Int. J. Production Economics 128 (2010) 358–367

Contents lists available at ScienceDirect

Int. J. Production Economics

0925-52

doi:10.1

n Corr

E-m

qzhang@

journal homepage: www.elsevier.com/locate/ijpe

Supply chain collaborative advantage: A firm’s perspective

Mei Cao a, Qingyu Zhang b,n

a Department of Business and Economics, University of Wisconsin-Superior, WI 54880, USAb Department of Computer and Information Technology, Arkansas State University, State University, AR 72467, USA

a r t i c l e i n f o

Article history:

Received 3 October 2009

Accepted 21 July 2010Available online 3 August 2010

Keywords:

Collaborative advantage

Supply chain

Survey research

Structural equation modeling

73/$ - see front matter & 2010 Elsevier B.V. A

016/j.ijpe.2010.07.037

esponding author.

ail addresses: mcao1@uwsuper.edu (M. Cao),

astate.edu (Q. Zhang).

a b s t r a c t

In the past decades, firms have strived to achieve greater collaborative advantages with their supply

chain partners. The objective of the study is to uncover the nature and characteristics of supply chain

collaborative advantage from a focal firm’s perspective. Collaborative advantage is defined as strategic

benefits gained over competitors in the marketplace through supply chain partnering and partner

enabled knowledge creation, and it relates to the desired synergistic outcome of collaborative activity

that could not have been achieved by any firm acting alone. The research conceptualizes supply chain

collaborative advantage as the five dimensions: process efficiency, offering flexibility, business synergy,

quality, and innovation. Data were collected through a web survey of U.S. manufacturing firms in

various industries. Reliable and valid instruments were developed through rigorous empirical analysis

including structured interviews, Q-sort, and a large-scale study. Predictive validity is evaluated by

demonstrating a strong and positive relationship between supply chain collaborative advantage and

firm performance. The construct and measures in this study have provided a rich and structured

understanding of collaborative advantage in a supply network. The results of the structural analysis

indicate that supply chain collaborative advantage indeed has a bottom-line influence on firm

performance.

& 2010 Elsevier B.V. All rights reserved.

1. Introduction

In the past decades, firms have strived to achieve greatercollaborative advantages with their supply chain partners (Faw-cett and Magnan, 2004; Lejeune and Yakova, 2005). Supply chaincollaboration means two or more autonomous firms are workingjointly to plan and execute supply chain operations (Simatupangand Sridharan, 2002). It can deliver substantial benefits andadvantages to its partners (Mentzer et al., 2000). Collaborativerelationships can help firms share risks (Kogut, 1988), accesscomplementary resources (Park et al., 2004), reduce transactioncosts and enhance productivity (Kalwani and Narayandas, 1995),and enhance profit performance and competitive advantage overtime (Mentzer et al., 2000).

Despite the popularity and benefits of interorganizationalcollaboration, many partner relationships fall short of meeting theparticipants’ expectations (Doz and Hamel, 1998; Barringer andHarrison, 2000). Few firms have truly capitalized on the potentialof interorganizational collaboration (Barratt, 2003; Min et al.,2005). Collaboration seems to have great potential, but furtherinvestigation is needed to recognize its value (Goffin et al., 2006).

ll rights reserved.

Although the collaborative advantage is acknowledged in theliterature, its exact nature and attributes are not well compre-hended. In addition, the research studies on the collaborativeadvantage in the context of supply chain are still sparse.Collaboration between supply chain partners is not merely puretransactions but leverages information sharing and marketknowledge creation for sustainable competitive advantage (Mal-hotra et al., 2005; Vachon and Klassen, 2008; Bailey and Francis,2008; Ding and Huang, 2010). For example, changing marketdemand information can be integrated into the production plansvia supply chain collaboration for automotive manufacturers(Tomino et al., 2009).

The objective of the study is to uncover the nature andcharacteristics of supply chain collaborative advantage. The studycontributes to the body of knowledge by providing theoreticalinsights and empirical findings. By pooling an extensive setof factors, the research extends our understanding of the natureand attributes of supply chain collaborative advantage asfive dimensions of process efficiency, offering flexibility, businesssynergy, quality, and innovation. Through a large-scaleweb survey with manufacturers across the US, the researchalso intends to develop reliable and valid instrumentsand demonstrates that these elements form a secondorder construct. In addition, predictive validity is examined bytesting the impact of supply chain collaboration on firmperformance.

