuracy (i.e., the percentage of cor-rect orders), and fill rate (i.e., amountof order filled as compared to amountrequested). Inventory refers to the ex-tent to which the collaborative prac-

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tice affects inventory and its associ-ated costs. This includes merchandiseinventory turnaround, a decrease ininventory days-of-supply, and a de-crease in inventory carrying cost(Ramdas and Spekman 2000). Respon-siveness measures the extent to whichthe collaborative practice affects lead-time and flexibility to accommodatedemand changes, and this measure isadapted from Wisner (2003). A five-point format ranging from 1 (stronglydisagree) to 5 (strongly agree) is usedfor each item.

The unit of analysis in this re-search is a specific retailer-supplierrelationship. This unit of analysis ischosen for several reasons. First, theretailer is an agent who ultimatelymeets the end customer demands ofthe entire supply chain (Whipple andRussell 2007). The retailers positionis crucial to improving supply chainperformance in terms of customer ser-vice for end customers. The retaileralso has intimate knowledge of de-mand condition because of direct con-tact with end customers. Sharing cur-rent and advanced demand informa-tion with the supplier may mitigate thepropagation of demand variation facedby the supplier (Lee et al. 1997). If theretailer shares private informationabout advanced customer demand withthe supplier, the supplier might be ableto anticipate this demand by placing amaterial order in advance or maintain-ing an inventory buffer to avoid prod-uct stockouts. At the same time, thesupplier can use points-of-sale (POS)data to create a quick response to the

retailers store shelves at predeter-mined levels (Parks 1999).

Second, the geographical proxim-ity of New Zealand companies tends toforce the domestic manufacturers toship products to their retailers directlyrather than through distributors(Sankaran 2000). This direct linkmakes the retailers position signifi-cant for the swift flow of the suppliersproducts to end customers. The samplealso represents a specific relation of adistributor and a retailer. Distributorcompanies in New Zealand mainlyaccommodate products from overseasmanufacturers to be delivered to theirretailers.

Third, the retailer-supplier link asthe unit of analysis is consistent withprevious research on advanced initia-tives such as efficient consumer re-sponse (ECR), vendor-managed inven-tory (VMI), and collaborative plan-ning, forecasting, and replenishment(CPFR) that employ a similar unit ofanalysis (Barratt and Oliveira 2001).

Data CollectionThe conceptualization of collabo-

rative relationships as multidimen-sional in nature requires substantialamount of information regarding sup-plier-retailer relationships in examin-ing the proposed conceptual model.Supplier firms can be manufacturersthat directly deliver their products toretailers or to distributors that mediatemanufacturers and retailers. The re-tailers often sell the majority of a part-ner suppliers products. Those compa-

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nies operate with consumer products.This sample restriction reduces theextraneous sources of variation thatmight lower consistency of responses.

Retail industry in New Zealandconsists of retailers of various sizes,ranging from small owner operators,general merchandise chains, special-ized chains, traditional departmentsstores, to big multinational retailerssuch as Foodstuffs, Progressive Enter-prises, Arthur Barnett, Briscoes, Farm-ers, K-Mart, The Warehouse, BabyFactory, Hannahs, and Ezibuy(Albertson 2009). There are more than30,000 retail outlets spread through-out New Zealand, including supermar-kets, department stores, clothing re-tailing, footwear retailing, furnitureretailing, houseware retailing, toy andgame retailing, stationery goods re-tailing, domestic appliances, andsoftgoods. New Zealand has more than150 national and regional chains oper-ating about 7,500 stores. NewZealanders spend more than $12,000in shops every year, for every adult,child, and baby. That adds up to annualretail sales of more than $65 billion.The industry employs 325,000 people,about 17 percent of the nationalworkforce.

