Industrial Management & Data SystemsImpact of supply chain linkages on supply chain performancePamela J. Zelbst Kenneth W. Green Jr Victor E. Sower Pedro Reyes

Article information:To cite this document:Pamela J. Zelbst Kenneth W. Green Jr Victor E. Sower Pedro Reyes, (2009),"Impact of supply chainlinkages on supply chain performance", Industrial Management & Data Systems, Vol. 109 Iss 5 pp. 665 -682Permanent link to this document:http://dx.doi.org/10.1108/02635570910957641

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Impact of supply chain linkageson supply chain performance

Pamela J. Zelbst, Kenneth W. Green Jr and Victor E. SowerDepartment of Management and Marketing,

College of Business Administration, Sam Houston State University,Huntsville, Texas, USA, and

Pedro ReyesDepartment of Management and Entrepreneurship,

HankamerSchool ofBusiness, Center of Excellence for Supply ChainManagement,Baylor University, Waco, Texas, USA

Abstract

Purpose – The purpose of this paper is to examine the impact of supply chain linkages on supplychain performance (SCP). It aims to define and describe linkage constructs for power, benefits, and riskreduction and develop multi-item scales for their measurement. It also aims to assess the relationshipsof the linkages with SCP.

Design/methodology/approach – A total of 145 manufacturing and services sector managers aresurveyed. The measurement scales are assessed for reliability and validity and further assessed withina measurement model context. Study hypotheses are then tested using a multiple regression approach.

Findings – Results for the combined sample indicate that power, benefits, and risk reductionlinkages positively and significantly impact SCP. Power is identified as the dominant linkage formanufacturers, and risk reduction as the most important within the services sector.

Practical implications – The key to effective supply chain management is the ability to establishlong-term, strategic relationships with supply chain partners. Practitioners should work to fullydevelop power, benefits, and risk reduction linkages with partners within their specific supply chainsin order to maximize value to the ultimate customers of the supply chain.

Originality/value – Through this study, previously discussed supply chain linkage constructs arespecifically defined, scales for measurement of the constructs are developed, and an initial assessmentof the relation of the constructs to SCP is provided.

Keywords Supply chain management, Business links, Risk management

Paper type Research paper

1. IntroductionResearchers have investigated supply chain performance (SCP) from many differentperspectives. Bichescu and Fry (2009) researched SCP in relation to decision making interms of power. Wang et al. (2009) investigated SCP in relation to product developmentstrategy focusing on efficiency but not necessarily the potential benefits sought bysupply chain members. Lau et al. (2008) looked at real-time supply chain controlin relation to risk reduction. Social exchange theory has been utilized in supply chainresearch to examine stability and alliance performance (Yang et al., 2008), coordinationof supply chains (Holweg and Pil, 2008), as well as competitive and cooperativepositioning in supply chain logistics relationships (Klein et al., 2007).

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

www.emeraldinsight.com/0263-5577.htm

Impact of supplychain linkages

665

Received 20 October 2008Revised 16 January 2009

Accepted 27 January 2009

Industrial Management & DataSystems

Vol. 109 No. 5, 2009pp. 665-682

q Emerald Group Publishing Limited0263-5577

DOI 10.1108/02635570910957641

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We extend the work related to SCP within the social exchange context by examiningthe exchange linkages within supply chain networks in terms of the relationshipscreated among supply chain partners. Previous research indicates that three variablesare important to these linkages and influence SCP. In particular, the supply chainlinkages will be examined in terms of power (Cook and Emerson, 1978; Emerson et al.,1983; Lawler, 1992; Maloni and Benton, 2000; Cox, 2001), risk (Cook and Whitmeyer,1992; Johannisson, 1987; Cannon et al. (2008) and benefits (Cook and Emerson, 1978;Burke, 1997; Willer, 1999; Cannon et al., 2008; Polo-Redondo and Cambra-Fierro, 2008;Im and Rai, 2008).

We propose a model in which power, benefits, and risk reduction linkages serve toenhance SCP. The model is assessed using data collected from a national sample ofmanufacturing and services sector managers. The measurement scales are fullyassessed for reliability and validity and further assessed within a measurement modelcontext. Study hypotheses are tested using a multiple regression approach for thecombined sample and also for the manufacturing and services sector sub-samples. Weextend the current literature by developing and assessing measurement scales forpower, risk reduction, and benefits linkages and providing an initial empiricalassessment of the relationships. The focus of this research is on the management ofexisting supply chain linkages and SCP.

A review of the supporting literature and development of the study hypothesesfollows in Section 2. We then provide a discussion of the methodology used and reportthe results of the scale assessment and regression analysis. Finally, a conclusionssection in which the results are discussed, limitations are delineated, future research issuggested, and implications for management practitioners is provided.

2. Literature review and hypothesesPast research tends to focus on the technological side of these linkages in terms of SCP. Forexample, the prominent technologies studied in terms of SCP include radio frequencyidentification (Lee et al., 2009; Bendavid et al., 2009; Ustundag and Tanyas, 2009),communication technology (Duchessi and Chengalur-Smith, 2008; Swafford et al., 2008),and enterprise resource planning systems (Forsiund and Jonsson, 2007; Zong, 2008). Whilethese are important streams of research there is a need for research investigatingthe non-technical side of exchange linkages in terms of supply chain relationships and theinfluence on SCP.