M. Cao, Q. Zhang / Int. J. Production Economics 128 (2010) 358–367 359

2. Conceptual development

Supply chain collaborative advantage is based on a paradigmof collaborative advantage (Kanter, 1994; Dyer, 2000) rather thancompetitive advantage (Porter, 1985). According to the collabora-tive paradigm, a supply chain is composed of a sequence ornetwork of interdependent relationships fostered through strate-gic alliances and collaboration (Chen and Paulraj, 2004). It is arelational view of interorganizational competitive advantage(Dyer and Singh, 1998).

By collaborating, supply chain partners can work as if theywere a part of a single enterprise (Lambert et al., 2004). Suchcollaboration can increase joint competitive advantage, i.e.,collaborative advantage (Jap, 2001). Supply chain collaborativeadvantage refers to strategic benefits gained over competitors inthe marketplace through supply chain partnering and partnerenabled knowledge creation, and such synergistic benefits couldnot be attained by acting independently (Jap, 2001; Vangen andHuxham, 2003; Malhotra et al., 2005). Supply chain partneringinvolves collaborative activities such as sharing information,synchronizing decisions, sharing complementary resources, andaligning incentives with partners’ costs and risks.

Collaborative advantage resides not within an individual firm butacross the boundaries of a firm via its relationship with supply chainpartners (Dyer, 1996; Dyer and Singh, 1998; Kanter, 1994; Jap,2001). Ferratt et al. (1996) define collaborative advantage as thebenefit gained by a group of firms as the result of their cooperationrather than their competition. They argue that, in healthcareindustry, IT enables firms to achieve competitive advantage throughcollaboration not only with supply chain partners but also withcompetitors (Pouloudi, 1999; Naesens et al., 2009).

Collaborative advantage relates to the desired synergisticoutcome of collaborative activity that could not have beenachieved by any firm acting alone (Vangen and Huxham, 2003).Jap (1999) explains that collaboration can enlarge the size of thejoint benefits and give each member a share of greater gain thatcould not be generated by each member on its own. Kanter (1994)argues that supply chain partnering, as the strongest and closestcollaboration, is a living system that grows progressively in theirpossibilities. Collaboration involves creating new values togetherrather than mere exchange, and it is controlled not by formalsystems but a web of connections and infrastructures thatenhance learning and open new doors and unforeseenopportunities. Thus, collaboration-associated benefits may notbe immediately visible; however potential long-term rewards areenticing and strategic (Min et al., 2005).

Table 1Definition of supply chain collaborative advantage and sub-dimensions.

Construct Definition

Supply chaincollaborativeadvantage

Strategic benefits gained over competitors in the marketplace

through supply chain partnering

Process efficiency The extent to which a firm’s collaboration with supply chain

partners is cost competitive

Offering flexibility The extent to which a firm’s supply chain linkage supports

changes in products or services available for customers

Business synergy The extent to which supply chain partners combine

complementary and related resources to achieve spill-over

benefits

Quality The extent to which a firm with supply chain partners offers

reliable and durable products that create higher value for

customers

Innovation The extent to which a firm works jointly with its supply chain

partners in introducing new processes, products, or services

Hansen and Nohria (2004) argue it is increasingly difficult tosustain competitive advantage on the basis of the economics ofscale and scope. Competitive advantage will belong to firms thatcan stimulate and support collaboration to leverage dispersedresources. They contend that the value creation from collabora-tion could be cost savings through the transfer of best practices,enhanced capacity and flexibility for collective actions, betterdecision making and increased revenue through resource synergy,and innovation through the combination and cross-pollination ofideas. Similarly, Lado et al. (1997) and Luo et al. (2006) suggestthat collaboration produces various benefits including costsavings, resource sharing, learning, and innovation.

Synthesizing the above studies, this research conceptualizessupply chain collaborative advantage as the following five sub-dimensions: process efficiency, offering flexibility, business synergy,quality, and innovation. These collaborative advantages are viewedfrom the focal firm’s perspective (Duffy and Fearne, 2004) (Table 1).

Process efficiency refers to the extent to which a firm’scollaboration process with supply chain partners is cost competi-tive among primary competitors (Bagchi and Skjoett-Larsen, 2005).The process could be information sharing process, joint logisticsprocess, joint product development process, or joint decisionmaking process. Process efficiency is a measure of success and adeterminant factor of the ability of the firm to make profit(e.g., inventory turnover and operating cost). Process efficiency isimproved through lower inventory and better delivery perfor-mance (Vachon and Klassen, 2008; Cachon and Fisher, 2000; Leeet al., 2000). Supply chain collaboration facilitates the cooperationof participating members along the supply chain to improveperformance (Bowersox, 1990). The benefits of collaborationinclude cost reductions and revenue enhancements (Lee et al.,1997; Giannoccaro and Pontrandolfo, 2009).

