The moderating effect of supplychain role on the relationshipbetween supply chain practices

and performanceAn empirical analysis

Lori S. Cook and Daniel R. HeiserDePaul University, Chicago, Illinois, USA, and

Kaushik SenguptaHofstra University, Hempstead, New York, USA

Abstract

Purpose The purpose of this paper is to examine the relationships between specific supply chainpractices and organizational performance and whether this relationship is moderated by the role that acompany assumes in its respective supply chain.

Design/methodology/approach This paper uses regression analysis and the relative weightsmethod to analyze a set of survey data from respondents within the non-academic, North Americanmembership of the Institute of Supply Management.

Findings The results show that the supply chain role for a company makes a difference in terms ofthe specific supply chain practices that lead to better performance. Further, there is a clear indicationthat the relative importance of a specific practice varies across the supply chain roles therebyindicating that a general link between practice and performance may be erroneous without consideringthe specific context of the company concerned.

Research limitations/implications Supply chain practices are complex constructs. While thisstudy shows the effect of broadly-accepted supply chain practices on performance, not all possiblepractices are covered in the study. Additional practices not considered may have an effect on companyperformance and future research may improve upon the findings by extending the analysis to includean expanded segmentation of supply chain role.

Practical implications The results of the study serve as a practical guideline for managersthat not all practices would be effective for all companies. Managers must look at the role-specificcontext of their organization in the supply chain before deciding which practices are likely to beappropriate.

Originality/value This paper expands the current body of research in the supply chain area byexamining the supply chain roles of manufacturer, distributor, retailer and service provider. This is amuch broader construct than the more common dyadic treatment of a supply chain consisting only of acustomer and supplier, and adds a new contextual dimension to supply chain research. In addition,service provider as a supply chain role has been hardly researched before.

Keywords Supply chain management, Organizational performance

Paper type Research paper

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

www.emeraldinsight.com/0960-0035.htm

Author last names are in alphabetical order. All authors contributed equally to this research.

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Received October 2009Revised May 2010,August 2010Accepted August 2010

International Journal of PhysicalDistribution & Logistics ManagementVol. 41 No. 2, 2011pp. 104-134q Emerald Group Publishing Limited0960-0035DOI 10.1108/09600031111118521

IntroductionOrganizations are faced with an array of challenges as they strive to compete intodays dynamic global markets. To remain competitive, organizations mustrecognize the importance of supply chain practices that improve not only their ownperformance, but also coordinate with their supply chain partners to improve their jointperformance. Yet, despite the significant advances in research and practices, manyorganizations continue to struggle to understand the complex issues associated with thecoordinated planning and supply activities amongst the members of their supplynetworks.

Supply chain management (SCM) research has evolved to a stage where analyticaland empirical methodologies have allowed researchers to identify and validate basicSCM models and constructs. Numerous studies have also investigated the effects ofvarious SCM related practices affecting organizational performance. As SCM researchcontinues to develop, many researchers are focusing on the cross-industry validity ofprevious findings. One of the aspects of interest is the effect of employing various bestpractices by organizations in different positions of the supply chain. This is a significantissue to address to determine whether commonly advocated practices are equallyrelevant across the length of the supply chain. While a few studies have examined thedifference in effectiveness of SCM practices based on whether these are applied on thesupply side or the distribution side of the supply chain (Frohlich and Westbrook, 2001,2002; Kim, 2006; Li et al., 2005b), most of these studies have treated the supply and thedistribution sides of the supply chain as one overall stage. Therefore, the treatment hasbeen largely based on a dyadic basis. Such an aggregated view of supply chain positionmasks a number of issues, which companies in specific supply chain roles may face.For instance, should distributors and retailers look at supply chain practices the sameway? From the dyadic standpoint, these two types of companies should face the sameissues and supply chain practices adopted for one, should be equally effective for theother. However, this may not be the case since the distributor stage is an intermediatestage in the supply chain while the retailer stage is typically the final stage before thecustomer.

The strictly dyadic treatment of the supply chain into the supply and distributionsides also prevents the inclusion of other roles, which play a significant role in theeffectiveness of supply chain. For instance, some recent research studies have startedlooking at service supply chains. The aspects of service supply chains emphasize therelative importance of non-physical flows within the supply chain. Prior research onservice aspects in supply chains has been limited, with only a few studies specificallyassessing the importance of services as a separate supply chain related construct (Fieldand Meile, 2008; Sengupta et al., 2006; Ellram et al., 2004).

Expanding the scope of inquiry to include supply chain position allows us to examinethese additional stages for divergence in the effectiveness of specific SCM practices,which is a significant contribution to the existent body of knowledge. More specifically,the main contributions of this study are twofold:

(1) it extends the results from previous studies (Frohlich and Westbrook, 2001,2002; Kim, 2006; Li et al., 2005b) which have largely treated supply chains in adyadic manner; and

(2) it introduces analysis on supply chain roles which have not been examinedextensively in extant research.

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105

The results of the study not only provide additional insights into effectiveness of SCMpractices from a research standpoint, the results also provide contextual implicationsto practicing managers who are more interested in knowing what specific SCMpractices improve performance for their type of company.

The remainder of this paper is organized as follows. The next section discusses therelevant literature supporting the proposed research framework, commonly researchedSCM practices and the linkage of practices to performance. Next, the researchhypotheses are proposed and the methodologies used to test them are presented. Finally,we summarize key findings and conclude with a discussion of limitations and futuredirections for research.

Literature review and research frameworkIn many of the initial SCM research studies, the supply chain was viewed simply as anextension of traditional areas such as operations, purchasing and logistics. This myopicview considered a limited set of organizational stakeholders and issues in the analysis ofSCM practices. However, over the past decade, SCM research has evolved and broadenedto encompass a variety of perspectives such as supplier relationships, supply chainnetwork structure and collaboration (Tan et al., 1999, 2002; Croom et al., 2000; Tan, 2002;Chen and Paulraj, 2004a, b; Chen et al., 2004; Cigolini et al., 2004; Frohlich andWestbrook, 2001, 2002; Ho et al., 2002; Giannakis and Croom, 2004; Lejeune and Yakova,2005; Li et al., 2005b; Kampstra et al., 2006; Sandberg, 2007; Narasimhan et al., 2008).

Extensive literature reviews have examined a variety of issues pertaining to SCM.Chen and Paulraj (2004a, b) identify the growth of SCM practice from fields such aspurchasing, logistics, operations, organizational theory, information systems andstrategic management. Sachan and Datta (2005) reviewed more than 400 paperspublished in three peer-reviewed journals, Journal of Business Logistics, InternationalJournal of Physical Distribution & Logistics Management, and Supply ChainManagement: An International Journal. While they conclude that survey-basedresearch still ranks as the most widely used methodology in supply chain and logisticsresearch, studies have increasingly adopted more advanced techniques and have beenmoving towards model building and testing. In addition, the authors highlight the needfor looking at all the firms in the value chain as a single entity which partially hints to theeffect of the position of the firms in their respective supply chains and whether this hasoverall effects on strategies and practices. Ho et al. (2002) highlight major conceptualgaps in the current research. They discuss the need to operationalize and model SCMconstructs so they may be viewed from a business process perspective with betterperformance as a desired objective. Their research sought to highlight the need fordefinitive frameworks for managers to put into practice. In a similar manner, Lejeuneand Yakova (2005) propose a typology of supply chain configurations and attempt tolink terms and concepts previously used to separately describe various forms of a supplychain. This approach of developing frameworks is continued in Li et al. (2005b) wherethe authors use an empirical survey to validate six dimensions of SCM practices relatingto strategic supplier partnership, customer relationship, information sharing,information quality, internal lean practices and postponement. Cigolini et al. (2004)propose a normative conceptual framework for SCM practices and a contingency modelto assist managers in matching demand characteristics with supply chain requirements.In the resulting demand-supply matrix, demand was mapped to the stage of the

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product life cycle and the associated supply chain requirements were classified asefficient, lean or quick supply chains. For each normative level of the associated SCMpractices, they proposed management tools and techniques such as just-in-time (JIT),network design, distribution resource planning, transportation optimization, orderingand replenishment policies. Giannakis and Croom (2004) propose a 3S modelemphasizing three dimensions including:

(1) the synthesis of the business and resources network;

(2) the synergy between different participants in the network; and

(3) the synchronization of operational decisions related to the control of theproduction and delivery of goods and services.