The traditional relationship be-tween retailers and suppliers is de-scribed as a transactional basis, as eachparty is most concerned with its owninterests. However, some retailers andsuppliers have made great efforts todevelop strategic partnerships sinceforeign retailers brought in the con-cept of supply chain collaboration in

the last few years. The drive to deploythis kind of system often comes frombusiness pressure to take the cost outof transactions. The suppliers have toimprove the documentation and sub-sequent delivery of goods. Progres-sive Enterprise, which operatesFoodtown, Woolworths, and Count-down chains, for example, worksclosely with its suppliers to improveits service level to at least 97 percentby involving suppliers in an ongoingco-managed inventory process. Its sup-pliers send their representatives to workwith Progressive to ensure that fore-casts and data are accurate, so they cansee both sets of data and how theywork to create an electronic order, anadvance shipping notice to advise Pro-gressive as to what the stores willreceive, and a purchase order adjust-ment (POA) document for the suppli-ers to agree to order adjustments be-fore shipment. With the variety of re-lationships between retailers and sup-pliers, the selected companies offer agood mixture of scenarios for the pur-pose of this study.

The sample was selected from theNew Zealand Business Whos Who,the New Zealand Business Directory,and Kompass. The respondents wereselected by checking their companytypes and product descriptions thatsuited this research. Duplicate listingswere deleted, leaving 400 firms. Thetargeted key informants who filled outthe questionnaires included generalmanagers, marketing managers, logis-tics managers, and purchasing manag-ers. Respondents were instructed to

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complete the entire questionnaires inreference to their relationship with aspecific trading partner.

Several techniques were used tomotivate respondents to participate inthis research (Dillman 1978). First,the survey was accompanied by a coverletter that described the objectives ofthe study and the contributions it madeto supplier-retailer collaboration. Sec-ond, the cover letter also stated that theMassey University Human EthicsCommittee (MUHEC) had approvedthe survey with PN Protocol 02/107,which increased the legitimacy of thesurvey. Third, all respondents wereguaranteed anonymity and offered asummary report of the results in ex-change for their participation. Fourth,a pre-addressed stamped envelope wasprovided to make it easy for the re-spondents to return the completed ques-tionnaires. Finally, respondents whodid not reply in four weeks were maileda reminder letter and another copy ofthe questionnaires.

After the second round of sendingquestionnaires to the non-respondingfirms, the survey produced 140 re-sponses. 28 respondents chose to de-cline to participate in the study on thebasis of company policy. 21 were re-turned due to missing addressees. 12respondents stated that their firms hadinappropriate supply chain structures,which were irrelevant to this study.There were three questionnaires withexcessive missing data. After elimi-nating these questionnaires, the validresponses were 76 out of 367 represen-tative sample firms, resulting in a re-

sponse rate of 21 percent. This re-sponse rate is comparable to the previ-ous study on supply chain manage-ment in New Zealand (Basnet et al.2003), and provides adequate data forfurther analysis (Malhotra and Grover1998).

The non-response bias was testedby comparing early and late respon-dents (Armstrong and Overton 1977).The data set was divided into threeaccording to the number of days frominitial mailing until receipt of the re-turned questionnaires. The basic ratio-nale is that late respondents are moresimilar to non-respondents than areearly respondents. There is no signifi-cant difference (p > .10) in the meansresponses between early and late re-spondents for all included variables.This finding provides reasonable evi-dence that non-response bias is not aproblem in the data.

Respondents represent mostlysome form of retailing (50%) but alsoinclude manufacturers (38.16%), anddistributors (11.84%). The averageannual sales of the respondents is be-tween NZ$25-50 million. The averagenumber of employees is about 250people. The respondents have beeninvolved in the supplier-retailer col-laboration for an average of two years.The respondents are spread across sixbroad product categories. Clothing andfootwear comprise 22.37 percent, foodand beverages 21.05 percent, homeimprovement and building supplies19.74 percent, electronics and appli-ances 18.42 percent, stationery andtoys 10.53 percent, and health prod-

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ucts 7.89 percent of the sample. Theresulting sample reflects the diversityof the retailer-supplier link inherent inthe marketplace. Since the size of thesample is considered small, there is noattempt to classify and contrast thepractice of collaboration across differ-ent sizes of companies since the inten-tion is to explore the relationship be-tween collaborative practice and op-erational performance.