Offering flexibility refers to the extent to which a firm’s supplychain linkage supports changes in product or service offerings(e.g., features, volume, and speed) in response to environmentalchanges. It is also called customer responsiveness in literature(Kiefer and Novack, 1999; Holweg et al., 2005). Offering flexibilityis based on the ability of collaborating firms to quickly changeprocess structures or adapt the information sharing process formodifying the features of a product or service (Gosain et al.,2004). In today’s market firms indeed pay attention to customersand more firms solicit customer inputs at the design stageresulting in better acceptance of the products and services later(Bagchi and Skjoett-Larsen, 2005).

Business synergy refers to the extent to which supply chainpartners combine complementary and related resources to

Literature

Jap, 2001; Dyer, 1996; Dyer and Singh, 1998; Ferratt et al., 1996; Kanter, 1994;

Vangen and Huxham, 2003

Bagchi and Skjoett-Larsen, 2005; Fisher, 1997; Lee et al., 1997; Simatupang and

Sridharan, 2005

Beamon, 1998; Gosain, et al., 2004; Holweg et al., 2005; Kiefer and Novack,

1999; Narasimhan and Jayaram, 1998

Ansoff, 1988; Itami and Roehl, 1987; Larsson and Finkelstein, 1999; Lasker et al.,

2001; Tanriverdi, 2006; Zhu, 2004

Harvey and Gray, 1992; Li, 2002; Rondeau et al., 2000; Fynesa et al., 2005

Clark and Fujimoto, 1991; Dyer and Singh, 1998; Handfield and Pannesi, 1995;

Kessler and Chakrabarti, 1996; Malhotra et al., 2001; Mowery and Rosenberg,

1998; Vesey, 1991

M. Cao, Q. Zhang / Int. J. Production Economics 128 (2010) 358–367360

achieve spill-over benefits. Ansoff (1988) suggests that synergycan produce a combined return on resources that is greater thanthe sum of individual parts (2+2¼5). This joint effect results fromthe process of making better use of resources in the supply chain,including physical assets such as manufacturing facilities andinvisible assets such as customer knowledge, technologicalexpertise, and organizational culture (Itami and Roehl, 1987).Tanriverdi (2006) offers two major sources of synergy: super-additive value by complementary resources and sub-additive cost(or economies of scope) by related resources. Collaboration canhelp partners to maximize their assets utilization (e.g., truckloadtransportation and transportation capacity sharing) resulting insubstantial capital relief (Min et al., 2005).

Lasker et al. (2001) claim that synergies between supply chainpartners are more than a mere exchange of resources. Bycombining the individual firms’ resources, skills, and socialcapital, the collaboration can create something new and valuabletogether. Supply chain partners can also achieve synergy ofcommon IT infrastructure, common IT management processes,and common IT vendor management processes (Larsson andFinkelstein, 1999; Zhu, 2004; Tanriverdi, 2006). As long as supplychain partners make decisions in the best economic interest of thesupply chain as a whole, not its own portion, the gain or jointoutcome will be expanded (Simatupang and Sridharan, 2005).

Quality refers to the extent to which a firm with supply chainpartners offers quality product that creates higher value forcustomers (Harvey and Gray, 1992; Rondeau et al., 2000; Li, 2002;Fynesa et al., 2005). It is expected that firms that can respond fast tocustomer needs with high quality products, innovative design, andexcellent after-sales service allegedly build customer loyalty,increase market share, and ultimately gain high profits. Garvin(1988) proposes eight dimensions of quality: performance, features,reliability, conformance, durability, serviceability, aesthetics, andperceived quality, which are comprehensive but measures for eachare difficult to establish. Quality improvement can be achievedthrough supply chain collaboration (Fynesa et al., 2005).

Innovation refers to the extent to which a firm works jointlywith its supply chain partners in introducing new processes,products, or services. Due to shorter product life cycles, firms needto innovate frequently and in small increments (Clark andFujimoto, 1991; Vesey, 1991; Handfield and Pannesi, 1995; Kesslerand Chakrabarti, 1996). By carefully managing their relationshipswith suppliers and customers, firms improve their ability to engagein process and product innovation (Hage, 1999; Kaufman et al.,2000). Innovation as a highly structured, knowledge-intensiveactivity embeds in networks that span organizational andgeographical boundaries (Dyer and Singh, 1998; Mowery andRosenberg, 1998; Sapolsky et al., 1999; Malhotra et al., 2001). Bytapping joint creativity capacities, joint organizational learning,knowledge sharing, and joint problem solving between supplychain partners, firms can improve absorptive capacity and thusintroduce new products and services fast and frequently.