Three levels of analysis, dyadic, chain and network are included in the classificationsystem proposed. Similar to other research studies, common management techniquesare proposed to help improve supply chain operations. Kim (2006) uses results of anempirical survey to develop a framework to assess the SCM integration and SCMpractices and the ensuing linkage to competitive capability. A companys integrationconsiders suppliers, customers and organizational cross-functional integration.A similar perspective regarding knowledge sharing among supply chain partnerswas examined by Wang et al. (2008). They analyze the gap between the theoreticalbenefits of mutual knowledge sharing among supply chain partners and the practicalcomplexity of achieving such a synergy because of various complexities in theinteraction processes between companies. Using a case-based methodology, theydevelop a model to enhance knowledge sharing among supply chain partners. Mentzeret al. (2001) conducted a comprehensive analysis of the various definitions and aspectsunder the umbrella of SCM. They define SCM as a comprehensive model whichencompasses inter-functional and inter-corporate coordination under an envelope ofglobal environment and a multi-tiered supply chain. Clearly, multiple studies haveshown the importance of interaction among the supply chain members as a way toimprove the competency of all supply chain partners.

While existing studies have extended the theoretical understanding of supply chainrelated issues and have highlighted the need for better interaction among the supplychain members, the question of whether the SCM practices are equally effective acrossall stages of the supply chain has not been explored beyond a few studies. Frohlich andWestbrook (2001, 2002) used an empirical survey to test whether manufacturers shouldhave different integration practices between suppliers and customers. In addition, theydetermined that the extent of integration with the customers and suppliers could bedifferent, with a higher level of integration usually associated with better performance.Although Frohlich and Westbrook (2001, 2002) contributed to understanding thenuances of integration practices, the treatment of suppliers and/or customers did notdifferentiate with respect to the specific position in the supply chain.

The differences in supply chain and organization performance across various stagesin the supply chain have been implicitly analyzed in a number of other studies.Simulation models were used in Zhao and Xie (2002) and Zhao et al. (2002a) to examinethe impact of forecasting and information sharing in a supply chain. Both studiesconsidered various independent variables such as demand patterns, informationsharing and forecasting related variables. While the dependent variables were totalcosts for the retailers, total cost for the supplier and the total cost for the supply chain.

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107

In both studies, the results showed that forecasting parameters significantly influencedthe performance of the supply chain and the value of information sharing. In Zhao et al.(2002b), results of another simulation model were presented to show the impact ofinformation sharing and ordering coordination on the performance of a supply chainwith one capacitated supplier and multiple retailers under demand uncertainty. Thismodel simulated inventory replenishment decisions by the retailers and productiondecisions by the supplier under different demand patterns and capacity tightness.Similar to the other studies, the results indicated that information sharing results insignificant benefits to the supplier and the entire supply chain. However, across all threestudies some of the scenarios simulated suggested that the retailers costs actuallyincreased. Therefore, the authors recommend that an incentive should be provided to theretailers to encourage and motivate partners to share information in the supply chain.These studies highlight the potential differences between a supplier and a retailer interms of the benefits of information sharing.

Singh et al. (2005) in their study of the Australian automotive supply chain highlightimportant issues related to logistics and information sharing. An exploratory study isconducted with vehicle manufacturers, component suppliers, industry bodies andgovernment agencies. Specific issues highlighted include low volume of production,threat of obsolescence, supplier relationship, supplier development, unavailability oflogistics providers and inadequacy of information systems. While several of these issuesrelate to some of the supply chain factors examined in our study, it also highlights theneed for examining the importance of the different factors based on the roles played byspecific companies. For instance, infrastructure issues related to logistics play asignificant role in the suppliers performances while these have a limited effect on themanufacturers. This study illustrates differences in the effectiveness of supply chainpractices based on the specific role of a company in the supply chain. This perspective isalso shown by Lau et al. (2004) where they examine the impact of information sharing oninventory replenishments. In their study, three stages of the supply chain are modelled:manufacturer, distributor and retailer. They conclude that there is a role-based differencein the effectiveness of the replenishment practices. For instance, it is more beneficial tohave information sharing among downstream partners (like retailers and distributors)than upstream partners (like distributors and manufacturers). They also analyzed theseeffects with regard to operating costs and conclude that information sharing initiativesshould be first initiated with downstream partners. Downstream partners hold moreinventory than upstream partners and the cost of backlog is also higher with thedownstream partners. With the upstream partners, the accuracy of demand informationdecreases resulting in any benefits of information sharing with the upstream partners tobe less significant. Therefore, it is more beneficial to initiate information sharing with thedownstream partners. Information sharing with supply chain partners is one of thesupply chain practices examined in our study and Lau et al.s (2004) study shows thatthe effects of information sharing differ by the specific role or position of the company inthe supply chain; the same perspective as the one undertaken in our research.

In this study, we extend the results from previous research by viewing the supplychain role of a company as a moderating factor in determining the relationship betweenspecific SCM practices and organizational performance. The supply chain role is definedas the position of a company in its supply chain including the roles of: manufacturer,distributor, retailer and service provider. The supply chain role of manufacturer

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includes component suppliers, fabricators and final assemblers of products. The supplychain role of distributor includes wholesalers, distributors and logistics companies. Thesupply chain role of retailer includes organizations which are positioned as the last linkin the supply chain interacting with the final consumer. Finally, the supply chain role ofservice provider includes organizations which provide an array of supporting services tothe other supply chain partners, such as utilities, construction, information services andprofessional services. Analyzing these four supply chain roles as a moderating factorsignificantly expands the results from previous research studies on the effect of SCMpractices on organizational performance. In addition, the inclusion of service providersintroduces a supply chain role that has received sparse coverage in extant research.

In order to examine the effect of specific SCM practices on organizational performanceand to determine whether the supply chain role moderates this relationship, this studyconsiders commonly advocated SCM practices used in previous research. The intentis not to provide exhaustive coverage of all possible SCM practices, but rather to assessthe relationship between a few commonly advocated practices and the resultingorganizational performance when moderated by the supply chain role. The resultsidentify context-dependent relationships and thereby identify rich pools of inquiry forfuture context-specific analysis.

The proposed research framework, shown in Figure 1, illustrates the specific SCMpractices examined in this study, their proposed relationship to organization performanceand the proposed moderating effect of the four supply chain roles on this relationship.

One of the objectives of this study is to exploring the relationship between commonsupply chain practices and organizational performance. We assume that a higher level ofadoption of a supply chain practice has a positive relationship to organizationalperformance. Since we examine six specific supply chain practices, the following sixhypotheses are proposed:

H1-H6. A higher level of adoption of the six supply chain practices:(a) SHARE, (b) RELATION, (c) PLAN, (d) INTERNET, (e) SOURCE and(f) DISTRIBUTION is positively related to organizational performance.

