development and validation of a measurement instrument for studying supply chain management...

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Journal of Operations Management 23 (2005) 618–641

Development and validation of a measurement instrument for

studying supply chain management practices

Suhong Li a,*, S. Subba Rao b, T.S. Ragu-Nathan b, Bhanu Ragu-Nathan b

a Computer Information Systems Department, Bryant University, 1150 Douglas Pike, Smithfield, RI 02917-1284, USAb College of Business Administration, The University of Toledo, Toledo, OH 43606, USA

Received 1 January 2003; received in revised form 1 December 2004; accepted 28 January 2005

Available onli
ne 13 March 2005
Abstract

It is widely argued that competition is no longer between organizations, but among supply chains. Effective supply chain

management (SCM) has become a potentially valuable way of securing competitive advantage and improving organizational

performance. This research conceptualizes, develops, and validates six dimensions of SCM practices (strategic supplier

partnership, customer relationship, information sharing, information quality, internal lean practices, and postponement). Data

for the study were collected from 196 organizations and the measurement scales were tested and validated using structural

equation modeling. It is hoped that this study will provide a parsimonious measurement instrument to assess the performance of

the overall supply chain.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Supply chain management; Measurement; Structural equation modeling

1 Council of Logistics Management (CLM) (2000) defines SCM

1. Introduction

As competition in the 1990s intensified and

markets became global, so did the challenges associa-

ted with getting a product and service to the right place

at the right time at the lowest cost. Organizations

began to realize that it is not enough to improve

efficiencies within an organization, but their whole

supply chain has to be made competitive. It has been

pointed out that understanding and practicing supply

* Corresponding author. Tel.: +1 401 232 6503;

fax: +1 401 232 6435.

E-mail address: [email protected] (S. Li).

0272-6963/$ – see front matter # 2005 Elsevier B.V. All rights reserved

doi:10.1016/j.jom.2005.01.002

chain management (SCM) has become an essential

prerequisite to staying in the competitive global race

and to growing profitably (Power et al., 2001; Moberg

et al., 2002).

SCM has been defined1 to explicitly recognize the

strategic nature of coordination between trading

partners and to explain the dual purpose of SCM: to

improve the performance of an individual organiza-

s the systemic, strategic coordination of the traditional business

unctions and tactics across these businesses functions within a

articular organization and across businesses within the supply

hain for the purposes of improving the long-term performance

f the individual organizations and the supply chain as a whole.

a

f

p

c

o

.

S. Li et al. / Journal of Operations Management 23 (2005) 618–641 619

tion, and to improve the performance of the entire

supply chain. The goal of SCM is to create sourcing,

making and delivery processes and logistics functions

seamlessly across the supply chain as an effective

competitive weapon.

The concept of SCM has received increasing

attention from academicians, consultants, and busi-

ness managers alike (Croom et al., 2000; Tan et al.,

1998; Van Hoek, 1998). Many organizations have

begun to recognize that SCM is the key to building a

sustainable competitive edge for their products or

services in an increasingly crowded marketplace

(Jones, 1998). Despite the increased attention paid

to SCM and the expectations from SCM, the literature

does not offer much evidence of successful imple-

mentations. For example, Boddy et al. (1998) found

that more than half of the respondents to their survey

considered that their organizations had not been

successful in implementing supply chain partnering;

Spekman et al. (1998) noted that 60% of supply chain

alliances tended to fail. A recent Deloitte Consulting

survey reported that only 2% of North American

manufacturers ranked their supply chains as world-

class although 91% of these same manufacturers

ranked their SCM as important or critical to their

organization’s success (Thomas, 1999). Thus, while it

is clear that SCM is important to organizations,

effective management of the supply chain does not

appear to have been realized.

While the lack of successful SCM efforts has been

attributed to the complexity of SCM itself, research in

the area of SCM has not been able to offer much

by way of guidance to help the practice of SCM. This

has been attributed primarily to conceptual confusion

and the lack of a theoretical framework in researching

SCM. It has been pointed out that the SCM

phenomenon has not been well understood in the

literature.

Many empirical studies reflect the lack of a

theoretical framework for anchoring the results of

their studies. For example, while some studies still

tend to consider SCM as being the same as integrated

logistics management, and hence focus on inventory

reduction both within and across organizations in the

supply chain (Alvarado and Kotzab, 2001; Bechtel and

Jayaram, 1997; Romano and Vinelli, 2001; Van Hoek,

1998), there are other studies that consider SCM as

just the extension of the traditional purchasing and

supplier management activities (Banfield, 1999;

Lamming, 1996). Also, much of the current empirical

research focuses on only the internal supply chain, the

upstream or downstream side of the supply chain.