3. Instrument development

The development of instruments for supply chain collaborativeadvantage was carried out in three steps: (1) item generation,(2) structured interview and Q-sort, and (3) large-scale analysis.First, to ensure the content validity of the constructs, an extensiveliterature review was conducted to define each construct andgenerate the initial items for measuring the constructs. Then,a structured interview and Q-sort were conducted to providea preliminary assessment of the reliability and validity of thescales. The third step was a large-scale survey to validate theinstruments.

3.1. Item generation

The objective of item generation is to achieve the content validityof constructs by reviewing literature and consulting with academicand industrial experts. The measurement items for a scale shouldcover the content domain of a construct (Churchill, 1979; Moore andBenbasat, 1991; Segars and Grover, 1998). To generate measure-ment items for each construct, prior research was extensivelyreviewed and an initial list of potential items was compiled. A five-point Likert scale was used to indicate the extent to which managersagree or disagree with each statement where 1¼strongly disagree,2¼disagree, 3¼neutral, 4¼agree, and 5¼strongly agree. The itemsmeasure collaborative advantages between supply chain partnersfrom a focal firm’s perspective. In the questionnaire, supply chainpartners are defined as a firm’s primary/key suppliers.

3.2. Structured interview and Q-sort

After the measurement items were created, the common poolof items was reviewed and evaluated by practitioners from fourdifferent manufacturing firms to pre-assess the reliability andvalidity of the scales. First, structured interviews were conductedto check the relevance and clarity of each sub-construct’sdefinition and the wording of question items. Then, intervieweeswere asked to sort out the questionnaire items into correspondingsub-constructs. Based on the feedback from the experts, redun-dant and ambiguous items were eliminated or modified.

Three Q-sort measures were used to evaluate the instruments.Inter-judge raw agreement score is the number of items that bothjudges agree to place into a certain category divided by the totalnumber of items. Second, item placement ratio (i.e., hit ratio) isthe items that are correctly sorted into the intended theoreticalcategory divided by twice the total number of items. Third,Cohen’s kappa is the proportion of joint judgments where there isagreement after chance agreement is excluded. After two Q-sortrounds, two items were dropped and some items were reworded.Then the questionnaire items were distributed to six academi-cians who reviewed each item and indicated to keep, drop,modify, or add items. Finally, questionnaire items were sent outfor a large-scale survey. Detailed Q-sort results are available fromthe authors.

3.3. Sampling design and large-scale data collection

To further purify the items and assess measurement properties, alarge-scale web survey was conducted. The sample respondents wereexpected to have knowledge or experience in supply chain manage-ment. The target respondents were CEOs, presidents, vice presidents,directors, or managers in the manufacturing firms across the U.S. Thesample respondents were expected to cover the following seven SICcodes: Furniture and Fixtures (SIC 25), Rubber and Plastic Products(SIC 30), Fabricated Metal Products (SIC 34), Industrial Machinery andEquipment (SIC 35), Electric and Electronic Equipment (SIC 36),Transportation Equipment (SIC 37), and Instruments and RelatedProducts (SIC 38).

An email list of 5000 target respondents were purchased fromCouncil of Supply Chain Management Professionals (CSCMP), aprestigious association of professionals in the area of supply chainmanagement, and lead411.com, a professional list company that isspecialized at providing executive level email lists. A web surveywas conducted to reach as many respondents as possible andretrieve as much information as possible in short time. Excludingmultiple names from the same organization, undelivered emails,and returned emails saying that target respondents were no longerwith the company, the actual mailing list contained 3538 names.

M. Cao, Q. Zhang / Int. J. Production Economics 128 (2010) 358–367 361

To improve the response rate, three waves of emails were sentonce a week. Out of the 227 responses received (16 incomplete),211 are usable resulting in a response rate of 6.0%. Characteristics ofthe respondents appear in Table 2. A chi-square test is conducted tocheck non-response bias (see Table 2). The results show that there isno significant difference between the first-wave and second/third-wave respondents by all three categories (i.e., SIC code, firm size, andjob title) at the level of 0.1. It exhibits that received questionnairesfrom respondents represent an unbiased sample.

3.4. Large-scale data analysis methods

Using confirmatory factor analysis with LISREL, steps wereundertaken to check (1) unidimensionality and convergent validity,(2) reliability, (3) discriminant validity, (4) second-order constructvalidity, and (5) predictive validity. The assessment was conductedfor all five constructs in one first-order correlated model so that therelated multi-items measures are grouped together (Papke-Shieldset al., 2002). Iterative modifications were undertaken by droppingitems with loadings less than 0.7 and also items with highcorrelated errors, thus improving the model fit to acceptable levels(Hair et al., 1995). In all cases where refinement was indicated,items were deleted if such action was theoretically sound and thedeletions were done one by one at each step (Hair et al., 1995).Model modifications were continued until all parameter estimatesand model fits were judged to be satisfactory.