The primary objective of this research is to examine whether the supply chain role of anorganization (manufacturer, distributor, retailer or service provider) has a moderating

Figure 1.Research framework

Dependent variableOrganizationalperformance

Moderating factorSupply chain roles

ManufacturerDistributorRetailerService provider

H1-H6

H7-H10Independent variables

SCM PracticesShare-Information sharingRelation-Long term relationshipsPlan-Advanced planning systemsInternet-Leveraging the internetSource-Supply network structureDistribution-Distribution network structure

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109

effect on the positive relationship between the SCM practices and organizationalperformance. Accordingly, the following additional hypotheses are proposed:

H7. The supply chain role of manufacturer in an organization moderates thepositive relationship between the SCM practices: (a) SHARE, (b) RELATION,(c) PLAN, (d) INTERNET, (e) SOURCE and (f) DISTRIBUTION andorganizational performance.

H8. The supply chain role of distributor in an organization moderates the positiverelationship between the SCM practices: (a) SHARE, (b) RELATION,(c) PLAN, (d) INTERNET, (e) SOURCE and (f) DISTRIBUTION andorganizational performance.

H9. The supply chain role of retailer in an organization moderates the positiverelationship between the SCM practices: (a) SHARE, (b) RELATION,(c) PLAN, (d) INTERNET, (e) SOURCE and (f) DISTRIBUTION andorganizational performance.

H10. The supply chain role of service provider in an organization moderates thepositive relationship between the SCM practices: (a) SHARE, (b) RELATION,(c) PLAN, (d) INTERNET, (e) SOURCE and (f) DISTRIBUTION andorganizational performance.

It is important to note that hypotheses H1 through H6 are treated as the first step in theanalysis and we do not draw conclusive suggestions or managerial implications fromthe associated results. Hence, results from H1 through H6 are used as the basis foranalyzing the moderating effect of the supply chain role indicated in subsequent H7through H10. The same positive relationships between a higher adoption level on thesupply chain practices and organizational performance, as stated in H1 through H6, isalso assumed under H7 through H10. Therefore, when drawing conclusions andmanagerial implications of the study, the discussion of the results combines the two setsof hypotheses.

Supply chain practicesThis study considers some of the more widely advocated SCM practices examined inprior SCM literature. These include information sharing between supply chain partners(SHARE), building long-term relationships between supply chain partners(RELATION), using advanced planning systems (PLAN), leveraging internet-basedtechnology (INTERNET), design of the supplier network (SOURCE) and design of thedistribution network (DISTRIBUTION). The overall objective is to consider arepresentative set of common SCM practices rather than an exhaustive list of all suchpractices. Table I displays a taxonomy of the literature used to create the specific items tomeasure each of the six SCM practices. Based on prior research studies, three to six suchitems were commonly used to evaluate the level of adoption of the six practices byrespondent organizations.

Information sharing (SHARE)Information sharing (SHARE) with supply chain partners has been an area of focusin SCM research primarily due to the perceived benefits of enhanced coordination.Prior research has examined the scope and process of information sharing related

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SC

Mp

ract

ices

/org

aniz

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nal

per

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item

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Rel

ated

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con

stru

cts

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tre

sear

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ence

s

Info

rmat

ion

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ing

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shar

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form

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non

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ento

ryle

vel

sw

ith

our

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ply

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ers

Deg

ree

tow

hic

hre

tail

ers

shar

efo

reca

stan

dot

her

info

rmat

ion

wit

hsu

pp

lier

s

Hsu

etal.

(200

8),

Zh

aoetal.

(200

2a,b

)

SH

AR

E2

We

shar

efo

reca

sts

ofcu

stom

erd

eman

dw

ith

our

sup

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artn

ers

Ex

ten

tto

wh

ich

pro

pri

etar

yan

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itic

alin

form

atio

nis

com

mu

nic

ated

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ply

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np

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er

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(200

5)

SH

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rom

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ns

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Info

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ith

sup

pli

ers

Hsu

etal.

(200

8),

Kim

(200

6)

SH

AR

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We

shar

ein

form

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nel

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onic

ally

wit

hou

rsu

pp

lych

ain

par

tner

s

Sh

arin

gd

iffe

ren

tin

form

atio

nw

ith

sup

pli

ers

and

cust

omer

s(i

nte

gra

tiv

eac

tiv

itie

s)

Fro

hli

chan

dW

estb

rook

(200

1)

Com

mu

nic

atio

nF

ield

and

Mei

le(2

008)

Ch

enan

dP

aulr

aj(2

004a

,b

)V

alu

eof

info

rmat

ion

shar

ing

Fie

ldan

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8)L

ietal.

(200

5)(S

CM

:IJ)

Lon

g-t

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rela

tion

ship

s(R

EL

AT

ION

)R

EL

AT

ION

1W

ech

oose

sup

pli

ers

bas

edu

pon

thei

rfl

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ilit

yan

dsp

eed

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ery

Str

ateg

icsu

pp

lier

par

tner

ship

defi

ned

aslo

ng

-ter

mre

lati

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etw

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ion

and

its

sup

pli

ers

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(200

5)

RE

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TIO

N2

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bu

ild

lon

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ben

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lier

s

Lev

elof

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ersh

ipw

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sup

pli

ers

Fie

ldan

dM

eile

(200

8),

Kim

(200

6)

RE

LA

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We

neg

otia

telo

ng

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999)

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tion

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and

rela

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clos

enes

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hoi

and

Har

tley

(199

6)

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erm

rela

tion

ship

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004a

,b

)A

dv

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lan

nin

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ing

raw

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nis

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din

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tan

dm

anu

fact

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ng

tech

nol

ogy

Kim

(200

6)

(continued

)

Table I.Taxonomy of selected

literature for the six SCMpractices and

organizationalperformance

Supply chain role

111

SC

Mp

ract

ices

/org

aniz

atio

nal

per

form

ance

Su

rvey

item

sS

urv

eyst

atem

ents

Rel

ated

stat

emen

tsor

con

stru

cts

from

pas

tre

sear

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ence

s

PL

AN

2M

anag

ing

wor

k-i

n-p

roce

ssin

ven

tori

esR

eal-

tim

ese

arch

ing

ofin

ven

tori

esK

im(2

006)

PL

AN

3U

sin

gm

ater

ial

req

uir

emen

tsp

lan

nin

g(M

RP

)sy

stem

sIn

teg

rati

ve

inv

ento

rym

anag

emen

tK

im(2

006)

PL

AN

4U

sin

gE

RP

syst

ems

Deg

ree

ofIT

adop

tion

(ten

item

sre

late

dto

ED

I,E

RP

,re

al-t

ime

info

rmat

ion

syst

ems)

Ak

yu

zan

dR

ehan

(200

9),

Jin

(200

6)

PL

AN

5U

sin

gco

llab

orat

ive

pla

nn

ing

,fo

reca

stin

g,

and

rep

len

ish

men

t(C

PF

R)

Info

rmat

ion

tech

nol

ogy

Ch

enan

dP

aulr

aj(2

004a

,b

)

Au

tom

ated

syst

ems

(on

-lin

ean

din

teg

rate

d)

Cig

olin

ietal.

(200

4)

PL

AN

6U

sin

gac

tiv

ity

-bas

edco

stin

g(A

BC

)ac

cou

nti

ng

met

hod

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per

atio

ns-

orie

nte

dap

pli

cati

ons

(in

clu

din

gE

RP

,C

PF

R)

San

der

san

dP

rem

us

(200

2)

Lev

erag

ing

the

inte

rnet

(IN

TE

RN

ET

)IN

TE

RN

ET

1S

har

ing

info

rmat

ion

over

the

inte

rnet

wit

hsu

pp

lych

ain

par

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s

Su

pp

ly-s

ide

inte

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ctic

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sin

gw

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ncl

ud

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and

and

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ento

rytr

ack

ing

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der

man

agem

ent

via

the

inte

rnet

)

Gim

enez

and

Lou

ren

co(2

008)

Fro

hli

chan

dW

estb

rook

(200

2)G

imen

ezan

dL

oure

nco

(200

9)

INT

ER

NE

T2

Pu

rch

asin

gm

ater

ial

and

serv

ices

via

the

inte

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Cu

stom

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ide

inte

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ctic

esu

sin

gw

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y(i

ncl

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esor

der

man

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ent,

sell

ing

and

dem

and

fore

cast

ing

via

the

inte

rnet

)

Fro

hli

chan

dW

estb

rook

(200

2)G

imen

ezan

dL

oure

nco

(200

9)

INT

ER

NE

T3

Sel

lin

gp

rod

uct

san

dse

rvic

esv

iath

ein

tern

etW

eb-b

ased

mar

ket

ing

orie

nte

dap

pli

cati

ons

San

der

san

dP

rem

us

(200

2)

(continued

)

Table I.