Data AnalysisThe collected data provide a basis

for measurement validation and statis-tical analysis. For measurement vali-dation, conventional methods are used,including coefficient alpha, item-to-total correlations, and exploratory fac-tor analysis (Bienstock et al. 1997;Churchill 1979). The threshold valueof the criteria for assessing adequatemeasurement properties is greater than0.7, above the minimum level sug-gested by Nunnally (1978). Factoranalysis is used to assess the measure-ment quality of the conceptual modelbecause it allows a stringent test of theconvergent and discriminant validityof the constructs in this study (Guinanet al. 1998). Consistent with theconceptualization, DS, IA, and IS arespecified as three factors. Constructvalidity is supported by the fact thatthe loading of each item on its respec-tive scale is greater than 0.49. Lowcorrelation between a factor and itsnon-associated items indicates a sup-port for discriminant validity. Table 1lists the scale items, factor loadings,

means, standard deviations, and coef-ficient alphas for the predictors. Theresults of factor analysis in Table 1provide statistical evidence of the con-vergent and discriminant validity ofthe three dimensions in the study. Twoitems of information sharing (inven-tory cost data and sharing collabora-tive cost) and one item of decisionsynchronization (creating joint fore-cast) cross-load on factors with whichthey are not supposed to be related andhave been deleted (Hair et al. 2005).The reasons for this deletion might berelated to little attention given for costreduction efforts between parties andlimited efforts given to create jointforecast. The chain members moreemphasize sharing forecast data ratherthan joint forecasting for avoiding dif-ficulties in intensive meetings betweenparties to identify and resolve excep-tions when conducting joint forecast-ing. The second interpretation is thewide range of collaborative continuumamongst respondents that might be asmall portion of them practice fullcollaboration such as CollaborativePlanning, Forecasting, and Replenish-ment (CPFR) (Basnet et al. 2003).After removing the items not conver-gent to the predetermined scale, mea-sures of Cronbachs alphas range from0.71 to 0.82, which indicate an accept-able reliability of internal consistency.

Cronbachs alphas for perfor-mance variables are also estimated.Performance criteria of fulfillment, in-ventory, and responsiveness haveCronbachs alpha scores of 0.77, 0.83,and 0.71, respectively. These reliabil-

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ity coefficients show a high degree ofinternal consistency amongst thesevariables.

For each variable, scale scores arecomputed as the average of the indi-vidual items. The profiles of the scoresfor predicted variables then serve asinputs for regression analysis. Severaltests for the assumptions of linearity,independence, and normality are car-ried out to ensure that the data could beused to apply a valid regression model.First, scatter plots of the individualindependent variables do not indicatenonlinear relationships between Infor-mation Sharing (IS), Decision Syn-

chronization (DS), and IncentiveAlignment (IA). Second, tests forheteroscedascity do not find any vari-able violating the assumption of con-stant variance. Finally, the tests ofnormality using the Kolmogorov D-Statistics, skewness, and kurtosis donot find any variable violating the as-sumption of normality. The predicatorvariables are found to be not corre-lated amongst themselves (IS and DSis .58, IS and IA is .32, and DS and IAis .47). The regression analysis is usedin this research to test the effect of thethree factors of collaborative practiceon performance. This procedure deter-

Table 1. Measurement Statistics for Predictor Variables

Rotated Component MatrixStd. Cronbachs

Scales of Collaborative Practice Factor 1 Factor 2 Factor 3 Mean Dev. Alpha

Decision Synchronization (DS)DS1: Reducing demand fluctuations .757 .014 .197DS3: Co-managing stock/inventory .638 .520 .205DS4: Ensuring on-time delivery .742 .278 .186 2.13 .86 .816DS5: Improving product availability .729 .101 .234IS3: Inventory cost data (deleted) .678 .678 .341IA3: Sharing collaborative cost (deleted) .670 .419 .149

Information Sharing (IS)IS1: Data about promotional events .463 .499 -.170IS2: Data about sales forecast .220 .590 .361 3.23 .89 .768IS4: On-hand inventory levels data .103 .846 .108IS5: Order tracking data .347 .767 .033DS2: Creating joint forecasts (deleted) .458 .492 .459

Incentive Alignment (IA)IA1: Sharing risks of uncertainty .299 -.247 .600IA2: Sharing saving from lowered stock .298 .067 .688 3.36 .77 .711IA4: Focusing on generating sales .067 .163 .781IA5: Sharing benefits of collaboration .037 .434 .709

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mines whether there is a significanteffect of the independent variables asspecified in the hypotheses (Cohenand Cohen 1983).