Bichescu and Fry (2009) examine questions related to when split decisions result inthe loss of SCP and the effect of power on performance loss. This research uses decisionmaking structure as a proxy for the relative division of channel power to determine theeffect on SCP. Our research takes a more direct approach and examines power asinfluence over other supply chain members in relation to SCP. Wang et al. (2009)developed an evaluation approach to measure SCP focusing on efficiency of variousproduct strategies made through group decisions. Our research focuses on theeffectiveness aspect in relation to the benefits sought in acquiring resources fromsupply chain members and the standardization of procedures for supply chainmembers. Lau et al. (2008) studied supply chain control in relation to adjustableautonomy which is the ability to reduce risk through reacting to changes and havingdynamic collaboration. These authors identify that the weakness of their approach is

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the need to have experts in the area provide accurate information. Our researchexamines risk reduction in terms of supply chain members.

Social exchange has been utilized in supply chain research to examine stability andalliance performance (Yang et al., 2008), coordination of supply chains (Holweg and Pil,2008) as well as competitive and cooperative positioning in supply chain logisticsrelationships (Klein et al., 2007). Yang et al. (2008) examine the stability between supplychain members in relation to alliance performance. They focused on benefits and trust.For the supply chain to form, members must seek some benefit or reduction in risk thatthe other supply chain members can provide. While trust is important it is not the focusof this research. Trust is not initially in place and is an end result in the supply chaincontext. These authors find that trust plays a role later in maintaining the supply chainrelationships. Our research is focusing on the benefits and risk reduction that is beingsought as well as the power that supply chain members use to influence each other(Bichescu and Fry, 2009). Klein et al. (2007) address the issue of need fulfillment thatcreates the relationship. Further, they state that this relationship is a mixture ofcooperation and competitiveness that will lead to performance gains. Theserelationships are exchange linkages between organizations that are supply chainmembers. A discussion of social exchange theory and its particular application to themodel as well as discussion of each of the model components (power, benefits, riskreduction, and SCP) and support for the study hypotheses follow in this section.

2.1 Social exchangeExchange theory is the conceptualization of interaction, structure, and order (Cook andWhitmeyer, 1992). In terms of exchange relations, exchange theory has a long historyin anthropology and more recently has been adopted by some sociologists (Cook andWhitmeyer, 1992). Markosky et al. (1993, p. 197) state that, “exchange theory wasdeveloped to predict negotiated distribution of resources in a class of networksconsisting of interrelated individual(s) or corporate actors”. As such, exchange theoryshould be relevant to supply chain management (SCM) since a supply chain is bydefinition an interrelated network of suppliers and customers.

Exchange theory then will be used to examine structures created as a result ofactivities, such as supply chain driven activities (Willer, 1999). The resulting structuresare effectiveness seeking (horizontal structure) and efficiency seeking (verticalstructure) depending upon the phase of development (Walters and Rainbird, 2004). Thelinkage between activities and structures is the need that is created for some resourceas a result of the benefit that is sought by the supply chain members (Willer, 1999;Burke, 1997; Cook and Emerson, 1978). According to Willer (1999, p. 21), “exchangetheory recognizes the efficacy of structure and focuses its investigation on finding theconditions in structures that produce different behaviors”.

2.2 Supply chain performanceWhile organizational managers are ultimately held accountable for the performance oftheir particular organizations, the success of their organizations depends heavily uponthe success of the supply chain in which the organization participates as a partner.Success in this case is defined as customer satisfaction. Today’s managers must bothmanage efficiently and effectively at the organizational level and also at the supplychain level. Heizer and Render (2006) propose that effective SCM depends on the ability

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to develop long-term, strategic relationships with supply chain partners. Such effectiveSCM maximizes value to the ultimate customers of the supply chain in terms of bothsatisfaction with the product and/or services and a relatively low total cost of theproduct and/or service. For purposes of this study, we take the extended view of thesupply chain from “supplier’s supplier to ultimate customer” and incorporate withinour measure of SCP the ability to satisfy the ultimate customer in terms of both qualityand cost.

2.3 Supply chain linkagesStinchcombe (1975) found that units within a group can be identified or defined bywhat they lack that the other members can furnish. Using Stinchcombe’s findings andextrapolating from Levi Strauss’s (1969) study these units form into subgroups thatneed each other and are a system of exchange such as supply chains. Stinchcombe(1975) and Freeman (1977) both identify that these subgroups will have a point ofcentrality and form a sort of boundary. These points of centrality act as a governingbody and will be balanced by the specialty of the desired resource (Stinchcombe, 1975).

Emerson (1972) identified a network of exchange as consisting of:. a set of actors (persons or corporate groups);. a distribution of valued resources among those actors;. for each actor a set of exchange opportunities with other actors in the network;. a set of historically developed and utilized exchange opportunities called

exchange relationships; and. a set of network connections linking exchange relations into a single network.

At minimum, an exchange network must be dyadic in nature with a point of centralityand an upstream or downstream member (Freeman, 1992). Emerson (1972) stated thatexchanges are limited to actions contingent on rewarding reactions from others. Thisimplies that the relationship at a minimum is two sided, mutually dependent, and allowfor mutually rewarding transactions or exchanges. Further these mutually rewardingtransactions or exchange relationships are identified by Willer (1999) as a socialexchange.

2.3.1 Power. Cook and Emerson (1978) found that exchange theory includes thelevel of power that the participants bring to the transaction. These authors definepower as the capacity to exploit while Lawler (1992) defines power as a control relatedoutcome of an exchange. Extrapolating from either of these definitions, the exploitationof a valued resource can increase the power of the upstream or downstream memberpossessing the resource.

Power is often a missing element in SCM. Prior to the advent of SCM, power was animportant aspect of channel management. In SCM the emphasis is on collaborationrather than control. However, power is important and differentials in power can affectthe decisions made within the supply chain. While power was included in the classicalsupply management literature (Cook and Emerson, 1978; Emerson et al., 1983; Lawler,1992). Maloni and Benton (2000, p. 51) note that much of the recent power literature“examines power influences from the marketing channel perspective” and caution that“It is not judicious [. . .] to extend the findings from such research to other industries.”They indicate that additional research is needed “to ensure that the topic of power is

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rigorously and holistically covered by supply chain literature”. Cox (2001) examinesbuyer and supplier power from a conceptual perspective but does not examine thepower relationship empirically.