Some researches have focused on certain aspect of the

internal supply chain, such as total quality manage-

ment practices (Tan et al., 2002), internal integration

(Pagell, 2004; Braganza, 2002), agile/lean manufac-

turing (Womack and Jones, 1996; Naylor et al., 1999;

McIvor, 2001), and postponement (Beamon, 1998;

Naylor et al., 1999; Van Hoek, 1998; Van Hoek et al.,

1999). Topics such as supplier selection, supplier

involvement, and manufacturing performance (Choi

and Hartley, 1996; Vonderembse and Tracey, 1999),

the influence of supplier alliances on the organization

(Stuart, 1997), success factors in strategic supplier

alliances (Monczka et al., 1998; Narasimhan and

Jayaram, 1998; Stuart, 1997), and supplier manage-

ment orientation and supplier–buyer performance

(Shin et al., 2000), have been researched on the

supplier side. Studies such as those by Clark and Lee

(2000) and Alvarado and Kotzab (2001), focus on the

downstream linkages between manufacturers and

retailers. A few recent studies have begun to consider

both the upstream and downstream sides of the supply

chain simultaneously. Tan et al. (1998) explore the

relationships between supplier management practices,

customer relations practices and organizational per-

formance; Frohlich and Westbrook (2001) investigate

the effects of supplier–customer integration on

performance. Tan et al. (2002) study SCM and

supplier evaluation practices, Min and Mentzer (2004)

develop an instrument to measure the supply chain

orientation and SCM at conceptual levels. Cigolini

et al. (2004) develop a set of supply chain techniques

and tools for examining SCM strategies. Taken

together, these studies are representative of efforts

to address various diverse but interesting aspects of

SCM practices. However, the absence of a unifying

conceptual framework, which covers upstream, inter-

nal and downstream side of a supply chain, detracts

from the usefulness of the implications of their results.

The lack of a comprehensive view of SCM

practices and the consequent lack of a reliable

operational measure of the concept have constrained

the earlier studies from offering broad-based and

generalizable implications for guiding both the

practice of SCM and further research on the topic.

S. Li et al. / Journal of Operations Management 23 (2005) 618–641620

The purpose of this research is to develop and validate

a parsimonious measurement instrument for SCM

practices. SCM practices are defined as the set of

activities undertaken by an organization to promote

effective management of its supply chain. SCM

practice is proposed to be a multi-dimensional

concept, and hence viewed as a more comprehensive

concept than the narrower view (the supplier side, the

internal side or the customer side) taken in most prior

research. Operational measures for the constructs are

then developed and tested empirically, using data

collected from respondents to a survey. It is expected

that the current research, by offering a validated

instrument to measure SCM practices, will offer

useful guidance for SCM practices measurement and

provide a springboard for further research in the area.

The remainder of this paper is organized as follows.

The next section presents the research framework,

provides the definitions and theory underlying each

dimension of SCM practices, and two of the

performance outcomes of SCM practices (delivery

performance and time to market). The research

methodology, empirical validation, and refinement

of the scales are to be found in the sections that follow.

The last section presents the discussion of results and

directions for future work.

2. Constructs and framework

SCM practices have been defined as the set of

activities undertaken in an organization to promote

effective management of its supply chain. Donlon

(1996) describes the latest evolution of SCM practices,

which includes supplier partnership, outsourcing,

cycle time compression, and continuous process flow,

and information technology sharing. Tan et al. (1998)

use purchasing, quality, and customer relations to

represent SCM practices, in their empirical study.

Alvarado and Kotzab (2001) include in their list of

SCM practices concentration on core competencies,

use of inter-organizational systems such as EDI, and

elimination of excess inventory levels by postponing

customization toward the end of the supply chain.

Tan (2001) suggests that a well-integrated supply

chain involves coordinating the flow of materials

and information among suppliers, manufacturers, and

customers, and implementing product postponement

and mass customization. Tan et al. (2002) identify six

aspects of SCM practices through factor analysis:

supply chain integration, information sharing, supply

chain characteristics, customer service management,

geographical proximity and JIT capability. Chen and

Paulraj (2004) use supplier base reduction, long-term

relationship, communication, cross-functional teams

and supplier involvement to measure buyer–supplier

relationships. Min and Mentzer (2004) identify the

concept SCM as including agreed vision and goals,

information sharing, risk and award sharing, coopera-

tion, process integration, long-term relationship and

agreed supply chain leadership. Thus, the literature

portrays SCM practices from a variety of different

perspectives with a common goal of ultimately

improving organizational performance. In reviewing

and consolidating the literature, six distinctive

dimensions of SCM practices emerge, including

strategic supplier partnership, customer relationship,

information sharing, information quality, internal lean

practices and postponement. The six constructs cover

upstream (strategic supplier partnership) and down-

stream (customer relationship) sides of a supply chain,

information flow across a supply chain (information

sharing and information quality), and internal supply

chain processes (internal lean practices and postpone-

ment) (see Fig. 1). It should be pointed out that even

though the above dimensions capture the major

aspects of SCM practices, they cannot be considered

complete. Other factors, such as total quality manage-

ment practices (Tan et al., 2002), internal integration

(Pagell, 2004; Braganza, 2002), geographical proxi-

mity, cross-functional teams, agreed vision and goals,

and agreed supply chain leadership (Min and Mentzer,

2004) are also identified in the literature. Though these

factors are of great interest, they are not included due

to the length of the survey and the concerns regarding

the parsimony of measurement instruments.

In the following paragraphs, we identify and define

the six constructs of SCM practices, as well as the two

performance outcomes of SCM practices, delivery

dependability and time to market (see Table 1).

Detailed descriptions of the constructs are presented in

the following paragraphs.