IJPDLM41,2

112

SC

Mp

ract

ices

/org

aniz

atio

nal

per

form

ance

Su

rvey

item

sS

urv

eyst

atem

ents

Rel

ated

stat

emen

tsor

con

stru

cts

from

pas

tre

sear

chR

efer

ence

s

Su

pp

lyn

etw

ork

stru

ctu

re(S

OU

RC

E)

SO

UR

CE

1D

ecid

ing

wh

eth

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Table I.

Supply chain role

113

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Table I.

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to operational issues such as inventory, forecasting, orders and production plan issues(Chen and Paulraj, 2004a; Kim, 2006; Lee and Whang, 1999; Li et al., 2005a, b; Zhao andXie, 2002; Zhao et al., 2002a,b; Narasimhan et al., 2008; Hsu et al., 2008; Sezen, 2008). Mostof the research in this area is based on a simplified model of a supply chain consisting oftwo stages, single product or a limited number of supply chain partners. Some of themore recent studies have examined other issues with information sharing including thepower of the relationship between the supply chain partners (Williams and Moore, 2007)and the extent of value realized by different partners within a supply chain (Lumsdenand Mirzabeiki, 2008). The latter study uses published papers and textbooks, andadditional interviews with practitioners in different segments of the supply chain todetermine that different type of information are valued differently by the supply chainpartners. In this research, we explore a similar need that the specific supply chain role ofa company may lead to different perspectives on information sharing and we use a morerobust definition of information sharing by including factors related to inventory levels,demand forecasts and pricing information. Specific items examined under this factorinclude information sharing on inventory levels, forecasts and price promotions; and theextent to which information is shared electronically with the supply chain partners.These modifications were appropriate to make the survey items more relevant to all ofthe manufacturing and service-oriented supply chain roles considered in this researchand are similar to those studied in previous research dealing with information sharingamong supply chain partners in both manufacturing and service areas (Field and Meile,2008; Sezen, 2008; Hsu et al., 2008; Frohlich and Westbrook, 2001).

Long-term relationships (RELATION)Building long-term relationships (RELATION) within a supply chain allows forfamiliarity and the opportunity for mutual benefit that correspond with a greater level ofcoordination in business decisions (Hahn et al., 1983; De Toni and Nassimbeni, 1999;Choi and Hartley, 1996). Maintaining extended relationships with partner companies is apillar of JIT principles. Prior studies have assessed long-term relationships from severalperspectives including strategic supplier partnership, long-term sourcing policy andgeneral structures of long-term relationships (Chen and Paulraj, 2004a, b; De Toni andNassimbeni, 1999; Choi and Hartley, 1996; Kim, 2006; Lee and Whang, 1999; Li et al.,2005b; Zhao et al., 2002b).

Advanced planning systems (PLAN)The high adoption patterns of sophisticated advanced planning systems (PLAN)suggest these systems are commonly advocated tools for improving supplychain-related efficiencies. Companies such as SAP, Oracle and others have beendeveloping state-of-the-art systems over the last decade. Prior to the year 2000 (Y2K),advanced planning and inventory control systems were perceived by many to be a coretool of SCM. More recently, the role of enterprise resources planning (ERP) systems inintegrating business processes was found to be relevant and important to remaincompetitive in a web-based environment (Akyuz and Rehan, 2009; Narasimhan et al.,2008; Auramo et al., 2005). However, in the post-Y2K environment and with thedownturn in technology-related equities, the central role of such advanced planningsystems has evolved to be less certain in non-internet dependent environments. In anycase, it is without dispute that the implementation of advanced planning systems

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represents a strategic decision requiring a large investment of capital and managerialfocus. In addition, with greater coordination between supply chain partners comes theneed for applying the advanced planning systems (which were largely developed andinstituted internally by companies) to an inter-firm environment ( Jonsson et al., 2007).Simultaneously, companies need to determine the purpose of using these systems froman inter-firm perspective and they need to identify the key drivers for such systems(Karkkainen et al., 2007). As a result, the costs and benefits of these investments are nowbeing weighed more carefully by managers. This factor has been considered in otherSCM frameworks in a variety of forms including use of advanced management andmanufacturing technology, integrative inventory management systems, degree of ITadoption, scope of automated systems and operations-oriented applications (Chen andPaulraj, 2004a, b; Cigolini et al., 2004; Jin, 2006; Kim, 2006; Sanders and Premus, 2002),and our survey instrument items related to this practice includes a combination of thespecific aspects of the practices examined in previous studies.

Leveraging the internet ( INTERNET)Even beyond its role in advanced planning systems, the evolution and leveraging of theinternet (INTERNET) has served as one of the main technological developmentssupporting increased collaboration and coordination among supply chain partners.In recent years, companies have continued to adopt internet-based collaboration to makeeffective decisions concerning forecasts, inventory and orders. Multiple studies over thelast few years have examined the impact of the internet on the different processes thatconstitute SCM. Gimenez and Lourenco (2008) conducted a literature review based studyfor the period 1995-2005. They found that the effect of internet on SCM has beenrecognized as an important topic of research with information flows, e-procurement ande-fulfilment being the main areas of research. Therefore, it is important to analyze thisfactors unique role as a supply chain practice in affecting organizational performance.While Frohlich and Westbrook (2002) also examined this factor, they only differentiatedbetween manufacturing and service-oriented organizations. The broader definition ofsupply chain roles in our study allows a deeper understanding of this factor and itsinfluence on organizational performance. The growth in internet usage and this factorsimportance in supply chain research has been reflected in other studies throughsupply-side integration practices using web technology, customer-side integrationpractices using web technology and web-based marketing-oriented applications(Gimenez and Lourenco, 2008; Sanders and Premus, 2002).

Supply network structure (SOURCE)The design of the supply network structure (SOURCE) refers to practices related to theupstream supply chain of a company. Typically, network structure implies the numberof suppliers and the number of stages in the supply chain (Frohlich and Westbrook,2001; Li et al., 2005b). While Frohlich and Westbrook (2001) consider the extent ofintegration with suppliers and customers, they do not consider some of the factors thatmay affect the nature and extent of integration. Tracey et al. (2005) consider thesupply-side structure in their SC framework through the outside-in capabilities. Otherstudies have also considered logistics initiatives, location policies, supplier selection,supply base reduction, sourcing issues and the network structure (Chen and Paulraj,2004a, b; De Toni and Nassimbeni, 1999; Kim, 2006; Sezen, 2008). Likewise, recent studies

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have also looked at the aspect of the supplier network from an extended networkstandpoint (Choi and Wu, 2009; Choi and Kim, 2008; Narasimhan et al., 2008). Thesestudies have attempted to extend the dyadic buyer-supplier relationship to a moreextended network on a triadic mode where the suppliers suppliers are also included inthe analysis. Continuing this theme, this research assumes the supply network structurefocuses on the upstream portion of the supply chain and includes specific items related tosupplier relations and outsourcing. The items could be important factors when oneconsiders both the dyadic network and also the extended supplier network.