The purpose of an exchange is to improve an individual’s or organization’s welfare(Hildenbrand, 1968). Hildenbrand (1968) identified two basic ingredients: commoditiesand agents. The first ingredient is the commodities involved in the exchange.Commodities are defined by Hildenbrand (1968) as anything that may be used orconsumed. The second ingredient identified is an agent. An agent is characterized bythree elements:

(1) the consumption set of an organization;

(2) preferences; and

(3) the organization ignition resources.

Further, groups are held together by the mutual benefit of the exchange (Spread, 1984).Extrapolating from that finding, for a group to maintain its cohesiveness, the groupmust enter into win-win situations. The win-win situation is identified as balance byCook (1987). According to Cook (1987, p. 217), “exchange relations are balanced if thetwo actors involved in (the) exchange are equally dependent upon one another”.However, as Johnsen and Ford (2008) propose these linkages (relationships) to becharacterized as asymmetric because some supply chain members are larger and morepowerful than others. Hamblin and Kunkel (1977, p. 120) state, “an exchange relation isa relation of mutual dependence and reciprocal (although not necessarily equal)power”. Extrapolating from these authors, a win-win situation is a relationship inwhich the corporate actors are equally dependent upon each other and power isreciprocal but not necessarily equal.

Freeman (1977) identified that the exchange relationship could be measured on thebasis of the linkages. Researchers (Cook and Emerson, 1978; Emerson et al., 1983;Lawler, 1992) identified power as a measure of these linkages. Amaeshi et al. (2008),Maloni and Benton (2000), and Cox (2001) further state that power is a critical factor insupply chain relationships:

H1. Power positively impacts SCP.

2.3.2 Benefits. Supply chain activities are distinguished by linkages that are createdbased on a need for a resource or service that the various organizations of the supplychain provide. Cook and Emerson (1978) and Burke (1997) examined linkages andlooked at the exchange of resources in relation to the dependence that is created.Willer (1999) argues that it is not necessarily a dependence that is created but a systemof mutual gain or benefit. One benefit that is sought is efficiency (Cannon et al., 2008).While Polo-Redondo and Cambra-Fierro (2008) found that internal standardization ofan organization’s processes can benefit supply chain relationships, it would stand toreason that other benefits could be gained by standardizing within the supply chainnetwork. For example, supply chain members may benefit from gained efficiencies by astandardization of policies and procedures within the supply chain network.In addition, Im and Rai (2008) explain the potential benefits to inter-organizationalrelationships of exploration and exploitation in order to sustain long-term performance,which could then extend to successful supply chain relationships. For example,knowledge gains, learning, and innovation are some of the benefits of these

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relationships. These types of relationships can lead to benefits that result in win-winsituations for all supply chain members:

H2. Benefits positively impact SCP.

2.3.3 Risk reduction. Sociologists explain the existence of groups such as channelpartners and supply chain members by examining the subject from the position thatmost groups have a point of centrality which is thought to be in the more powerfulposition (Cook and Whitmeyer, 1992). However, when viewed abstractly within therealms of business, logistics or supply chain requirements or advantages may berationales that explain the existence of these groups.

According to Cook and Whitmeyer (1992), exchange theory focuses on the tiesbetween members of these groups or networks. These linkages are created by the needto fulfill a requirement for some resource. These resources can be material,informational or symbolic. Groups or networks are often used to gain access toresources that might otherwise be difficult to obtain (Johannisson, 1987). These typesof resources are difficult or costly to obtain and thus create a need. This need, createdbecause of the scarcity of resources, is motivation for organizations to coordinate witheach other (Johannisson, 1987). Extrapolating from these findings, the scarcity ofresources creates a risk. Organizations may become members of a supply chain toreduce risk. Cannon et al. (2008) further posit one of the linkages that impacts SCP isthe desire to reduce risk:

H3. Risk reduction positively impacts SCP.

Figure 1 shows the hypothesized supply chain linkages model that is examined in thispaper.

3. Methodology3.1 Data collection processData were collected via an online data service during the summer of 2008. Of the 300individuals who accessed the survey, 145 completed the supply chain linkages andsupply chain scales. Of the respondents 91 represented the manufacturing sector, andthe remaining 54 represented the services sector. Respondents have been in theircurrent positions for an average of 5.6 years and represent organizations with anaverage of 27,533 employees and average annual revenues of $1.48 billion.

Figure 1.Supply chain linkagesmodel with hypotheses

Power

Benefits

Risk reduction

Supply chainperformance

H1: (+)

H2: (+)

H3: (+)

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3.2 Measurement scalesThe supply chain linkages construct incorporates three dimensions: power, benefits,and risk reduction. The power, benefits, and risk reduction scales were developedspecifically for this study. These scales were developed from the existing literature andthen reviewed by experts to ensure they were measuring power, benefits and riskreduction. The theorized model incorporates a measure of SCP developed by Green andWhitten (2008). The study scales are presented in Table I.