Strategic supplier partnership is defined as the

long-term relationship between the organization and

its suppliers. It is designed to leverage the strategic and

operational capabilities of individual participating


Fig. 1. Theoretical framework linking SCM practices constructs and performance (domain definitions).

organizations to help them achieve significant ongoing

benefits (Balsmeier and Voisin, 1996; Monczka et al.,

1998; Noble, 1997; Stuart, 1997). A strategic partner-

ship emphasizes direct, long-term association and

encourages mutual planning and problem solving

efforts (Gunasekaran et al., 2001). Such strategic

partnerships are entered into to promote shared

benefits among the parties and ongoing participation

in one or more key strategic areas such as technology,

products, markets, etc. (Yoshino and Rangan, 1995).

Strategic partnerships with suppliers enable organiza-

tions to work more effectively with a few important

suppliers who are willing to share responsibility for

the success of the products. Suppliers participating

early in the product-design process can offer more

cost-effective design choices, help select the best

components and technologies, and help in design

assessment (Monczka et al., 1993). Strategically-

aligned organizations can work closely together and

eliminate wasteful time and effort (Balsmeier and

Voisin, 1996). An effective supplier partnership can be

a critical component of a leading edge supply chain

(Noble, 1997).

Customer relationship comprises the entire array of

practices that are employed for the purpose of managing

customer complaints, building long-term relationships

with customers, and improving customer satisfaction

(Aggarwal, 1997; Claycomb et al., 1999; Tan et al.,

1998). Noble (1997) and Tan et al. (1998) consider

customer relationship management as an important

component of SCM practices. The growth of mass

customization and personalized service is leading to an

era in which relationship management with customers

is becoming crucial for corporate survival (Wines,

1996). Close customer relationship allows an organiza-

tion to differentiate its product from competitors,

sustain customer loyalty, and dramatically extend the

value it provides to its customers (Magretta, 1998).

Information sharing refers to the extent to which

critical and proprietary information is communicated


Table 1

Constructs for SCM practices, delivery dependability and time to market

Constructs Definitions Literature

Strategic supplier

partnership

Strategic Supplier Partnership is defined as the long-term relationship

between the organization and its suppliers. It is designed to

leverage the strategic and operational capabilities of individual

participating organizations to help them achieve significant

ongoing benefits

Balsmeier and Voisin (1996),

Gunasekaran et al. (2001),

Lamming (1996),

Monczka et al. (1998),

Stuart (1997)

Customer relationship The entire array of practices that are employed for the

purpose of managing customer complaints,

building long-term relationships with customers,

and improving customer satisfaction

Aggarwal (1997),

Claycomb et al. (1999),

Magretta (1998), Noble (1997),

Tan et al. (1998), Wines (1996)

Information sharing The extent to which critical and proprietary information

is communicated to one’s supply chain partner

Balsmeier and Voisin (1996),

Jones (1998), Lalonde (1998),

Mentzer et al. (2000),

Monczka et al. (1998),

Novack et al. (1995),

Stein and Sweat (1998),

Towill (1997), Yu et al. (2001)

Information quality Refers to the accuracy, timeliness, adequacy, and credibility

of information exchanged

Alvarez (1994), Berry et al. (1994),

Chizzo (1998), Holmberg (2000),

Jarrell (1998), Lee et al. (1997),

Mason-Jones and Towill (1997),

McAdam and McCormack (2001),

Metters (1997), Monczka et al. (1998)

Internal lean practices The practices of eliminating waste (cost, time, etc.) in a

manufacturing system, characterized by reduced set-up

times, small lot sizes, and pull-production

Handfield and Nichols (1999),

Mason-Jones and Towill (1997),

McIvor (2001), Taylor (1999),

Womack and Jones (1996)

Postponement The practice of moving forward one or more operations

or activities (making, sourcing and delivering) to a much

later point in the supply chain

Beamon (1998),

Lee and Billington (1995),

Naylor et al. (1999),

Van Hoek (1998),

Van Hoek et al. (1999),

Waller et al. (2000)

Delivery dependability The extent to which an organization is capable of providing

on time the type and volume of product required by customer(s)

Hall (1993), Koufteros et al. (1997),

Rondeau et al. (2000)

Time to market The extent to which an organization is capable of introducing

new products faster than major competitors

Stalk (1988), Vesey (1991),

Handfield and Pannesi (1995),

Kessler and Chakrabarti (1996)

to one’s supply chain partner (Monczka et al., 1998).

Shared information can vary from strategic to tactical

in nature and from information about logistics activities

to general market and customer information (Mentzer

et al., 2000). Many researchers have suggested that the

key to the seamless supply chain is making available

undistorted and up-to-date marketing data at every node

within the supply chain (Balsmeier and Voisin, 1996;

Towill, 1997). By taking the data available and sharing

it with other parties within the supply chain, informa-

tion can be used as a source of competitive advantage

(Jones, 1998; Novack et al., 1995).

Many researchers have emphasized the importance

of information sharing in SCM practices. Lalonde

(1998) considers sharing of information as one of five

building blocks that characterize a solid supply chain

relationship. According to Stein and Sweat (1998),

supply chain partners who exchange information

regularly are able to work as a single entity. Together,

they can understand the needs of the end customer

better and hence can respond to market change

quicker. Moreover, Yu et al. (2001) point out that the

negative impact of the bullwhip effect on a supply

chain can be reduced or eliminated by sharing


information with trading partners. Tompkins and Ang

(1999) consider the effective use of relevant and

timely information by all functional elements within

the supply chain as a key competitive and distinguish-

ing factor. As an example, sharing of information with

suppliers has given Dell Company the benefits of

faster cycle times, reduced inventory, and improved

forecasts. Customers, for their part, have benefited by

getting a higher-quality product at a lower price (Stein

and Sweat, 1998).