Unidimensionality is assessed by the fit indices and convergentvalidity is assessed by the significance of t-values of eachmeasurement indicator. The overall model fit can be tested usingthe comparative fit index (CFI), non-normed fit index (NNFI), rootmean square error of approximation (RMSEA), and normed chi-square (i.e., w2 per degree of freedom) (Byrne, 1989; Bentler, 1990;Hair et al., 1995; Chau, 1997; Heck, 1998). Values of CFI and NNFIbetween 0.80 and 0.89 represent a reasonable fit (Segars andGrover, 1998) and scores of 0.90 or higher are evidence of good fit(Byrne, 1989; Joreskog and Sorbom, 1989; Papke-Shields et al.,2002). Values of RMSEA less than 0.08 are acceptable (Hair et al.,1995; Joreskog and Sorbom, 1989). The normed chi-square(w2 divided by degrees of freedom) estimates the relative

Table 2Demographic data for the respondents (211 responses).

Variables Total responses First-wave frequency

SIC25 8 4

30 9 4

34 40 25

35 30 23

36 67 43

37 29 21

38 23 15

Others 5 4

Firm size1–50 10 7

51–100 16 12

101–250 38 27

251–500 58 34

501–1000 14 8

1001+ 75 51

Job titleCEO/President 54 36

Vice president 99 62

Manager 27 20

Director 23 17

Others 8 4

efficiency of competing models. For this statistic, a value lessthan 3.0 indicates a reasonable fit and a value less than 2.0 showsa good fit (Segars and Grover, 1998; Papke-Shields et al., 2002).

Following Hair et al. (1995), the composite reliability (rc) andthe average variance extracted (AVE) of multiple indicators of aconstruct can be used to assess reliability of a construct. WhenAVE is greater than 50% and rc is greater than 0.70, it implies thatthe variance by the trait is more than that by error components(Hair et al., 1995).

Discriminant validity can be assessed by three methods. Onemethod is to perform a pair-wise comparison using a model withcorrelation constrained to 1 with an unconstrained model. Adifference between the w2 values of the two models, which issignificant at po0.05 level, would indicate support for discrimi-nant validity (Joreskog and Sorbom, 1989). The second methodentails comparing the average variance extracted (AVE) to thesquared correlation between constructs. If the AVE for each factoris greater than the squared correlation of this factor and another,there is evidence of discriminant validity (Fornell and Larcker,1981). A more rigorous method involves constructing 95%confidence intervals for each pair of constructs using formulaf72se where f is the correlation between constructs and se isits associated standard error in an all-factor correlated model (seeFig. 1a). If the value 1.0 is not included in the interval,discriminant validity is achieved (Anderson and Gerbing, 1988).

An important aspect of construct validity is the validation of thesecond-order construct. The construct was validated by a second-order model with good model fit indices as discusses before (Jianget al., 2000; Stewart and Segars, 2002). Furthermore, a T-coefficientwas used to test whether a second-order construct exists accountingfor the variations in its sub-constructs. T-coefficient is calculated asthe ratio of the chi-square of the first-order model to the chi-squareof the second-order model and a T-coefficient of higher than 0.80indicates the existence of a second-order construct (Marsh andHocevar, 1985; Doll et al., 1995; Stewart and Segars, 2002).

Predictive validity is the extent to which a scale predicts scoreson criterion measures. It can be assessed by evaluating if thevariable has significant relationships with other theory-drivenoutcome variables. Thus, predictive validity is tested by examin-ing structural relationships.

Second/third wave frequency Chi-square test

4 w2¼10.00

5 df¼7

15 p¼0.17

7

24

8

8

1

3 w2¼4.71

4 df¼5

11 p¼0.45

24

6

24

18 w2¼4.73

37 df¼4

7 p¼0.32

6

4

Fig. 1. Measurement model of collaborative advantage. (a) Correlated first-order model chi-square¼344.11, df¼160, normed chi-square¼2.15, CFI¼0.94, NNFI¼0.93,

RMSEA¼0.074. (b) Second-order model chi-square¼384.38, df¼165, normed chi-square¼2.33, CFI¼0.93, NNFI¼0.92, RMSEA¼0.080.

M. Cao, Q. Zhang / Int. J. Production Economics 128 (2010) 358–367362

4. Results

In the following section, the results of large-scale analysis foreach construct will be reported and discussed.