Supply chain linkagesPlease indicate the extent to which you agree or disagree with each statement as the statement relatesto your organization’s primary supply chain (1, strongly disagree; 7, strongly disagree)Power

1. My company has a great amount of influence over the suppliers of resources for our company2. My company has a great amount of influence over the buyers of our products3. My company has a great amount of influence over the cost of resources received from our

suppliers4. My company has a great amount of influence over our competitors

Benefits5. My company receives benefits other than resources from our relationships with suppliers6. My company receives benefits other than purchases from our relationships with buyers7. My company benefits from standardization of procedures with our suppliers8. My company benefits from the standardization of procedures with our buyers

Risk reduction (a ¼ 0.924)9. My company’s suppliers reduce the uncertainty we face

10. My company’s buyers reduce the uncertainty we face11. My company’s suppliers reduce the risk we face12. My company’s buyers reduce the risk we face

Supply chain performance scalePlease indicate the extent to which you agree or disagree with each statement as the statement relatesto your organization’s primary supply chain (1, strongly disagree; 7, strongly disagree)

1. This organization’s primary supply chain has the ability to deliver zero-defect products to finalcustomers

2. This organization’s primary supply chain has the ability to deliver value-added services to finalcustomers

3. This organization’s primary supply chain has the ability to eliminate late, damaged andincomplete orders to final customers

4. This organization’s primary supply chain has the ability to quickly respond to and solveproblems of the final customers

5. This organization’s primary supply chain has the ability to deliver products precisely on-time tofinal customers

6. This organization’s primary supply chain has the ability to deliver precise quantities to finalcustomers

7. This organization’s primary supply chain has the ability to deliver shipments of variable size ona frequent basis to final customers

8. This organization’s primary supply chain has the ability to deliver small lot sizes and shippingcase sizes to final customers

9. This organization’s primary supply chain has the ability to minimize total product cost to finalcustomers

10. This organization’s primary supply chain has the ability to minimize all types of wastethroughout the supply chain

11. This organization’s primary supply chain has the ability to minimize channel safety stockthroughout the supply chain

Table I.Measurement scales

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Non-response bias was assessed by comparing the responses of early and laterespondents using a common approach described by Lambert and Harrington (1990).Of the study respondents 104 were categorized as early respondents and 41 werecategorized as late respondents based on whether they responded to the initial orfollow-up request to participate. A comparison of the means of the demographicvariables (years in current position, total number of employees in organization, andtotal sales revenues for the organization) was conducted using one-way ANOVA. Thecomparisons resulted in statistically non-significant differences. An additionalcomparison of the means for the summary variables (power, benefits, risk reduction,and SCP) indicated that the second wave means for the supply chain linkages variableswere significantly less than those for the first wave. The first wave is more heavilyweighted with respondents from the manufacturing sector than is the second wave. Toevaluate whether there are systematic differences in supply chain linkages in themanufacturing versus the service sectors, responses from each sector were alsoanalyzed separately. There were no significant differences noted for the SCP summaryvariable means. Based upon these results, there is some concern related tonon-response bias. Common method bias may lead to inflated estimates of therelationships among variables, when data are collected from single respondents(Podsakoff and Organ, 1986). As Podsakoff and Organ (1986) recommend, Harman’sone-factor test was used to examine the potential bias. Substantial bias is indicatedwhen either a single factor or one “general” factor explains a majority of the totalvariance (Podsakoff and Organ, 1986). Results of the factor analysis with varimaxrotation identify three factors combining to account for 78 per cent of the total variance.The first factor accounted for only 32 per cent of the total variance. While the SCP andpower related items loaded on separate factors, the benefits and risk reduction itemsloaded on a third single factor. A x 2 difference test was subsequently conducted on thebenefits and risk reduction scales with a significant result indicating that while thescales load together they exhibit discriminant validity. Based on this analysis, commonmethod bias is not a significant problem in this data collection.

3.3 Regression analysisA multiple regression analysis approach, as opposed to an SEM approach, is adopteddue to the relatively small sizes of the manufacturing (n ¼ 91) and services (n ¼ 54)samples. Hair et al. (2006, p. 741) recommend a sample size of 200 as a “sound basis forestimation” based on a structural equation modeling approach.

4. Results4.1 Measurement scale assessmentWe have adopted the confirmatory factor analysis (CFA) approach recommended byGerbing and Anderson (1988) and Koufteros (1999) to assess unidimensionality of themeasurement scales. Koufteros (1999) recommends that the individual scales beincorporated together in a measurement model and that this model be subjected to CFAand that relative x 2, non-normed fit index (NNFI), and comparative fit index (CFI) valuesto assess fit when the sample size is relatively small as is the case for this study. Relativex 2 values of less than 2.00 and NNFI and CFI values greater than 0.90 indicate reasonablefit (Koufteros, 1999). Results of the analysis indicate that the measurement model fits thedata well with an NNFI of 0.939, and a CFI of 0.952. The relative x 2 of 3.79 is somewhat

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higher that the recommended value of 2.00. Kline (1998) recommends relative x 2 values ofless than the 3.00, while Marsch and Hocevar (1985) discuss a somewhat less stringentcut-off of 5.00. The root mean square error of approximation (RMSEA) is often reported asan absolute fit measure (Garver and Mentzer, 1999). RMSEA is sensitive to sample size,however, and is best suited for relatively large samples (n . 500) (Hair et al., 2006). Thestandardized root mean residual (SRMR) is reported as an alternative absolute fit measure,since it’s value is not significantly impacted by sample size, with lower SRMR valuesrepresenting better fit. In this case, the SRMR is 0.046. The individual measurement scalesare considered sufficiently reliable and valid and the fit of the confirmatory factor model isconsidered sufficient to support further analysis. Table II displays the standardizedparameter estimates and associated t-values for scale items as well as reliability andaverage variance extracted values for each scale.

Garver and Mentzer (1999) recommend computing Cronbach’s coefficient a toassess scale reliability, with a values greater than or equal to 0.70 indicating sufficientreliability. a scores for all of the measurement scales exceed the 0.70 level. a values forpower, benefits, risk reduction, and SCP are 0.923, 0.897, 0.924, and 0.972, respectively.The study scales are sufficiently reliable.