Information quality includes such aspects as the

accuracy, timeliness, adequacy, and credibility of

information exchanged (Monczka et al., 1998). While

information sharing is important, the significance of

its impact on SCM depends on what information is

shared, when and how it is shared, and with whom

(Chizzo, 1998; Holmberg, 2000). Jarrell (1998) notes

that sharing information within the entire supply chain

can create flexibility, but this requires accurate and

timely information.

In the literature there are many examples of the

dysfunctional effects of inaccurate/delayed informa-

tion, as information moves along the supply chain

(Lee et al., 1997; Mason-Jones and Towill, 1997;

McAdam and McCormack, 2001; Metters, 1997). It

has been suggested that organizations will deliberately

distort information that can potentially reach not only

their competitors, but also their own suppliers and

customers (Mason-Jones and Towill, 1997, 1999). It

appears that there is a built-in reluctance within

organizations to give away more than minimal

information (Berry et al., 1994) since information

disclosure is perceived as a loss of power. Given these

predispositions, ensuring the quality of the shared

information becomes a critical aspect of effective

SCM. Organizations need to view their information as

a strategic asset and ensure that it flows with minimum

delay and distortion. Alvarez (1994) notes that

information shared must be as accurate as possible

in order to obtain the best SCM solution.

Internal lean practices are the practices of

eliminating waste (cost, time, etc.) in a manufacturing

system, characterized by reduced set-up times, small

lot sizes, and pull-production (Womack and Jones,

1996; McIvor, 2001; Taylor, 1999). The term ‘‘lean’’

is used to refer to a system that uses less input to

produce at a mass production speed, while offering

more variety to the end customers. Elimination of

waste is a fundamental idea within the lean system. In

‘‘Lean Thinking’’ written by Womack and Jones

(1996), five principles are identified as fundamental to

the elimination of waste. (1) Specify what does and

does not create value from the customer’s perspective;

(2) identify all the steps necessary to design, order and

produce the product across the whole value stream to

highlight non-value-adding waste; (3) make those

actions that create value flow without interruption,

detours, backflows, waiting or scrap; (4) only make

what is pulled by the customers just-in-time; (5) strive

for perfection by continually removing successive

layers of waste as they are uncovered. Following these

principles, internal lean practices may include set-up

reduction, pull production, short lead times from

suppliers, streamlining ordering, receiving and other

paperwork and continuous quality improvement.

Lean thinking and lean practices have become very

important aspects of effective SCM (Handfield and

Nichols, 1999; Mason-Jones and Towill, 1997).

Organizations that have not made the effort to drive

out unnecessary cost, time and other wastes from their

internal supply chain (so that they can deliver high

quality, best value products in a timely manner) will

run the risk of losing customers. Lean operating

practices are the dominant drivers of a highly

integrated and down-sized supply chain, promising

both cost savings and productive working partner

relationships.

Postponement is defined as the practice of moving

forward one or more operations or activities (making,

sourcing and delivering) to a much later point in the

supply chain (Beamon, 1998; Naylor et al., 1999; Van

Hoek, 1998; Van Hoek et al., 1999). In general, there

are three types of postponement: form, time, and place

postponement. ‘‘Form postponement entails delaying

activities that determine the form and function of

products in the chain until customer orders have been

received. Time postponement means delaying the

forward movement of goods until customer orders

have been received. Place postponement refers to the

positioning of inventories upstream in centralized

manufacturing or distribution operations, to postpone

the forward or downward movement of goods’’ (Van

Hoek et al., 1999). Two primary considerations in

developing a postponement strategy are: (1) determin-

ing how many steps to postpone and (2) determining

which steps to postpone (Beamon, 1998).


Postponement allows an organization to be flexible

in developing different versions of the product in order

to meet changing customer needs, and to differentiate

a product or to modify a demand function (Waller

et al., 2000). Keeping materials undifferentiated for

as long as possible will increase an organization’s

flexibility in responding to changes in customer

demand. In addition, an organization can reduce

supply chain cost by keeping undifferentiated inven-

tories (Lee and Billington, 1995; Van Hoek et al.,

1999).

Postponement needs to match the type of products,

market demands of a company, and structure or

constraints within the manufacturing and logistics

system (Fisher et al., 1994; Fisher, 1997; Fuller et al.,

1993; Pagh and Cooper, 1998). In general, the

adoption of postponement may be appropriate in the

following conditions: innovative products (Fisher et al.,

1994; Fisher, 1997); products with high monetary

density, high specialization and wide range; markets

characterized by long delivery time, low delivery

frequency and high demand uncertainty; manufacturing

or logistics systems with small economies of scales and

no need for special knowledge (Pagh and Cooper,

1998).

Performance outcomes: In this study, the constructs

of delivery dependability and time to market have been

included primarily to evaluate the predictive validity of

the six SCM practices constructs. Delivery depend-

ability is the ability of an organization to provide

products on time and of the type and in the volume as

required by the customers (Hall, 1993; Koufteros et al.,

1997; Rondeau et al., 2000). Time to market is the

capability of an organization to introduce new products

faster than the competitors (Stalk, 1988; Vesey, 1991;

Handfield and Pannesi, 1995; Kessler and Chakrabarti,

1996). Delivery dependability and time to market are

impacted by the SCM practices like strategic supplier

partnership, information sharing, postponement, etc.