Distribution network structure (DISTRIBUTION)The design of the distribution network structure (DISTRIBUTION) has been examinedin a variety of frameworks and directly relates to the downstream supply chain for acompany (Frohlich and Westbrook, 2001; Kim, 2006; Li et al., 2005b; Tracey et al., 2005).Some frameworks have focused on customer-related practices, while others haveemphasized distribution in the downstream portion of the supply chain. Kim (2006)proposes a construct which considers the organizations integration with its customers.Tracey et al. (2006) incorporate distributors through the inside-out capability construct.Other studies have also considered logistics initiatives, location policies, integrativeactivities with customers, warehouse network redesign and transportation optimization(Cigolini et al., 2004; Frohlich and Westbrook, 2001; Kim, 2006; Tracey et al., 2005).Shang et al. (2009) apply a mixed integer model to not only reduce overall costs but alsoto improve service levels for Glaxo Smithkline and Beecham. Their study shows thataspects related to facilities location and choosing the most effective distribution networkis still relevant to companies. Based on these studies, our research uses a generalizeddefinition of the distribution network construct (DISTRIBUTION) and encompassesfactors such as facility location, inventory positioning and the choice of alternativedistribution channels.

Organizational performanceAlthough prior research suggests there is a direct link between the level of adoption ofSCM practices and organizational performance, there have been various definitions oforganizational performance, with some studies emphasizing operational measures,while others stressing financial measures. For example, Li et al. (2005b) use deliverydependability and time to market as performance measures, while firm performancedefined by sales growth, market share growth and profitability are used in other studies(Narasimhan et al., 2008; Narasimhan and Kim, 2002). Many studies have selected acombination of pertinent operational and financial measures to reflect overallorganizational performance. For example, Vereecke and Muylle (2006) use factoranalysis to extract five components of performance related to delivery, cost, flexibility,procurement and quality. Tracey et al. (2005) measure performance through fourseparate dimensions including perceived value, customer loyalty, market performanceand financial performance. Similarly, Tan et al. (2002) use six items for performanceincluding product quality, customer service, competitive position, market share,average selling price and return on assets. Chen and Paulraj (2004a, b) use supplierperformance and buyer performance to assess the financial performance of the buyingfirm. Vickery et al. (2003) use customer service performance followed by financialperformance as the performance constructs. Finally, Jin (2006) assesses operational

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performance via three levels of performance criteria: strategic, operational and financial.Strategic performance is measured by market share and sales growth, operationalperformance is measured by lead-time performance and financial performance isassessed through return on investments and return on sales.

Based on other SCM studies, Table I displays how this research defines organizationalperformance as a combination of operational and financial results as measured bythe respondents perceived performance relative to their competitors. The operationalmeasures selected are commonly used to assess operational excellence and measures acompanys relative performance with its main competitors on three competitive priorities:speed, delivery and quality. Financial performance is measured by the companys cost-and profit-related performance as compared to their direct competitors. These metrics arecommonly used to assess an organizations financial performance. The performancemeasures in this study were chosen for their applicability across a broad spectrum ofindustries. Given the diversity of the respondents in this study, the dependent variablewas designed to capture evidence of an organizations perceived performance relative totheir direct competitors to avoid confounding results with disparate inter-industrystandards of performance. Similar methods have been used by several other studies(Tan et al., 1999; Gunasekaran et al., 2001; Sanders and Premus, 2002; Tan, 2002; Lockamyand McCormack, 2004). For instance, Tan (2002) proposes that due to a lack of consensusregarding a valid cross-industry measure of corporate performance, performance can beoperationalized by managements perceptions of a firms performance in comparison tothat of major competitors.

Research methodologyThis study is part of a larger research project exploring supply chain-related practices,their relevance to managers and their impact on firm performance. This arm of the studycompares common SCM practices among working managers across supply chain roles.The respondents were drawn from the non-academic, North American membership ofthe ISM. Data collection for this study included several key items of interest for this studyincluding the primary industrial sector, supply chain role, company demographics, SCMpractices and organizational performance. All items were measured on a seven-pointLikert scale with higher scores indicating a higher level of respondent agreement that theitem accurately describes their organization.

The content validity of each construct was ensured through pre-testing of thequestionnaire and structured interviews with academic experts and managers in the field.A two-step process was used to develop and refine the survey instrument. In the first step,a panel of SCM academic experts examined the questionnaire items to check for relevancyor possible ambiguity in the wording of specific items. There were six academic expertswho participated in this step. In the second step, a panel of supply chain professionalscompleted the survey. Subsequent interviews with these individuals assessed whetherthey found any non-relevant or ambiguous items. The supply chain professionals weredrawn from two separate samples from two different graduate MBA classes in an urbanuniversity in the USA. The sample was chosen such that all participants were experienced,working professionals in various functions associated with their respective companiespurchasing, operations or distribution functions. Feedback from this two-step processresulted in minor changes to the survey instrument.

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The initial mailing consisted of 666 instruments. Each mailing of the surveyinstrument included a cover letter stating the purpose of the overall study. Several stepswere taken to maximize the response rate, including the inclusion of a postage-paidbusiness reply envelope, a financial incentive to complete the survey and the use of afollow-up letter to non-respondents. All of these steps are considered effective ways toincrease response rates in other operations management research studies (Frohlich,2002). Ten survey packets were returned by the postal service as undeliverable.Approximately, 15 percent of the targeted recipients replied within the first four weeks.After the second mailing, a total of 161 useable surveys were received, which representsa response rate of 24.2 percent. For this research, another 16 surveys were eliminated dueto incomplete responses, leaving 145 responses (21.8 percent) as the basis for thisanalysis. The possibility of non-response bias was investigated through a series oft-tests comparing the responses from the first and second mailing. The t-tests yielded nostatistically significant differences between the early and late responders suggestingthat non-response bias was not an area of concern in this study (Tan, 2002; Lambert andHarrington, 1990; Armstrong and Overton, 1977). Factor analysis was then conductedon the data using the extraction method of principal component analysis followed by avarimax rotation (Tan, 2002).

The data were analyzed using hierarchical regression and the relative weights (RWs)technique developed by Johnson (2000). The primary distinction between the methods isthat stepwise regression introduces independent variables into a regression equationbased upon their additional contribution to explained variance. In contrast, the RWstechnique examines the multi-linear relationships with all of the proffered independentvariables in the equation. The RWs technique then parses the unique contribution ofeach independent variable to the observed variance. To facilitate comparison betweenthe methods, we also report the explained variance (R 2) for a standard multipleregression with all variables introduced along with the results of the RWs technique.

The application of the RWs technique is appropriate when simultaneously examiningthe effect of multiple SCM initiatives on organizational performance. This is because it iscommon for firms to pursue multiple SCM-related practices at the same time. As a result,there is a possibility that the overlapping effects and interactions of the relationships ofinterest will affect the results of the stepwise regression. The RW technique is robustagainst the presence of multi-collinearity between the independent variables. Under suchconditions of potential multi-collinearity, the RW method is suggested for scenarioswhen there is no inherent ordering of the predictors and the researcher is interested in therelative contribution each variable makes to the prediction of a dependent variable( Johnson, 2000). It should also be noted that when using multiple regression analysis, thestandardized regression coefficients are often assumed to suggest the relative importanceof the individual independent variables. However, these coefficients are only useful in thisregard when there are no significant correlations between the predictors. In the presenceof multi-collinearity, using standardized regression coefficients to explain the relativeimportance of the predictors may lead to erroneous interpretation (Johnson, 2000).

Johnson (2000) and Johnson and LeBreton (2004) provide detailed explanations of theRW indices and the associated calculations. They have also made the SPSS syntax file,RWEIGHT, publicly available to compute RWs (Johnson, 2001a). Subsequently,the RWs technique has been used in a variety organizational settings and researchstudies (Cochran, 1999; Johnson, 2000, 2001b; Lievens et al., 2003).