4.1. Large-scale measurement results

The construct of supply chain collaborative advantage wasrepresented by five dimensions and 20 items (see the Appendix).An all-factor correlated LISREL measurement model (see Fig. 1a)was specified for supply chain collaborative advantage. FollowingHair et al. (1995), iterative modifications were made by examin-ing modification indices, correlated errors, and loadings toimprove key model fit statistics.

The final model fit indices of CFI, NNFI, RMSEA, and normed w2

are 0.94, 0.93, 0.074, and 2.15, respectively, meeting therecommended criteria and demonstrating good unidimensional-ity. Shown in Table 3, the item loadings for each factor are greaterthan 0.70 and significant at po0.01 based on t-values. Alldimensions exhibit good convergent validity.

The estimates of AVEs for the five factors are 0.67, 0.77, 0.74,0.74, and 0.71, respectively, greater than the critical value of 0.50.The composite reliabilities (rc’s) for the five factors are 0.89, 0.93,0.92, 0.92 and 0.91, respectively, above the critical value of 0.70.The results of the AVEs and rc’s provide evidence of goodreliability for each factor.

Table 4 reports the results of the three methods performed toassess discriminant validity. 10 pair-wise w2 differences between

constrained and unconstrained models for the 5 dimensions of CAare all significant at po0.01. Likewise, the squared correlationsfor each pair of dimensions are less than the AVE for eachdimension. None of the confidence intervals for each bivariatecorrelation of dimensions include the value 1.0. The resultssupport the case for discriminant validity.

4.2. Validation of second-order constructs

Supply chain collaborative advantage as a second-order constructwas run with five sub-dimensions as the first order factors (seeFig. 1b). All the factor loadings and the error terms of each indicatorare shown in Fig. 1b. The model fit indices of CFI, NNFI, RMSEA, andnormed w2 are 0.93, 0.92, 0.080, and 2.33, respectively, meeting therecommended criteria and demonstrating good model fit.

The second-order model explains the co-variations amongfirst-order factors in a more parsimonious way. However, thevariations shared by the first-order factors cannot be totallyexplained by the single second-order factor, and thus thefit indices of the higher-order model can never be better thanthe corresponding first-order model (Segars and Grover, 1998).The first-order model provides a target fit for higher-ordermodels. The efficacy of second-order models can be assessed byexamining the target (T) coefficient (where T¼first-order w2/second-order w2) (Marsh and Hocevar, 1985; Doll et al., 1995;Stewart and Segars, 2002). The T-coefficient 0.80–1.00 indicatesthe existence of a second-order construct.

M. Cao, Q. Zhang / Int. J. Production Economics 128 (2010) 358–367 363

Table 5 shows the calculated target coefficient between the first-order model and the second-order model for supply chaincollaborative advantage. The T-coefficient is 0.925, suggesting thatthe second-order models should be accepted as a more accuraterepresentation of model structure over the corresponding first-ordermodel because they represent more parsimonious explanation ofobserved covariance. The results support the second-orderconstructs proposed in the conceptual development section.

4.3. Predictive validity

Many scholars contend that both customers and suppliers seekcollaborative relationships as a way of improving performance(Ittner and Larcker, 1997; Duffy and Fearne, 2004; Sheu et al.,2006). Stank et al. (2001) suggest that both internal and externalcollaborations are necessary to ensure performance. Partnerships

Table 3Factor loadings (t-statistics), AVE, and reliability (rc) for CA sub-constructs.

Items Process efficiency Offering flexibility

CAPE1 0.83 (–)

CAPE2 0.78 (12.81)

CAPE3 0.84 (14.33)

CAPE4 0.82 (13.78)

CAOF1 0.93 (–)

CAOF2 0.89 (21.38)

CAOF3 0.87 (19.67)

CAOF4 0.81 (16.76)

CABS1

CABS2

CABS3

CABS4

CAQL1

CAQL2

CAQL3

CAQL4

CAIN1

CAIN2

CAIN3

CAIN4

AVE 0.67 0.77

qc (Reliability) 0.89 0.93

Table 4Discriminant validity of supply chain collaborative advantage sub-constructs.

Sub-Construct CAPE CAOF

CAPE 0.67a 0.58e

CAOF 0.76b

[0.59, 0.93]c 0.7711.24 dnn

CABS 0.73 0.51

[0.63, 0.83] [0.35, 0.67]

8.32nn 49.22 nn

CAQL 0.72 0.75

[0.51, 0.91] [0.57, 0.93]

17.04nn 14.78nn

CAIN 0.71 0.71

[0.51, 0.91] [0.55, 0.87]

10.90nn 10.53nn

a Values on the diagonal are the average variance extracted for each sub-constructb Values represent correlations (f) for each pair of CA sub-constructs.c The 95% confidence interval for correlations of each pair of CA sub-constructs.d w2 differences between each constrained model and unconstrained model.e Values above the diagonal are squared correlations for each pair of CA sub-constnn significant at po0.01.

can improve profitability, reduce purchasing costs, and increasetechnical cooperation (Ailawadi et al., 1999; Han et al., 1993).