For example, strategic supplier partnership can reduce

time to market (Ragatz et al., 1997) and increase level of

customer responsiveness and satisfaction (Power et al.,

2001). Information sharing will enable organizations to

make dependable delivery and introduce products to the

market quickly (Jarrell, 1998). Postponement not only

increased the flexibility in the supply chain, but also

balances global efficiency and customer responsiveness

(Van Hoek et al., 1999).

3. Instrument development and validation

An effective instrument should cover the content

domain of each construct (Nunnally, 1978; Churchill,

1979). The items that measure a construct should

agree (converge) with each other, and the items of one

construct should disagree (discriminate) with mea-

sures of the other constructs. Each construct should be

reliable and short and easy to use. Scale development

and refinement is a two-phase approach. In the first

phase, the definitions of the constructs as well as the

measurement items for each construct are established.

In this phase, we also provide tentative indications of

reliability and validity. This phase included item

generation, pre-pilot study, and pilot study. In the

second phase, we further refine this scale and validate

the measures using large-scale survey data collected

based on the scales developed in the first phase.

3.1. Scale development

The very basic requirement for a good measure is

content validity, which means that the measurement

items in an instrument should cover the major content

of a construct (Churchill, 1979). Content validity is

usually achieved through a comprehensive literature

review and interviews with practitioners and acade-

micians. The items for SCM practices were generated

based on previous SCM literature (Aggarwal, 1997;

Claycomb et al., 1999; Forker et al., 1999; Lee and

Kim, 1999; Monczka et al., 1998; Shin et al., 2000;

Stuart, 1997; Tan et al., 1998; Vonderembse and

Tracey, 1999; Walton, 1996). All the items were

measured on a five-point scale.

In the pre-pilot study, these items were reviewed by

six academicians and re-evaluated through structured

interviews with three practitioners who were asked to

comment on the appropriateness of the research

constructs. Based on the feedback from the academi-

cians and practitioners, redundant and ambiguous

items were either modified or eliminated. New items

were added wherever deemed necessary.

The next stage, pilot-study, in the development of

scales was the application of the Q-sort procedure for

assessing initial construct validity and reliability. The

Q-sort method, a manual factor sorting technique

(Moore and Benbasat, 1991), is an iterative process in

which the degree of agreement between judges forms


the basis of assessing construct validity and improving

the reliability of the constructs. For the Q-sort method

two evaluation indices are used to measure inter-judge

agreement level: Cohen’s Kappa (Cohen, 1960) and

Moore and Benbasat’s ‘‘Hit Ratio’’ (Moore and

Benbasat, 1991). Cohen’s Kappa is a measure of

agreement that can be interpreted as the proportion of

joint judgment in which there is agreement after

chance agreement is excluded. For Kappa, no general

agreement exists with respect to required scores.

However, several studies have considered scores

greater than 0.65 to be acceptable (e.g., Vessey,

1984; Jarvenpaa, 1989). Landis and Koch (1977) have

provided a more detailed guideline to interpret Kappa

by associating different values of this index to the

degree of agreement beyond chance.

Moore and Benbasat’s (1991) method requires

analysis of how many items are placed by the panel of

judges for each round within the target construct. The

higher the percentage of items placed in the target

construct, the higher the degree of inter-judge

agreement across the panel, which must have

occurred. There are no established guidelines for

determining good levels of placement, but the matrix

of the item placement ratio can be used to highlight

any potential problem areas. Item placement ratios

were calculated by counting all the items that were

correctly sorted into the target category by each of the

judges and dividing them by twice the total number of

items.

For the Q-sort method, purchasing/production

managers were requested to act as judges and sort

the items into the six dimensions of SCM practices,

based on similarities and differences among items. To

assess the reliability of the sorting conducted by the

judges, Cohen’s Kappa, the inter-judge raw agreement

scores and item placement ratios were used.

In the first round, the inter-judge raw agreement

scores averaged 0.89 and the initial overall placement

ratio of items within the target constructs was 0.87,

which are in acceptable range. Cohen’s Kappa score

averaged 0.87. Following the guidelines of Landis and

Table 2

Inter-judge agreements

Agreement measure Round 1 Round 2 Round 3

Raw agreement (%) 89 95 95

Cohen’s Kappa (%) 87 94 94

Koch (1977) for interpreting the Kappa coefficient, the

value of 0.87 was considered an excellent level of

agreement (beyond chance) for the judges in the first

round. In order to further improve the agreement

scores and Cohen’s Kappa measure of agreement, an

examination of the off-diagonal entries in the

placement matrix was conducted. Items classified in

a construct different from their target construct were

identified and dropped or reworded. Also, feedback

from both judges was obtained on each item and

incorporated into the modification of the items.

The reworded items were then entered into a

second sorting round. In the second round, the inter-

judge raw agreement scores averaged 0.95, the initial

overall placement ratio of items within the target

constructs was 0.97, and the Cohen’s Kappa score

averaged 0.94. Since the second round achieved an

excellent overall placement ratio of items within the

target constructs (0.97), it was decided to keep all the

items for the third sorting round.