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Results and discussionTable II displays the profile of the respondents by supply chain role, number of full-timeemployees and annual sales volume. Similar organizational dimensions have beenreported and used in other SCM studies (Li et al., 2005b; Tan et al., 2002). The externalvalidity of the collected data was considered by testing the data for normality to assesswhether a bias towards a specific company size or annual sales revenue was present.The results show there is considerable variation and lack of bias in the data thusallowing the results to be generalizable.

Although the survey items are oriented towards measuring the key supply chainpractices examined in the study, we needed to first analyze whether these statementsactually reflect a unitary construct. In other words, multiple statements related to aspecific strategic factor should generate relatively consistent responses from thecompanies. For example, in the case of the information sharing (SHARE) factor, it isexpected that a company that shares information electronically with its supply chainpartners, will also share information regarding inventory levels, demand and pricepromotions. Although some variation may be expected across the four items, the moreconsistent the responses are, the more likely the factor actually measures a cohesiveconcept. In order to achieve this, a factor analysis was conducted on the survey items withfactors extracted using the principal component analysis followed by a varimax rotation.

The main criteria used to decide which factors would be used for further analysiswere that total variance explained had to be greater than 60 percent for all factorscombined; there should be a minimum of three variables per factor; factor loadings(eigenvalues) for each of the variables for a factor should be at least 0.45 (which is higherthan the normally accepted level of factor loads of 0.3 (Hair et al., 1998)); and theCronbachs alpha for each factor had to be at least 0.5 (Hair et al., 1998).

Table III displays the results of the factor analysis including the survey items thatload on each factor, the factor loadings (eigenvalues) and the value of the reliabilitycoefficient or the Cronbachs alpha. The specific SCM practices identified have a totalvariance explained greater than 60 percent and the factor loadings (eigenvalues) for eachof the items is at least 0.525. This threshold value corresponds to a 0.05 level of

Dimensions Levels Retailer DistributorServiceprovider Manufacturer Total

Number of full-timeemployees

1-500 employees 3 16 17 29 65 (44.8%)500-5,000employees

4 8 12 24 48 (33.1%)

Greater than5,000 employees

11 4 10 7 32 (22.1%)

Annual sales volume Less than $10million

1 1 7 6 15 (10.3%)

$10-$50 million 1 10 8 13 32 (22.1%)$50-$250 million 1 7 7 23 38 (26.2%)Greater than$250 million

15 10 17 18 60 (41.4%)

Total 18 28 39 60 145(12.4%) (19.3%) (26.9%) (41.4%) (100.0%)

Note: n 145Table II.Respondents profileby supply chain role

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significance, with an 80 percent power level for a sample size of 150 which is close to thesample size of 145 in this study (Hair et al., 1998). As shown in Table III, the values forCronbachs alpha for the SCM practices range from 0.608 to 0.878. The factor fororganizational performance was also extracted using a similar method. The fivestatements related to performance loaded onto a single factor. The factor loadings andreliability coefficients show a high level of reliability with a Cronbachs alpha value of0.812. The results show that there is a fairly high level of congruence among the itemsmeasuring a particular factor. The relatively high values of the Cronbachs alpha whichmeasures the reliability of the factors and the eigenvalues provides excellent validity tousing the factor scores for all subsequent analysis. For instance, the SOURCE factorrelates to the supply network structure and has a reliability coefficient of 0.776. The threeitems that load on the factor relate to: deciding whether, and how much, to outsource;selecting and certifying suppliers; and, rationalizing the supply base (e.g. strategicpartnering, vertical integration and single source supply). All three items relate to thesupplier-related practices followed by companies and hence, the loading is logical. Theloadings of the items on the other factors can also be explained in a similar manner.Based on these results, each respondents individual survey items under each extractedfactor are normalized by taking the average scores over the items and the resultingfactor scores are used as the independent variables.

SCM practices Items Factor loadings Cronbachs alpha

Information sharing (SHARE) SHARE1 0.678 0.795SHARE2 0.777SHARE3 0.756SHARE4 0.615

Long-term relationships (RELATION) RELATION1 0.525 0.608RELATION2 0.715RELATION3 0.746

Advanced planning systems (PLAN) PLAN1 0.708 0.878PLAN2 0.813PLAN3 0.805PLAN4 0.716PLAN5 0.615PLAN6 0.605

Leveraging the internet (INTERNET) INTERNET1 0.729 0.792INTERNET2 0.819INTERNET3 0.667

Supply network structure (SOURCE) SOURCE1 0.605 0.776SOURCE2 0.806SOURCE3 0.713

Distribution network structure(DISTRIBUTION)

DISTRIBUTION1 0.656 0.692DISTRIBUTION2 0.678DISTRIBUTION3 0.642

Organizational performance (ORGPERF) ORGPERF1 0.823 0.812ORGPERF2 0.860ORGPERF3 0.757ORGPERF4 0.649ORGPERF5 0.661

Table III.Factor analysis forSCM practices and

organizationalperformance

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Table IV displays the means and standard deviations for the entire sample and thefour supply chain roles. These overall results suggest the most commonly pursued SCMpractices include design of the supply network (SOURCE) and building long-termrelationships with supply chain partners (RELATION). The least commonly pursuedSCM practices were information sharing with supply chain partners (SHARE) and thedesign of the distribution network (DISTRIBUTION). It is also interesting to note that areview of the standard deviation suggests that there is the most agreement of the value ofbuilding long-term relationships (RELATION) and the least agreement on the emphasisplaced on information sharing (SHARE).

There are also differences in the means and standard deviations among the foursupply chain roles. Some of the differences are intuitive. For example, the mean scores ofservice providers are lower than those of the other supply chain roles for the design of thedistribution network (DISTRIBUTION) and the use of advanced planning systems(PLAN), which reflects the absence of physical goods in the value propositions.However, other findings were unexpected, such as the low emphasis placed on sharinginformation with supply chain partners (SHARE) by the service providers. This mayindicate that service providers remain on the periphery of their respective supply chains.It is also interesting to note that the observed standard deviations of the retailers arehigher than those for the other supply chain roles on four of the six constructs (SHARE,RELATION, INTERNET and DISTRIBUTION). This may indicate that compared withthe other roles examined, there is less agreement on what the appropriate SCM practicesare within the retail sector. The results reported in Table IV suggest that the level ofadoption on the commonly advocated SCM practices vary according to the supply chainrole of respondents.

The inter-correlations of each of the six SCM practices are shown in Table V. Thecorrelations between the independent variables range from 0.083 to 0.398. For example,the significant correlation between RELATION and SHARE of 0.353 shows thatcompanies which have long-term relationships with their supply chain partners wouldbe more likely to share information with these partners. Likewise, the correlationbetween INTERNET and PLAN is significant at 0.366. This points to the strong synergybetween using web-based business practices and using advanced planning systems.Given that many of the correlations shown in Table V are significant, it also points to the

Supply chain rolesAll supplychain

members Retailers DistributorsService

providersManu

facturersSCM practices M SD M SD M SD M SD M SD

SHARE 3.87 1.54 3.90 1.75 3.87 1.55 3.50 1.61 4.13 1.42RELATION 5.24 1.09 4.78 1.63 5.20 0.85 5.44 1.09 5.28 0.96PLAN 4.29 1.48 4.38 1.49 4.34 1.34 3.21 1.54 4.94 1.07INTERNET 4.17 1.52 3.99 1.61 4.42 1.34 4.40 1.57 3.95 1.55SOURCE 5.32 1.19 5.06 1.09 5.07 1.24 5.17 1.44 5.61 0.97DISTRIBUTION 3.88 1.41 4.20 1.57 4.26 1.37 3.10 1.43 4.11 1.18

Notes: Mean M; standard deviation SD; scores on a seven-point Likert scale; higher scoresindicate higher preference for the specific SCM practices

Table IV.Descriptive statisticsfor the SCM practice

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notion that many of these practices are undertaken simultaneously and some aspects ofa particular practice overlap with aspects of a different practice.