Ahire et al. (1996) recommend assessing convergent validity using the normed-fitindex (NFI) coefficient with values greater than 0.90 indicating strong validity.

Scale/item Standarized coefficients t-value Cronbach’s a Average variance extracted

Power 0.923 0.775Item 1 0.92 14.23Item 2 0.90 13.90Item 3 0.88 12.50Item 4 0.85 12.63Benefits 0.897 0.708Item 1 0.69 9.236Item 2 0.80 11.41Item 3 0.92 14.32Item 4 0.93 14.72Risk reduction 0.924 0.765Item 1 0.82 11.88Item 2 0.91 14.13Item 3 0.88 13.37Item 4 0.89 13.70SCP 0.972 0.789Item 1 0.86 12.92Item 2 0.87 13.22Item 3 0.89 13.64Item 4 0.95 15.25Item 5 0.92 14.40Item 6 0.90 13.82Item 7 0.90 14.00Item 8 0.84 12.45Item 9 0.89 13.71Item 10 0.87 13.20Item 11 0.89 13.65

Note: n ¼ 145

Table II.CFA results based on full

sample

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Garver and Mentzer (1999) recommend reviewing the magnitude of the parameterestimates for the individual measurement items to assess convergent validity. A strongcondition of validity is indicated when the estimates are statistically significant andgreater than or equal to 0.70. NFI values for the power (0.96), risk reduction (0.98), andSCP (0.96) scales exceed the 0.90 threshold, and parameter estimates for each of theindividual items exceed the 0.70 threshold, with values of 0.84 or greater for all items inthe three scales. The NFI of 0.79 for the benefits scale does not meet the recommendedlevel. The four items in the benefits scale are, however, all significant with oneparameter estimate slightly below the recommended level at 0.69.

Discriminant validity was assessed using a x 2 difference test for each pair of scalesunder consideration, with a statistically significant difference in x 2 indicating validity(Garver and Mentzer, 1999; Ahire et al., 1996; Gerbing and Anderson, 1988). Allpossible pairs of the study scales were subjected to x 2 difference tests with eachpairing producing a statistically significant difference.

Predictive validity was assessed by testing whether the scales of interest correlatewith other measures as expected (Ahire et al., 1996; Garver and Mentzer, 1999).A review of the correlation matrix (Table III) for study summary variables indicatesthat all variables are positively and significantly correlated, as expected, indicatingsufficient predictive validity.

4.2 Correlation and regression analyses resultsA multiple regression approach was taken to test the three study hypotheses. First, thecombined (both manufacturing and service sectors) sample was analyzed with thedescriptive statistics, correlations, regression results are displayed in Table III.The results support all three study hypotheses. All of the supply chain linkagesvariables (power, benefits, and risk reduction) are positively and significantlycorrelated with SCP at the 0.01 level. Results of the multiple regression analysis withthe three linkages variables as independent variables and SCP as the dependentvariable indicate that power and risk reduction are positively and significantly relatedto SCP at the 0.01 level. The benefits variable is also positively and significantly relatedbut at the 0.05 level. The R 2 for the regression model is 0.613 indicating that thelinkages variables combine to explain 61 per cent of the variation in SCP.

Next, the manufacturing sector sample was analyzed with the results displayed inTable IV. Again, all correlations of the linkage variables with SCP are positive andsignificant at the 0.01 level. Results of the multiple regression analysis indicate thatonly the power variable is significantly related (at the 0.05 level) to SCP. The R 2 for theregression model is 0.435 indicating that the linkages variables combine to explain 44per cent of the variation in SCP in the manufacturing sector.

Finally, the services sector sample was analyzed with results displayed in Table V.All correlations are again significant at the 0.01 level. The multiple regression resultsindentify only the risk reduction variable as significantly related to SCP at the 0.01level. The R2 for the regression model is 0.784 indicating that the linkages variablescombine to explain 78 per cent of the variation in SCP in the services sector.

Multicolinearity among the independent variables is a concern when using multipleregression analysis. Hair et al. (2006) recommend assessing multicolinearity byreviewing correlation matrix for the independent variables and further by computingtolerance and variance inflation factor (VIF) values. The correlation matrices are

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A.Descriptive

statistics

Variable

Mean

SD

SC

lin

kag

es–

pow

er(S

CL

-PW

)4.

521.

43S

Cli

nk

ages

–b

enefi

ts(S

CL

-BF

)4.

371.

27S

Cli

nk

ages

–ri

skre

du

ctio

n(S

CL

-RR

)4.

361.

29S

up

ply

chai

np

erfo

rman

ce(S

CP

)4.

681.

41B.Correlations

Variables

PW

BF

RR

SCP

Pow

er(P

W)

1B

enefi

ts(B

F)

0.68

3*

1R

isk

red

uct

ion

(RR

)0.

725

*0.

854

*1

Su

pp

lych

ain

per

form

ance

(SC

P)

0.68

5*

0.71

8*

0.74

3*

1C.Regressionresultsa

Standardized

coefficients

Collinearity

statistics

Independentvariables

bt-value

Sig.

Tolerance

VIF

Pow

er(P

W)

0.27

53.

556

0.00

10.

459

2.17

9B

enefi

ts(B

F)

0.24

42.

387

0.01

80.

263

3.80

5R

isk

red

uct

ion

(RR

)0.

335

3.09

10.

002

0.23

34.

285

Notes:

* Cor

rela

tion

issi

gn

ifica

nt

atth

e0.