The third sorting round was used to re-validate the

constructs. The third round achieved the same inter-

judge raw agreement and Cohen’s Kappa scores as the

second round, thereby indicating an excellent level of

agreement between the judges in the third round and

consistency of results between the second and third

rounds. At this stage the statistics suggested an

excellent level of inter-judge agreement indicating a

high level of reliability and construct validity. Table 2

presents a summary of agreement scores for the three

rounds. In Table 3 we present the final round of item

placement ratios. Each of the SCM practices scale is

listed on the rows of the tables. For strategic supplier

partnership, perfect item placement ratio for this scale

would be a score of 20 (10 items � 2 judges). In this

case, 18 judge-items were classified as intended, while

2 items under N/A category. The item-placement ratio

for strategic supplier partnership thus equals 18/20 or

90%. On the other hand, information sharing,

information quality, internal lean practices, and

postponement obtained a 100% item placement ratio.

All of the scales are well above the recommended

value of 0.65 (Moore and Benbasat, 1991).

3.2. Empirical scale refinement and validation

This study sought to choose respondents who can

be expected to have the best knowledge about the


Table 3

Item-placement ratios (final sorting round) for SCM practices

Intended SCM

practices scales

(no. of items in scale)

Actual classifications NA Total Item

placement

ratio (%)Strategic

supplier

partnership

Customer

relationship

Information

sharing

Information

quality

Internal

lean

practices

Postponement

Strategic supplier

partnership (10)

18 2 20 90

Customer relationship (9) 17 1 18 94

Information sharing (7) 14 14 100

Information quality (5) 10 10 100

Internal lean practices (8) 16 16 100

Postponement (5) 10 10 100

Total item placements = 88, Hits = 85, Overall hit ratio (%) = 97.

operation and management of the supply chain in his/

her organization. Mailing lists were obtained from two

sources: the Society of Manufacturing Engineers

(SME) and the attendees at the Council of Logistics

Management (CLM) conference in New Orleans,

2000. The lists were limited to organizations with

more than 100 employees since organizations with

less than 100 employees are unlikely to engage in

any sophisticated SCM. Six SIC codes were covered

in the study: 25 ‘‘Furniture and Fixtures’’, 30

‘‘Rubber and Plastics’’, 34 ‘‘Fabricated Metal Pro-

ducts’’, 35 ‘‘Industrial and Commercial Machinery’’,

36 ‘‘Electronic and Other Electric Equipment’’, 37

‘‘Transportation Equipment’’.

The final version of the questionnaire was

administrated to 3137 target respondents. To ensure

a reasonable response rate, the survey was sent in three

waves. The questionnaires with a cover letter

indicating the purpose and significance of the study

were mailed to the target respondents. In the cover

letter, a web-address of the online version of the

survey was also provided in case the respondents

wished to fill it in electronically. There were 196

complete and usable responses, representing a

response rate of approximately 6.3%.

A significant problem with organizational-level

research is that senior and executive-level mangers

receive many requests to participate and have very

limited time. Because this interdisciplinary research

collects information from several functional areas, the

size and scope of the research instruments are large

and time consuming to complete. This further

contributes to the low response rate. While the

response rate was less than desired, the makeup of

respondent pool was considered excellent (see

Appendix A). Among the respondents, almost 20%

of the respondents are CEO/President/Vice President/

Director. About half of the respondents are managers,

some identified them as supply chain manager, plant

manager, logistics manager or IT manager in the

questionnaire. The areas of expertise were 30%

purchasing, 47% manufacturing production, and

30% distribution/transportation/sales. It can be seen

that respondents have covered all the functions across

a supply chain from purchasing, to manufacturing, to

distribution and transportation, and to sales. Moreover,

about 30% of the respondents are responsible for more

than one job function and 60% of respondents have

stayed at the organization for more than 10 years, and

as such they should have a broad view of SCM

practices in their organization.

A concern that is typical of such surveys is that

information collected from respondents might have a

non-response bias. This research did not investigate

non-response bias directly because the mailing list had

only name and addresses of the individuals and not any

organizational details. Hence a comparison was made

between those subjects who responded after the initial

mailing and those who responded to the second/third

wave. The later wave of surveys received was

considered to be representative of non-respondents

(Amstrong and Overton, 1977; Lambert and Harring-

ton, 1990). Similar methodology has been used in

prior empirical studies of SCM (Handfield and

Bechtel, 2002; Moberg et al., 2002; Narasimhan

and Kim, 2001). Using the x2 statistic and P < 0.05, it


was found that there were no significant differences

between the two groups in terms of employment size,

sales volume, and respondent’s job title.

4. Assessment of construct validities

The measurement properties of the six dimensions

of SCM practices construct were evaluated by

assessing key components of construct validity. As

per the guidelines of Bagozzi (1980), and Bagozzi and

Phillips (1982), the following measurement properties

are considered important for assessing the measures

developed in this paper: (1) content validity, (2)

internal consistency of operationalization (unidimen-

sionality and reliability), (3) convergent validity, (4)

discriminant validity, and (5) predictive validity.

An instrument has content validity if there is a

general agreement among the subjects and researchers

that the instrument has measurement items that cover

all important aspects of the variable being measured.