From a statistical perspective, presence of correlation between independent factors (likethe supply chain practices examined here) results in the possibility of multi-collinearitywhere the independent factors are not really independent of one another. However, in ourstudy, although many of the observed correlations are statistically significant, bivariatecorrelations below 0.80 are not considered indicative of the presence of multi-collinearity inthe dataset (Mason and Perreault, 1991). This means that the application of stepwiseregression remains appropriate under the evidence presented. Johnson (2000) hasrecommended using a RW analysis to supplement the interpretation of the regression resultsin such cases where the independent factors have significant inter-correlations. The RWsmethod provides additional explanatory power regarding the relative importance of specificrelationships between the factors and the dependent variable.

It is important to test for the discriminant validity of the factors since there aresignificant correlations among some of the independent factors and the scales of thefactors were compiled based on items derived from a number of previous researchstudies. Campbell and Fiske (1959) first introduced the concept of discriminant validityby stating that multiple factors should exhibit discriminant validity when a uniquefactor should not measure similar concepts as the other factors. In other words,measurement error should not exist between multiple factors. This was furtherconfirmed in a subsequent study (Campbell, 1960) where specific recommendations weremade to measure the correlation index between two factors, x and y, as equal to:

rxy

rxx:ryyp ;

where rxy is the correlation between factors x and y; and rxx, ryy are the reliabilitycoefficients of factors x and y, respectively. If the calculated value of the index is less than0.85, the two factors under consideration are distinct from each other and there isdiscriminant validity between the two factors. Table VI displays the results of the indexfor the test of discriminant validity on the SCM factors. The results show that the valuesrange from 0.114 (between PLAN and RELATION) to 0.508 (between SOURCE andINTERNET), which are considerably lower than the accepted threshold of 0.85.In addition, as suggested by Straub (1989), constructs are deemed to be different from oneanother if, during the factor analysis, the respective items load most heavily on differentfactors. This is true based on our results for the factor analysis reported in Table III.

SHARE RELATION PLAN INTERNET SOURCE DISTRIBUTION

SHARE RELATION 0.353 * * PLAN 0.349 * * 0.083 INTERNET 0.235 * * 0.187 * 0.366 * * SOURCE 0.253 * * 0.299 * * 0.237 * * 0.398 * * DISTRIBUTION 0.217 * * 0.179 * 0.379 * * 0.228 * * 0.338 * *

Notes: n 145; significance at: *p , 0.05, * *p , 0.01 levelsTable V.

Correlations forthe SCM practices

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123

In conclusion, the methods of Campbell and Fiske (1959) and Straub (1989) indicate thatthe SCM factors have discriminant validity and, in fact, measure separate constructs.

Table VII displays the results of five stepwise regression models designed toexplore the relationship between the six supply chain practices and organizationalperformance by supply chain role. The first regression model consists of all respondents.The remaining four regression models segregate supply chain member by theirrespective role: retailer, distributor, service provider and manufacturer. As shown inTable VII, four of the five regression models were found to be statistically significantwith R 2-values ranging from 0.174 to 0.624, which are similar to those reported in otherSCM research studies (Tan, 2002; Lockamy and McCormack, 2004).

These findings indicate thatH1 andH6 are accepted while H2-H5 are rejected. In theoverall model, the SCM practices of sharing information with supply chain partners(SHARE) and design of the distribution network (DISTRIBUTION) were found tosignificantly differentiate an organizations performance from its competitors. Withinthe sub-models, additional support was found that indicates that the supply chain

SHARE RELATION PLAN INTERNET SOURCE DISTRIBUTION

SHARE RELATION 0.507 PLAN 0.417 0.114 INTERNET 0.296 0.269 0.439 SOURCE 0.322 0.435 0.287 0.508 DISTRIBUTION 0.292 0.276 0.486 0.308 0.461

Note: n 145Source: aBased on Campbell and Fiske (1959)

Table VI.Values for test ofdiscriminant validitya

among the SCM practices

Supply chainrole Factors Coefficient

Significancelevela R 2 F

SignificanceLevelb

All supplychain members

Constant 3.036 0.000 * * * 0.174 14.974 * * * 0.000SHARE 0.205 0.001 * * *

DISTRIBUTION 0.209 0.001 * * *

Retailer Regression wasnot significant

Distributor Constant 3.460 0.002 * * * 0.624 13.274 * * * 0.000DISTRIBUTION 0.474 0.000 * * *

PLAN 0.385 0.001 * * *

RELATION 20.608 0.009 * * *

Serviceprovider

ConstantSHARE

2.9240.436

0.000 * * *

0.001 * * *0.282 14.527 * * * 0.001

Manufacturer Constant 1.700 0.035 * * 0.240 5.901 * * * 0.001DISTRIBUTION 0.247 0.028 * *

SHARE 0.180 0.052 *

RELATION 0.318 0.087 *

Notes: Significance at: *p , 0.10, * *p , 0.05 and * * *p , 0.01 levels; alevel of significance forindividual factors; blevel of significance for overall model

Table VII.Stepwise regressionresults by supplychain role

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practices of building long-term relationships with supply chain partners (RELATION)and utilization of advanced planning systems (PLAN) also significantly differentiate anorganizations performance relative to its direct competitors. However, evidence was notfound to support a positive relationship between leveraging the internet (INTERNET) ordesign of the supplier network (SOURCE) and differentiated performance.

When moderated by the supply chain role, the results of the stepwise regressionindicate strong support for H8-H10. Specifically, H8 was accepted for distributors; H9was accepted for service providers; and H10 was accepted for manufacturers. Each ofthe regression equations, when moderated by the supply chain role are unique, withdifferent variables introduced and with different levels of explanatory power. Indeed,with the exception of H7 for retailers, the explanatory power of the regression equationsincreases with the consideration of supply chain role. This is because the values for theR 2 increase from the model encompassing all the supply chain roles to the ones specificfor each supply chain role, except for the retailers. The R 2-value for the overall model is0.174, while those with the supply chain role as a moderating variable result inR 2-valuesof 0.624, 0.282 and 0.240 for the distributors, service providers and manufacturers,respectively. Therefore, when moderated by the supply chain role, the regression modelswith organization performance as the dependent variable and the SCM practices as theindependent variables have a better fit.

It may be noted that (Table VII), the RELATION variable has a negative coefficient inthe regression model for the distributors. However, this model is the strongest in ourstudy further confirming the evidence that despite this negative relationship betweenRELATION and performance, the inclusion of the variable in the model increases the fitof the model and explains the variance in the dependent variable better than any of theother models examined.

The inability to identify significant SCM-related relationships within the retail sectormay indicate that marketing-related practices may have a stronger positive relationshipwith organization performance than back office SCM-related practices. An examinationof the respondents profile in Table I shows that for the retailers, the sample was heavilybiased towards large retail companies. As many as 15 out of the 18 retailers reportedannual sales of greater than $250 million and 11 out of 18 reported having more than5,000 employees. A further analysis of the population of US-based retailers that arepublicly traded companies show that more than 91 percent of all such companies havesales greater than $250 million (US Retail Industry Publicly Traded Company List andStock Exchange Symbols, 2010). In other words, the sample bias towards larger retailersis not atypical if one considers the entire population. This also maps to the fact thatrespondents to the survey are members of the ISM and mostly professionals working inlarge corporations are expected to be members of such an organization. Therefore, whilewe cannot make conclusive recommendations about the retailers for the effect of thespecific supply chain practices examined in this study because of the small sample andthe lack of significant independent variables in the regression model, the results do pointto the strong possibility of the existence of additional factors that were not includedin this study. It is possible that specific SCM practices may not have as much an effect onthe retailers as in the other supply chain roles. This is all the more pertinent because theretail industry as classified on the publicly traded companies (US Retail Industry PubliclyTraded Company List and Stock Exchange Symbols, 2010) includes different types ofretailers including online retailers, discount retailers wholesalers, and specialty retailers

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125

in various industry categories such as dress/apparel, electronics, etc. Therefore, while thelimitations in the current study with regard to the small sample does not allow to examinethe effects of other factors, the results do point to a strong need for further studies todetermine what other (if any) supply chain practices affect retailer performance.