This study uses firm performance to evaluate the predictivevalidity of supply chain collaborative advantage. If supply chaincollaborative advantage as a second-order construct has signifi-cant relationship with firm performance, there is evidence ofpredictive validity. Firm performance refers to how well a firmfulfills its financial and competitive goals compared with thefirm’s primary competitors (Yamin et al., 1999). In this study, firmperformance is measured by growth of sales, return on invest-ment, growth in return on investment, and profit margin on sales.Test results of unidimensionality, convergent validity, andreliability for firm performance are provided in Table 6. Asshown in Fig. 2, the path coefficient (g¼0.72, t¼9.62) shows thatsupply chain collaborative advantage as a second-order constructis significantly related to firm performance, providing evidence ofpredictive validity.

Business synergy Quality Innovation

0.86 (–)

0.86 (15.88)

0.84 (15.26)

0.87 (16.33)

0.92 (–)

0.89 (19.80)

0.88 (19.44)

0.74 (13.72)

0.87 (–)

0.85 (15.98)

0.83 (15.28)

0.83 (15.38)

0.74 0.74 0.71

0.92 0.92 0.91

CABS CAQL CAIN

0.53 0.52 0.50

0.26 0.56 0.50

0.74 0.24 0.41

0.49

[0.32, 0.66] 0.74 0.50

22.40nn

0.64 0.71

[0.48, 0.80] [0.56, 0.86] 0.719.43nn 14.18nn

.

ructs.

Table 6Confirmatory factor analysis results for firm performance.

Construct Measurement model fit Loadings (t-statistics) AVE qc (reliability)

Firm performance w2¼0.40, df¼2, p¼0.82 FP3: 0.80 (–) 0.74 0.92

CFI¼0.99 FP4: 0.95 (16.16)

NNFI¼0.98 FP5: 0.87 (14.55)

RMSEA¼0.000 FP6: 0.81 (13.26)

Normed w2¼0.20

χ2= 69.19; df =26; Normed χ2=2.66; RMSEA = 0.089; CFI=0.96; NNFI=0.95.

γγγγ =0.72 (t =9.73) Supply Chain Collaborative

Advantage

CAPE CAOF CABS CAQL CAIN FP1 FP2 FP3 FP4

Firm Performance

Fig. 2. Structural model of evaluating the predictive validity of supply chain collaborative advantage.

Table 5Fit indices for first- and second-order models.

Construct Model w2 (df) Normed w2 CFI NNFI RMSEA T-coefficient

Supply chain collaborative advantage First-order 344.11 (160) 2.15 0.94 0.93 0.074 92.50%

Second-order 384.38 (165) 2.33 0.93 0.92 0.080

M. Cao, Q. Zhang / Int. J. Production Economics 128 (2010) 358–367364

5. Discussion and implications

The research has provided a more accurate definition of supplychain collaborative advantage. The definition contains fourcomponents: (1) collaborative advantages are achieved by supplychain partnering activities such as sharing information, synchro-nizing decisions, sharing complementary resources, and aligningincentives with partners’ costs and risks; (2) benefits are enlargedthan acting independently; (3) there are some leverage effects orsynergistic outcomes; and (4) it is not just collaborative transac-tions but involves joint knowledge creation and joint innovation.This definition puts more emphasis on business synergy and jointvalue creation and innovation. This emphasis enables firms to seethe advantages from a long-term and strategic perspective,shifting from the only cost and short-term focus.

The current study has identified five dimensions that make upsupply chain collaborative advantage: process efficiency, offeringflexibility, business synergy, quality, and innovation. Collabora-tive advantage will be realized when all parties in the supplychain from suppliers to customers cooperate. Collaborativeadvantage is absent when supply chain members purse theirown objectives (Chopra and Meindl, 2007; Abreu et al., 2009). Ifeach member views its action locally and is unable to see theimpact of its action on other members, the chain as a wholesuffers because the total benefit is diminished.

The study has developed valid and reliable instruments forsupply chain collaborative advantage. All the scales have been testedthrough rigorous statistical methodologies including Q-sort method,confirmatory factor analysis, reliability, and the validation of second-order construct. All the scales are shown to meet the requirements

for reliability and validity and thus can be used in future research.The accurate definitions and measures of collaborative advantagehave provided a rich and structured understanding of what occurs ina supply chain or network. They also facilitate empirical researchefforts because the relationships among constructs can be bettercaptured with better definitions and measures.