01le

vel

(tw

o-ta

iled

);n¼

145;

ad

epen

den

tv

aria

ble¼

SC

P

Table III.Descriptive statistics,

correlations, andregression results for

combined sample

Impact of supplychain linkages

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A.Descriptive

statistics

Variable

Mean

SD

SC

lin

kag

es–

pow

er(S

CL

-PW

)4.

741.

28S

Cli

nk

ages

–b

enefi

ts(S

CL

-BF

)4.

581.

08S

Cli

nk

ages

–ri

skre

du

ctio

n(S

CL

-RR

)4.

531.

16S

up

ply

chai

np

erfo

rman

ce(S

CP

)4.

941.

24B.Correlations

Variables

PW

BF

RR

SCP

Pow

er(P

W)

1B

enefi

ts(B

F)

0.57

1*

1R

isk

red

uct

ion

(RR

)0.

598

*0.

824

*1

Su

pp

lych

ain

per

form

ance

(SC

P)

0.54

9*

0.59

1*

0.60

3*

1C.Regressionresultsa

Standardized

coefficients

Collinearity

statistics

Independentvariables

bt-value

Sig.

Tolerance

VIF

Pow

er(P

W)

0.26

62.

607

0.01

10.

624

1.60

4B

enefi

ts(B

F)

0.22

91.

585

0.11

70.

311

3.21

3R

isk

red

uct

ion

(RR

)0.

255

1.72

50.

088

0.29

73.

369

Notes:

* Cor

rela

tion

issi

gn

ifica

nt

atth

e0.

01le

vel

(tw

o-ta

iled

);n¼

91;

ad

epen

den

tv

aria

ble¼

SC

P

Table IV.Descriptive statistics,correlations, andregression results formanufacturing sample

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A.Descriptive

statistics

Variable

Mean

SD

SC

lin

kag

es–

pow

er(S

CL

-PW

)4.

171.

61S

Cli

nk

ages

–b

enefi

ts(S

CL

-BF

)4.

021.

49S

Cli

nk

ages

–ri

skre

du

ctio

n(S

CL

-RR

)4.

061.

44S

up

ply

chai

np

erfo

rman

ce(S

CP

)4.

251.

58B.Correlations

Variables

PW

BF

RR

SCP

Pow

er(P

W)

1B

enefi

ts(B

F)

0.76

7*

1R

isk

red

uct

ion

(RR

)0.

844

*0.

875

*1

Su

pp

lych

ain

per

form

ance

(SC

P)

0.79

9*

0.81

3*

0.87

3*

1C.Regressionresultsa

Standardized

coefficients

Collinearity

statistics

Independentvariables

bt-value

Sig.

Tolerance

VIF

Pow

er(P

W)

0.19

71.

598

0.11

60.

284

3.52

7B

enefi

ts(B

F)

0.18

31.

338

0.18

70.

232

4.31

7R

isk

red

uct

ion

(RR

)0.

547

3.34

70.

002

0.16

26.

180

Notes:*

Cor

rela

tion

issi

gn

ifica

nt

atth

e0.

01le

vel

(tw

o-ta

iled

);n¼

54;

ad

epen

den

tv

aria

ble¼

SC

P

Table V.Descriptive statistics,

correlations, andregression results for

services sample

Impact of supplychain linkages

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displayed in the A panels for Tables II-IV, and the tolerance and VIF values arereported in the C panels. Hair et al. (2006) identify correlations greater than or equal to0.90, tolerance values less than 0.10, and VIF values greater than 10 as indicatingunacceptable levels of multicolinearity. All correlations are below 0.90, all tolerancevalues are greater than 0.10, and all VIF values are less than 10 indicating thatmulticolinearity is within acceptable bounds.

To summarize, all linkages variables (power, benefits, and risk reduction)significantly impact SCP when the combined sample is considered. When the sample isparsed, the power variable is identified as having the most significant impact in themanufacturing sector, and the risk reduction variable as the most significant within theservices sector.

5. ConclusionsHeizer and Render (2006, p. 432) propose that the key to effective SCM is the ability toforge long-term, strategic relationships with supply chain partners for the purpose of“maximizing value to the ultimate customer”. The results presented support thisgeneral proposition. More specifically, this study and the reported results identify thesupply chain linkage variables of power, benefits, and risk reduction as important tothe performance of the supply chain. The separate analyses of the manufacturingsector and services sector samples provides insight into the importance of the variablesdepending upon the type of organization and the organization’s position within thesupply chain. For the manufacturing sector, power is the dominant linkage; within theservices sector, risk reduction dominates.

SCM emphasizes collaboration rather than control. Our research findings havepotential implications for both manufacturer and service sectors. For example,consider at minimum a supplier-customer dyad. Alliances and long-term agreementsare valuable during peak demand time for holding lead times and getting preferentialdelivery as compared to those who are on short-term purchasing cycle. In addition, amanufacturing oriented supply chain is more likely to have a point of centrality, suchas the manufacturing organization, to which other supply chain members are solelydependent upon for orders. The implication is that supply chain members with lesspower might suffer when demand is low or may face competition from other supplierswhen demand is high.

Risk reduction is found to be the important variable in service oriented supplychains. Service organizations have few tangible resources associated with them. As aresult service organizations are vulnerable to changes in demand and have fewerassets to use as potential collateral. This implies that service oriented supplychains may use membership as a way to reduce the risk associated with a change indemand.

While the objective to investigate the relationships of the supply chain linkagesvariables to SCP was accomplished, there are limitations to the study that should benoted. First, because the same data collection was used to assess the new linkagesscales and to test the hypotheses, the study must be described as somewhatexploratory. The benefits scale in particular will require some revision for subsequentresearch applications. Additionally, some concern related to non-response bias shouldbe noted. Time and financial resource limitations precluded additional third and fourthwaves necessary to more clearly identify the presence of the bias.