As previously discussed, when predictor variables are inter-correlated, stepwiseregression does not provide a definitive answer to the relative importance of thepredictor variables. A RW analysis is used to analyze the relative contribution of eachpredictor to the prediction of the dependent variable by considering both the unique andcollective contributions of each of the six SCM practices ( Johnson, 2000). The results ofthe RWs analysis are displayed in Table VIII.

Similar to the stepwise regression analysis, the RWs have been determined for allrespondents and by supply chain role: retailer, distributor, service provider andmanufacturer. Table VIII shows the percentage of R 2 explained (the RW) by each of theSCM practices. The last row of the table displays the variance explained for each of thefive models with all six variables present in the equation. Therefore, the R 2 shown inTable VIII are higher than the corresponding R 2 shown in Table VII.

The results from the RW analysis are consistent with the findings from the stepwiseregression models, but also provide additional insight to the relationships underscrutiny. For example, in the model reflecting all respondents, only two factors, SHAREand DISTRIBUTION, entered the regression model. In the RWs analysis, the same twofactors, SHARE and DISTRIBUTION, have the highest RWs (0.070 and 0.063,respectively) and contribute the highest percentages (36.8 and 33.0 percent, respectively)in predicting variation in the dependent variable. However, advanced planning systems(PLAN) also provides a unique contribution of 22.3 percent to the variation explained bythe model. In addition, the SCM practice of leveraging the Internet (INTERNET) was notfound to be significantly related to differentiated performance in the stepwise regressionanalysis. However, using the RWs method, INTERNET is found to contribute12.8 percent to the variance explained in distributor performance and 11.7 percent of thedifferentiated performance of service providers. Although these relationships did notprove to be statistically significant, their unique contribution displayed in the RWsanalysis may indicate they are managerially significant.

Finally, in the stepwise regression, the design of the supplier network did not appear tosignificantly differentiate organizational performance under any of the contextualparameters considered. However, a review of the RWs output indicates that SOURCEhighly corresponds with the observed variance in differentiated organizationalperformance within the retail sector almost an order of magnitude higher than anyother supply chain role. Combined with the comparatively large relative values of thedesign of the distribution network (DISTRIBUTION) and the use of advanced planningsystems (PLAN), the authors argue that further analysis of SCM practices within the retailsector are warranted. In particular, the results for the retailer role may have been influencedby the fact that franchise agreements and other contractual obligations tie many retailersto specific supply chain partners, and because of this reason, many retailers may havelimited opportunity to control their SCM practices. The potential of this factor to impact theefficacy of SCM practices within the retail sector should be the focus of future research.

For the distributors, the results in Table VIII are consistent with the stepwiseregression analysis. The RWs indicate a clear hierarchy in the unique contribution of eachpredictor variable found to be significant in the regression analysis DISTRIBUTION

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of SCM practices toperformance metricvariance by supply

chain role

Supply chain role

127

(40.40 percent), PLAN (25.90 percent), and RELATION (14.40 percent). Moreover,RELATION only marginally exceeds the explanatory power of INTERNET(12.80 percent). Note that RELATION was the third significant factor in the stepwiseregression model and had a negative coefficient. The counter-intuitive direction of thecoefficient is partially related to the small difference between the RWs of the two variables,RELATION and INTERNET and the significant correlation between the two variables.Although the results in Table IV show that respondents within the distributors group hada high score on the RELATION variable, a high score does not necessarily indicate thenature of the relationship of the variable with the performance metric. In fact, a furtherinvestigation of the correlation between RELATION and performance reveals that, fordistributors, the correlation is 20.264 which explains why the regression coefficient isnegative for this variable. Therefore, while the negative coefficient cannot be intuitivelyexplained using just the stepwise regression analysis, the addition of the RWs analysisand the additional insights from the correlations explain a possible cause. The resultsshow that while it has been traditionally accepted that long-term relationships with onessupply chain partners helps a company, the evidence here suggests that this may not betrue for distributors.

For service providers, information sharing (SHARE) was the only significant factor inthe regression equation. The RWs analysis shows that SHARE contributes 61.8 percentto the observed variance in organizational performance. This confirms the strategicimportance of information sharing for service companies to improving theirperformance the domination of these practices over all other practices.

For manufacturers, DISTRIBUTION, SHARE and RELATION were significant in theregression equation. The RWs display a similar order with DISTRIBUTION explaining31.3 percent of the variation and the other two factors, SHARE and RELATION,displaying a similar contribution of 24.7 and 24.9 percent, respectively. By comparison,the use of advanced planning systems (PLAN) only contributed 11.5 percent to observedvariance in organizational performance. The use of advanced planning systems was firstinitiated by manufacturers and subsequently adopted by other companies. Combinedwith the insignificant result in the regression model, the comparatively smallcontribution assigned to PLAN suggests that the return on above average investment inadvanced planning systems may be questionable for manufacturers at present. However,the high level of attention paid to advanced planning systems (a mean of 4.94 as shown inTable IV), indicates many manufacturers emphasize usage of advanced planningsystems and our findings may only indicate that the effect of these investments may nolonger be a significant differentiator of organizational performance.

The preceding comment highlights one limitation of this study. This research wasdesigned to identify SCM practices that positively relate to organizational performancelevels not to establish baseline levels for the SCM practices under review. For example,two of the top three most common practices (SOURCE and INTERNET) were not foundto be significant in any of the stepwise regression models. However, their absence shouldnot be interpreted as implying that investment in outsourcing and supplier selection orutilization of the internet is not important. A similar inference can be drawn for two of theother variables, SHARE and DISTRIBUTION, which had a positive relationshipwith organizational performance for a majority of the supply chain roles examined.While the mean scores on these two variables are low (from Table IV), respondents in thestudy also had a high variance in the adoption levels of the SCM practices. Since we were

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not establishing baseline levels for the practices in the study but were linking them toperformance, it could well be that the higher variances in the scores for these twovariables explain why these two have a positive relationship with organizationalperformance and hence appear in most of the regression models reported in the study. Infact, a further analysis of the correlations between SHARE and DISTRIBUTION withorganizational performance shows that these two variables have the two highestpositive correlations with performance (correlations of 0.335 and 0.321, respectively,with both being significant at the 0.01 level). Hence, what is more significant here is thepositive relationships of the SCM practices to performance rather than the overall meanor variances of the scores across the respondents.

Conclusions and suggestions for future researchThe results from the study highlight several implications for managers. First, the resultsshow that the importance and effectiveness of specific SCM practices are not the same forall positions in the supply chain. Rather, the mix of SCM practices pursued by anorganization should consider the specific role that a company occupies within a supplychain. This finding is in contrast to the common argument that supply chain partnersshould strive to align their SCM-related strategic initiatives, regardless of their specificroles in the supply chain. Owing to the differences in the results across the supply chainroles, specific SCM practices are more likely to differentiate an organizations performancebased on the organizations role in the supply chain. Managers in companies who areengaged primarily in distribution-related activiti