Unidimensionality indicates that all of the items are

measuring a single theoretical construct. Reliability

values indicate the degree to which operational

measures are free from random error and measure

the construct in a consistent manner. Convergent

validity is about the extent to which there is

consistency in measurements across multiple oper-

ationalizations (Campbell and Fiske, 1959). Discri-

minant validity refers to the independence of the

dimensions (Bagozzi and Phillips, 1991), i.e., the

extent to which measures of the six constructs are

distinctly different from each other. Predictive validity

seeks to find support for the validity of the construct by

investigating whether it exhibits relationships with

other constructs that are in accordance with theory.

LISREL was used to check the measurement

properties of the constructs. Model-data fit was

evaluated based on multiple fit indexes. The chi-

square statistic is perhaps the most popular index to

evaluate the goodness of fit of the model. It measures

the difference between the sample covariance and the

fitted covariance. However, this index has some

disadvantages. The chi-square index is sensitive to

sample size and departures from multivariate normal-

ity. Therefore, it has been suggested that it must be

interpreted with caution in most applications (Jor-

eskog and Sorbom, 1989; Chau, 1997). Researchers

are hence turning to multiple fit criteria as suggested

by Bollen and Long (1993) to reduce any measuring

biases inherent in different measures. Some of the

other measures of overall model fit that are being used

by researchers are the Goodness of Fit Index (GFI),

which indicates the relative amount of variance and

covariance jointly explained by the model. The

Adjusted Goodness of Fit Index (AGFI) differs from

the GFI in that it adjusts for the number of degrees of

freedom in the model. GFI and AGFI values range

from 0 to 1, with higher values indicating better fit

(Bryne, 1989). GFI and AGFI scores in the 0.80–0.89

range are generally interpreted as representing

reasonable fit; scores of 0.90 and above represent

good fit (Chau, 1997). The Root Mean Square

Residual (RMR) indicates the average discrepancy

between the elements in the sample covariance matrix

and the model-generated covariance matrix. RMR

values range from 0 to 1, with smaller values

indicating better models; values below 0.05 signify

good fit (Bryne, 1989).

4.1. Content validity

Content validity depends on how well the

researchers create measurement items to cover the

domain of the variable being measured (Nunnally,

1978). The evaluation of content validity is a rational

judgmental process not open to numerical evaluation.

Usual method of ensuring content validity is an

extensive review of literature for the choice of the

items and getting inputs from the practitioners and

academic researchers on the appropriateness, com-

pleteness, etc. In addition to the above, experts

performing the manual sorting of the constructs in the

Q-sort method also contributed to the content validity

of the constructs.

4.2. Unidimensionality

The model for unidimensionality can be written,

following Joreskog’s conventions of measurement

model specifications, as:

X ¼ Ljþ d

where X is a vector of p indicators, L is p � k matrix

of factor loadings, j is a k (< p)-vector of theoretical

factors, and d is a p-vector of unique scores (i.e.,


random errors) (Venkatraman and Ramanujam, 1987).

Assuming that E(j) = E(d) = 0, E(jj0) = F, and

E(dd0) = C, the variance–covariance matrix S of X

can be written as

S ¼ LFL0 þ C

where F is the matrix of inter-correlations among the

factors, and C is a symmetric matrix of error variances

(ud) for the measures. As mentioned before we use

multiple fit criteria to test for unidimensionality and

reduce any measuring biases inherent in different

measures. Two goodness of fit indexes were used:

GFI and RMR. GFI values of 0.90 and higher, or RMR

values of 0.05 or lower suggest no evidence of a lack

of unidimensionality.

SCM practices construct was initially represented

by 6 dimensions and 40 items (see Appendix B). A

single factor LISREL measurement model is specified

for each dimension of SCM practices. Following Sethi

and King (1994), iterative modifications were made

for each of the constructs by observing modification

indices and coefficients to improve key model fit

statistics. Further, as recommended by Joreskog and

Sorbom (1989), only one item was altered at a time to

avoid over-modification of the model. This iterative

process continued until all model parameters and key

fit indices met recommended criteria. If the constructs

have less than four items, model fit statistics could not

be obtained. In these cases, two-factor model was

tested by adding the items of another construct. The

items of another construct are added only to provide a

Table 4

Assessment of unidimensionality and convergent validity

Construct Indicators x2

Supplier rationalization (SR)* 2 37.53

Strategic supplier partnership (SSP) 6 22.50

Customer relationship practice (CRP) 5 9.69

Information sharing (IS) 6 18.44

Information quality (IQ) 5 10.78

Internal lean practices (ILP) 5 13.38

Postponement (POS)* 3 37.01

Delivery dependability (DD)* 2 47.90

Time to market (TM) 4 4.73

Note: Constructs marked by an asterisk have either 2 or 3 items and the m

model was tested by adding the items of another construct. The items

rationalization and postponement construct, respectively, while the items

construct.

common basis for comparison and to keep items in

sufficient number so that model fit statistics could be

obtained. Appendix C presents the details of this

modification process and the final items.

After this modification, supplier rationalization

was added as an additional sub-construct of SCM

practices. Two items (SSP4 and SSP10) were removed

from strategic supplier partnership; three items (CR1,

CR3, and CR7) were removed from customer

relationship, one item (IS1) was removed from

information sharing, and two items (POS2 and

POS5) were removed from postponement. No items

were removed from information quality and internal

lean practices. The results are summarized in Table 4.