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Future research to extend the investigation of the power, benefits, and riskreduction linkages is necessary. While this study incorporated both the manufacturingand services sectors, the sample sizes for each sector are relatively small. Additionaldata collections are necessary in each sector to verify the results reported here. There isalso a need to investigate the impact of the linkages on other performance measuressuch as operational performance and logistics performance. It will be important also tomore thoroughly develop and document best-practice methods for developing theidentified linkages with supply chain partners. Another area of future researchincludes investigation of building and designing new supply chain linkages.

The results reported here serve to generally inform management practitioners of theimportance of establishing strong linkages throughout the supply chain with bothimmediate and extended suppliers and customers. Practitioners must continue tosuccessfully manage their internal organizations while forging the “long-term,strategic” relationships requisite for improved performance at the supply chain levels.

References

Ahire, S.L., Golhar, D.Y. and Waller, M.A. (1996), “Development and validation of TQMimplementation constructs”, Decision Sciences, Vol. 27 No. 1, pp. 23-56.

Amaeshi, K.M., Osuji, O.K. and Nnodim, P. (2008), “Corporate social responsibility in supplychains of global brands: a boundaryless responsibility? Clarification, exceptions andimplications”, Journal of Business Ethics, Vol. 81, pp. 223-34.

Bendavid, Y., Lefebvre, E., Lefebvre, L.A. and Fosso-Wamba, S. (2009), “Key performanceindicatiors for the evaluation of RFID-enabled B-to-B e-commerce applications: the case ofa five-layer supply chain”, Information Systems and e-Business Management, Vol. 7 No. 1,pp. 1-20.

Bichescu, B.C. and Fry, M.J. (2009), “A numerical analysis of supply chain performance undersplit decision rights”, Omega, Vol. 37 No. 2, pp. 358-79.

Burke, P.J. (1997), “An identity model for network exchange”, American Sociological Review,Vol. 62 No. 1, pp. 134-50.

Cannon, A.R., Reyes, P.M., Frazier, G.V. and Prater, E.L. (2008), “RFID in the contemporarysupply chain: multiple perspectives on its benefits and risks”, International Journal ofOperations & Production Management, Vol. 28 No. 5, pp. 433-54.

Cook, K.S. (1987), “Emerson’s contributions to social exchange theory”, in Cook, K.S. (Ed.),Social Exchange Theory, Sage, Newbury Park, CA, pp. 209-22.

Cook, K.S. and Emerson, R.M. (1978), “Power, equity, and commitment in exchange networks”,American Sociological Review, Vol. 45 No. 3, pp. 721-39.

Cook, K.S. and Whitmeyer, J.M. (1992), “Two approaches to social structure: exchange theory andnetwork analysis”, Annual Review of Sociology, Vol. 18, pp. 109-27.

Cox, A. (2001), “Understanding buyer and supplier power: a framework for procurement andsupply competence”, The Journal of Supply Chain Management, Spring, pp. 8-15.

Duchessi, P. and Chengalur-Smith, I. (2008), “Enhancing business performance, via vendormanaged inventory applications”, Communications of the ACM, Vol. 51 No. 12, pp. 121-7.

Emerson, R.M. (1972), Position and Power in Exchange Networks, Institute of Sociology,Seattle, WA.

Emerson, R.M., Cook, K.S. and Gillmore, M.R. (1983), “Valid predictions from invalidcomparisons: response to Heckathorn”, Social Forces, Vol. 61 No. 4, pp. 1232-47.

Impact of supplychain linkages

679

Dow

nloa

ded

by S

HA

HE

ED

ZU

LFI

KA

R A

LI

BH

UT

TO

IN

ST O

F SC

I &

TE

CH

KA

RA

CH

I A

t 01:

29 1

3 N

ovem

ber

2015

(PT

)

Forsiund, H. and Jonsson, P. (2007), “Dyadic integration of the performance managementprocess”, International Journal of Physical Distribution & Logistics Management, Vol. 27No. 7, pp. 546-67.

Freeman, L.C. (1977), “A set of measures of centrality based on between-ness”, Sociometry, Vol. 40No. 1, pp. 35-41.

Freeman, L.C. (1992), “The sociological concept of ‘group’: an empirical test of two models”, TheAmerican Journal of Sociology, Vol. 98 No. 1, pp. 152-66.

Garver, M.S. and Mentzer, J.T. (1999), “Logistics research methods: employing structuralequation modeling to test for construct validity”, Journal of Business Logistics, Vol. 20No. 1, pp. 33-57.

Gerbing, D.W. and Anderson, J.C. (1988), “An updated paradigm for scale developmentincorporating unidimensionality and its assessment”, Journal of Marketing Research,Vol. 25 No. 2, pp. 186-92.

Green, K.W. Jr and Whitten, D. (2008), “The impact of aligning marketing strategies throughoutthe supply chain”, Working Paper No. 08-02 MGT, Sam Houston State University,Huntsville, TX.

Hair, J.F. Jr, Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. (2006), Multivariate DataAnalysis, Pearson Prentice-Hall, Upper Saddle River, NJ.

Hamblin, R.L. and Kunkel, J.H. (1977), “Behavioral theory in sociology”, in Stolte, J.F. andEmerson, R.M. (Eds), Structural Inequality: Position and Power in Network Structures,Transaction, New Brunswick, NJ.

Heizer, J. and Render, B. (2006), Operations Management, 8th ed., Pearson Education, UpperSaddle River, NJ.

Hildenbrand, W. (1968), “The core of an economy with a measure of space of economic agents”,The Review of Economic Studies, Vol. 35 No. 4, pp. 443-52.

Holweg, M. and Pil, F.K. (2008), “Theoretical perspective on the coordination of supply chains”,Journal of Operations Management, Vol. 26, pp. 389-406.