DiscussionThe main purpose of this study is

to provide a new insight into the con-ceptual link between the interface fac-tors of collaborative practice and op-erational performance. The hypoth-eses are tested through data analysis.Major findings are discussed as fol-lows.

All hypotheses stated in the mod-elling section are tested by estimatingthe regression equation for each per-formance criterion. The estimationresults for the first three hypothesesare summarized in Table 2. The F-tests for the three regression equationsindicate the linear relationship betweenpredictor variables and realized per-formance with alphas less than 0.001.The regression equations vary in val-ues of adjusted coefficients of deter-mination. The first regression equa-tion accounts for about 60 percent of

the variation in fulfillment perfor-mance. The second regression equa-tion accounts for about 54 percent ofthe variation in inventory performance.The third regression equation accountsfor about 33 percent of the variation inresponsiveness performance. The re-sults substantiate the three hypotheses.All being equal, realized performanceis greater when information sharing,decision synchronization, and incen-tive alignment are higher.

Testing Hypothesis 1: Hypoth-esis 1 relates to the relationship be-tween information sharing and opera-tional performance. As expected, in-formation sharing significantly helpsthe chain members achieve better ful-fillment ( = 0.225), lowered inven-tory ( = 0.489), and higher respon-siveness ( = 0.199). This finding isconsistent with previous research onthe impact of information sharing onthe performance of supply chain(Fawcett et al. 2009; Fisher 1997).Through information sharing, the chainmembers are able to take into accountfactors affecting the future require-ments of fulfillment, inventory, and

Table 2. Standardized Beta Coefficients for Realized Operational Performance

Performance Criteria

Hypotheses Fulfillment Inventory Responsiveness

H1. Information Sharing (IS) .225 ** .489 *** .199 *H2. Decision Synchronization (DS) .496 *** .380 *** .423 ***H3. Incentive Alignment (IA) .226 *** -.077 .066Adjusted R square .602 .537 .329

Note: * p < .10, one-tailed test; ** p < .05, one-tailed test, *** p < .01, one-tailed test,- Not statistically significant.

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responsiveness. Shared informationcan be used to better coordinate orderand replenishments in reducing inven-tory costs and improving customerservice levels.

Testing Hypothesis 2: Hypoth-esis 2 states that decision synchroniza-tion influences better performance. Itis found that decision synchronizationsignificantly contributes to fulfillment(b = 0.496), inventory (b = 0.380), andresponsiveness performance (b =0.423). This finding is consistent withprevious research on the effect of col-laborative decision making on supplychain performance (Holweg et al. 2005;Stank et al. 2001). The chain membersare able to attain better view of plan-ning, monitoring, and coordination ofthe overall supply chain processes(Morash and Clinton 1998). They cansynchronize their decisions on prod-uct planning, ordering, and replenish-ment that enable them to fulfil currentand future demand with minimum in-ventory. Collaborative decision mak-ing also helps them carry out improve-ments such as shortening cycle timethat affects their ability to respondmore quickly to demand changes(Simchi-Levi et al. 2007).

Testing Hypothesis 3: Hypoth-esis 3 states that incentive alignmenthas a positive relationship to opera-tional performance. It is found thatincentive alignment contributes onlyto fulfillment performance ( = 0.226).There is no significant impact of in-centive alignment on lowered inven-tory and higher responsiveness. Thisfinding indicates that the chain mem-

bers consider fulfillment to be the ba-sic criterion to satisfy customer needsand a means of incentive alignment(Giunipero et al. 2001). This is be-cause fulfillment performance speci-fies clearly an agreement between twoparties where the supplier delivers or-ders by a predetermined due date in-cluding the payment arrangement. Forinstance, a large retailer in NewZealand often requires their suppliersto send orders within pre-specifieddelivery time windows. The supplierswill be charged for sending ordersearlier or tardier than this time win-dow. Furthermore, as both parties en-joy inventory and responsiveness fromcollaboration, the benefits of inven-tory and responsiveness serve as self-imposed incentives. In other words,incentive alignment is often embed-ded in this collaborative effort. Thismeans both parties would reap thebenefits of lowered inventory and fastresponsiveness if they are to cooperateclosely in information sharing and de-cision synchronization without devis-ing incentive alignment (Fisher 1997;Stank et al. 1999).