To the author’s best knowledge, the study represents the firstof its kind in the supply chain literature to define andoperationalize collaborative advantage. The results stronglysuggest that better collaboration among supply chain partnersexpands the gain pie due to synergy through complementaryresources and collaborative processes (Jap, 2001; Tanriverdi,2006; Simatupang and Sridharan, 2005).

The results empirically confirm that supply chain collaborativeadvantage directly improves firm performance. Previous researchlinks collaboration directly to firm performance (Duffy andFearne, 2004; Stank et al., 2001; Tan et al., 1998) withoutexplicitly considering any intermediate variable such as colla-borative advantage. This is an important finding since there existsdoubt among researchers and practitioners in the economicjustification of whether supply chain collaborative advantagecan bring financial benefits to the focal firm. The statisticalsignificance of the structural relationship suggests that supplychain collaborative advantage indeed has a bottom-line influenceon firm performance.

In addition to the theoretical contributions of the study, thereare practical implications that can be inferred. The definition andmeasures of collaborative advantages can help managers to definespecific actions to be taken collaboratively to improve sharedsupply chain processes that benefit all members. The definition

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and measurements can serve as a powerful tool for managers toform effective collaborative relationships.

6. Limitations and future research

While the research has made significant contributions toresearch and practice, there are limitations that need tobe considered when interpreting the study findings. Because ofthe limited number of observations (211), the revalidationof constructs was not carried out in this research. This needsto be addressed in the future research. New data may be collectedto revalidate the measures and structural models.

Future research should also conduct factorial invariance tests.Using the instruments developed in this research, one may test forfactorial invariance across different organization sizes and acrossindustries. For example, an analysis of collaborative advantage byindustry would be very beneficial. Future research should apply

multiple methods to obtain data. The use of a single respondentto represent what are supposed to be supply chain widevariables may generate some inaccuracy and more than the usualamount of random error. Future research should seek toutilize multiple respondents from each participating organizationas an effort to enhance reliability of research findings. Moreinsights will be gained by collecting information from both sidesof the manufacturer–supplier dyad rather than just from oneorganization.

Some researchers examine supply chain collaborationpractices directed either upstream toward suppliers or down-stream toward customers (Vachon and Klassen, 2008; Nyagaet al., 2010). They found out some similarities and somedifferences between two directions. In the future research,it would be interesting to investigate whether the measure-ment structure of collaborative advantage is similar or differentbetween the upstream and downstream partners with thefocal firm.

Appendix. Instruments

These items measure your firm’s collaborative advantages with your supply chain partners (i.e., primary/key suppliers) and firmperformance using a 5-point Likert-type scale to indicate the extent to which you agree or disagree to each statement as applicable toyour firm: 1¼strongly disagree, 2¼disagree, 3¼neutral, 4¼agree, and 5¼strongly agree.

Supply Chain Collaborative Advantage

Process efficiency

CAPE1 Our firm with supply chain partners meets what are agreed upon unit costs in comparison with industry norms CAPE2 Our firm with supply chain partners meets productivity standards in comparison with industry norms CAPE3 Our firm with supply chain partners meets on-time delivery requirements in comparison with industry norms CAPE4 Our firm with supply chain partners meets inventory requirements (finished goods) in comparison with industry

norms

Offering flexibility

CAOF1 Our firm with supply chain partners offers a variety of products/services efficiently in comparison with industry

norms

CAOF2 Our firm with supply chain partners offers customized products/services with different features quickly in

comparison with industry norms

CAOF3 Our firm with supply chain partners meets different customer volume requirements efficiently in comparison

with industry norms

CAOF4 Our firm with supply chain partners has good customer responsiveness in comparison with industry norms

Business synergy

CABS1 Our firm and supply chain partners have integrated IT infrastructure and resources CABS2 Our firm and supply chain partners have integrated knowledge bases and know-how CABS3 Our firm and supply chain partners have integrated marketing efforts CABS4 Our firm and supply chain partners have integrated production systems

Quality

CAQL1 Our firm with supply chain partners offers products that are highly reliable CAQL2 Our firm with supply chain partners offers products that are highly durable CAQL3 Our firm with supply chain partners offers high quality products to our customers CAQL4 Our firm and supply chain partners have helped each other to improve product quality

Innovation

CAIN1 Our firm with supply chain partners introduces new products and services to market quickly CAIN2 Our firm with supply chain partners has rapid new product development CAIN3 Our firm with supply chain partners has time-to-market lower than industry average CAIN4 Our firm with supply chain partners innovates frequently

Firm performance

FP1 Growth of sales FP2 Return on investment FP3 Growth in return on investment FP4 Profit margin on sales

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