Im, G. and Rai, A. (2008), “Knowledge sharing ambidexterity in long-term interorganizationalrelationships”, Management Science, Vol. 54 No. 7, pp. 1281-96.

Johannisson, B. (1987), “Anarchists and organizers: entrepreneurs in a network perspective”,International Studies of Management & Organizations, Vol. 17, pp. 49-63.

Johnsen, R.E. and Ford, D. (2008), “Exploring the concept of asymmetry: a typology of analyzingcustomer-supplier relationships”, Industrial Marketing Management, Vol. 37 No. 4,pp. 471-83.

Klein, R., Rai, A. and Straub, D.W. (2007), “Competitive and cooperative positioning in supplychain logistics relationships”, Decision Sciences, Vol. 38 No. 4, pp. 611-46.

Kline, R.B. (1998), Principles and Practice of Structural Equation Modeling, Guilford Press,New York, NY.

Koufteros, X.A. (1999), “Testing a model of pull production: a paradigm for manufacturingresearch using structural equation modeling”, Journal of Operations Management, Vol. 17No. 4, pp. 467-88.

Lambert, D.M. and Harrington, T.C. (1990), “Measuring nonresponse bias in customer servicemail surveys”, Journal of Business Logistics, Vol. 11 No. 2, pp. 5-25.

Lau, H.C., Agussurja, L. and Thangarajoo, R. (2008), “Real-time supply chain control viamulti-agent adjustable autonomy”, Computers & Operations Research, Vol. 35 No. 11,pp. 3452-64.

IMDS109,5

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TO

IN

ST O

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I &

TE

CH

KA

RA

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I A

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ber

2015

(PT

)

Lawler, E.J. (1992), “Effective attachments to nested groups: a choice process theory”, AmericanSociological Review, Vol. 57 No. 1, pp. 327-39.

Lee, Y.M., Cheng, F. and Leung, Y.T. (2009), “A quantitative view on how RFID can improveinventory management in a supply chain”, International Journal of Logistics: Research andApplications, Vol. 12 No. 1, pp. 23-43.

Levi-Strauss, C. (1969), The Raw and the Cooked, Harper & Row, New York, NY.

Maloni, M. and Benton, W.C. (2000), “Power influences in the supply chain”, Journal of BusinessLogistics, Vol. 21 No. 1, pp. 49-73.

Markovsky, B., Skvoretz, J., Willer, D., Lovaglia, M.J. and Erger, J. (1993), “The seeds of weakpower: an extension of network exchange theory”, American Sociological Review, Vol. 58No. 2, pp. 197-209.

Marsch, H.W. and Hocevar, D. (1985), “Application of CFA of the study of self-concept: first andhigher order factor and their invariance across groups”, Psychological Bulletin, Vol. 97No. 3, pp. 562-82.

Podsakoff, P.M. and Organ, D.W. (1986), “Self-reports in organizational research: problems andprospects”, Journal of Management, Vol. 12 No. 4, pp. 531-44.

Polo-Redondo, Y. and Cambra-Fierro, J. (2008), “Influence of the standardization of a firm’sproductive process on the long-term orientation of its supply relationships: an empiricalstudy”, Industrial Marketing Management, Vol. 37, pp. 407-20.

Spread, P. (1984), “Blau’s exchange theory, support and the macrostructure”, The British Journalof Sociology, Vol. 35 No. 2, pp. 157-75.

Stinchcombe, A.L. (1975), “A structural analysis of sociology”, The American Sociologist, Vol. 10,pp. 57-64.

Swafford, P.M., Ghosh, S. and Murthy, N. (2008), “Achieving supply chain agility throughIT integration and flexibility”, International Journal of Production Economics, Vol. 116No. 2, pp. 288-97.

Ustundag, A. and Tanyas, M. (2009), “The impacts of radio frequency identification(RFID) technology on supply chain costs”, Transportation Research, Vol. 45 No. 1,pp. 29-38.

Walters, D. and Rainbird, M. (2004), “The demand chain as an integral component of the valuechain”, Journal of Consumer Marketing, Vol. 21 No. 7, pp. 465-75.

Wang, S., Chang, S. and Wang, R. (2009), “Assessment of supplier performance based onproduct-development strategy by applying multi-granularity linguistic term sets”, Omega,Vol. 37 No. 1, pp. 215-26.

Willer, D. (1999), Network Exchange Theory, Praeger, Westport, CT.

Yang, J., Wang, J., Wong, C.W.Y. and Lai, K. (2008), “Relational stability and allianceperformance in supply chain”, Omega, Vol. 36, pp. 600-8.

Zong, D. (2008), “Supply chain transformation by ERP for enhancing performance: anempirical investigation”, Advances in Competitiveness Research, Vol. 16 No. 1,pp. 87-98.

Further reading

Dai, Z. (2008), “Supply chain transformation by ERP for enhancing performance: an empiricalinvestigation”, Advances in Competitiveness Research, Vol. 16 No. 1, pp. 87-98.

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Eden, L. (2002), “Regional integration and foreign direct investment: theory and lessons fromNAFTA”, in Kotabe, M. and Aulakh, P. (Eds), Emerging Issues in International BusinessResearch, Edward Elgar, Northampton, MA, pp. 15-36.

Emerson, R.M. (1987), “Toward a theory of value in social exchange”, in Cook, K.S. (Ed.), SocialExchange Theory, Sage, Newbury Park, CA, pp. 11-46.

Levi-Strauss, C. (1963), Structural Anthropology, Harper & Row, New York, NY.

Corresponding authorPamela J. Zelbst can be contacted at: mgt_pjz@shsu.edu

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