The three factors of collaborativepractice provide opportunities to im-prove supply chain performance. Thefindings suggest that information shar-ing, decision synchronization, and in-centive alignment are important deter-minants of operational performance. Ithas been found that information shar-ing and decision synchronization con-sistently contribute to fulfillment, in-ventory, and responsiveness perfor-mance. However, incentive alignment

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is found to only affect fulfillment per-formance. This is not unusual for thechain members that prioritize fulfill-ment as a key performance indicator ofcustomer service level (Croxton 2003).It appears that the chain membersshould put together information shar-ing, decision synchronization and in-centive alignment if they want to im-prove their fulfillment performance.Furthermore, information sharing anddecision synchronization are both im-portant to attaining better inventoryand responsiveness. This finding alsosuggests that the chain members shouldidentify alternative incentive schemesif they prioritize inventory reductionand better responsiveness as key per-formance indicators of the relation-ships (Corbett et al. 1999; Narayananand Raman 2004).

Further research should firstlyconcentrate on developing alternativemeasures of operational performance.This study employs operational mea-sures of performance. An importantarea for future research is to includefinancial measures of performancebecause it would be useful for theparticipating members to confirm therelationship between collaborativepractice and financial performance(Corbett et al. 1999; Wisner 2003).Next, this research can be extended toexamine the antecedents of collabora-tive practice such as interdependence,top management support, trust, com-mitment, power disparity, and organi-zational capability (Mentzer et al. 2000;Mohr and Spekman 1994; Sheu et al.2006). Third, the focus of this research

is on the retailer-supplier link. Theconceptual model can be modified toexamine other links along the supplychain such as the manufacturer-dis-tributor, the manufacturer-logistics,and the retailer-logistic service pro-viders. Finally, there is also an oppor-tunity to study the complicated inter-action of antecedents, collaborativepractice, and performance using struc-tural equation model with a largersample size (Wisner 2003).

ConclusionsResearch questions addressed in

this study is the strength of relation-ship between collaborative practice andoperational performance. A concep-tual model and an empirical study areundertaken to answer this researchquestion.

The study seeks to make a contri-bution to the theory and practice ofsupply chain collaboration. The firstcontribution is the demonstration thatsupply chain collaboration can be mea-sured using three factors: (1) decisionsynchronization, (2) information shar-ing, and (3) incentive alignment. In-formation sharing enables the chainmembers to realize that it is importantto take into account a global perspec-tive in making optimal decisions. De-cision synchronization enables thechain members to agree upon jointdecisions, such as collaborative fore-casting, ordering, and delivery. Incen-tive alignment encourages the chainmembers to pursue mutual strategicobjectives that yield better profits to

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all members through sharing costs,benefits, and risks. The studys secondcontribution lies in its demonstrationof the empirical examination of theimpact of collaborative practice onoperational performance.

The study statistically describessome important findings on collabora-tive practice. Results show that infor-mation sharing, decision synchroniza-tion, and incentive alignment signifi-cantly contribute to fulfillment perfor-mance. Information sharing and deci-sion synchronization consistently af-fect fulfillment, inventory, and respon-siveness performance.

Although this research confirmsthat collaborative practice providesbenefits to participating members interms of improved operational perfor-mance, there are a number of opportu-nities for further research. Severalimportant ideas are to include finan-cial performance as a key successmeasure of collaboration and to usestructural equation model to capturemore interaction between key factorsof collaborative practice and perfor-mance. Moreover, it would be usefulto explore antecedent variables ofcollaborative practice and expand thestudy across other industries.

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managerial views of supply chain collaboration an empirical study

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