The table presents the number of items measuring

each construct, and statistics for assessing the good-

ness of fit of the measurement model indicating the

unidimensionality of all constructs. The items

removed in the final instrument are identified by an

asterisk in Appendix B.

4.3. Reliability

Traditionally, the Cronbach a coefficient (Cron-

bach, 1951) has been used to evaluate reliability. A

scale is found to be reliable if a is 0.70 or higher

(Nunnally, 1978). However, it has been noted that

Cronbach a uses restrictive assumptions regarding

equal importance of all indicators and the measure of

reliability can be biased. An alternate composite

reliability measure has been suggested (Werts et al.,

P-value GFI RMR Bentler–Bonett (D)

0.01 0.94 0.04 0.95

0.01 0.96 0.03 0.95

0.08 0.98 0.02 0.97

0.03 0.97 0.02 0.97

0.06 0.98 0.02 0.98

0.02 0.97 0.04 0.95

0.07 0.96 0.04 0.94

0.00 0.92 0.05 0.91

0.09 0.99 0.03 0.98

odel fit statistics could not be obtained. In these cases, a two-factor

of strategic supplier partnership construct were added to supplier

of time to market construct were added to delivery dependability


Table 5

Assessment of reliability

Construct Indicators Reliability

(rc)

Reliability

(a)

Supplier rationalization

(SR)*

2 – 0.93

Strategic supplier

partnership (SSP)

6 0.85 0.86

Customer relationship

practice (CRP)

5 0.84 0.84

Information sharing (IS) 6 0.87 0.86

Information quality (IQ) 5 0.87 0.86

Internal lean practices

(ILP)

5 0.78 0.78

Postponement (POS)* 3 0.74 0.73

Delivery dependability

(DD)*

2 – 0.93

Time to market (TM) 4 0.77 0.76

1974). This reliability measure rc for an underlying

theoretical dimension A, can be calculated as follows:

rc ¼ðP p

i¼1 liÞ2variance A

ðP p

i¼1 liÞ2variance A þP p

i¼1 ud

where rc is the composite measure reliability, p is the

number of indicators, and li is the factor loading

which relates item I to the underlying theoretical

dimension A. When rc is greater than 0.50 it implies

that the variance captured by the factor is more than

that captured by the error components (Bagozzi,

1981). Bagozzi (1981) and Werts et al. (1974) suggest

using rc in conjunction with Cronbach alpha. The

calculated values for Cronbach’s alpha were very

similar to Werts–Linn–Jorsekog coefficient (rc),

Table 5 reported rc and Cronbach’s alpha for each

dimension of SCM practices. Note that all coefficients

are greater than 0.73, indicating good construct relia-

bility of all the constructs.

4.4. Convergent validity

To assess convergent validity of constructs we look

at each item in the scale as a different approach to

measure the construct and determine if they are

convergent. The convergent validity of each scale is

checked using the Bentler–Bonett coefficient (D)

(Bentler and Bonett, 1980), which is the ratio of the

difference between the chi-square value of the null

measurement model and the chi-square value of the

specified measurement model to the chi-square value

of the null model. A value of 0.90 and above

demonstrates strong convergent validity (Hartwick

and Barki, 1994; Segar and Grover, 1993). Bentler–

Bonett coefficient (D) is shown in Table 4 for all the

constructs. Note that all the constructs have values of

0.91 or above, demonstrating strong convergent

validity.

4.5. Discriminant validity

Discriminant validity refers to the uniqueness and

the independence of the measures, i.e., the extent to

which the measures are distinctly different from each

other. A test of discriminant validity is performed

taking two constructs at a time. The constructs are

considered to be distinct if the hypothesis that the two

constructs together form a single construct is rejected.

To test this hypothesis, a pair-wise comparison of

models was performed by comparing the model with

correlation constrained to equal one with an uncon-

strained model (see Fig. 2). A difference between the

x2 values (d.f. = 1) of the two models that is significant

at P < 0.05 level would indicate support for the

discriminant validity criterion (Joreskog, 1971).

Table 6 reports the results of the 21 pair-wise tests

of discriminant validity for SCM practices. All x2

difference are significant at the P < 0.01 level,

indicating strong support for the discriminant validity

criterion.

4.6. Predictive validity

4.6.1. Predictive validity using qualitative measure

(delivery dependability and time to market)

According to Bagozzi and Phillips (1991) and

Fornell (1982), the conceptual meaning of a construct

should be determined not only by its definition and

operationalization but also by its relationship to

antecedents and consequents. Predictive validity is

represented in the form of structural relationships in

addition to the measurement models. The structural

relationship is represented as:

h ¼ Gjþ z

where h is an endogenous theoretical construct (i.e.,

performance), G the matrix of structural coefficients

relating exogenous theoretical construct (i.e., perfor-


Table 6

Assessment of discriminant validity

Description Model fit indices x2 statistics Difference

Unconstrained Constrained Unconstrained model (d.f.) Constrained model (d.f.)

GFI AGFI GFI AGFI

SR with SSP 0.96 0.92 0.81 0.66 35.73 (19) 181.67 (20) 145.94

SR with CRP 0.97 0.93 0.80 0.60 21.24 (13) 171.41 (14) 150.17

SR with IS 0.97 0.94 0.82 0.67 25.01 (19) 176.12 (20) 151.11

SR with IQ 0.97 0.94 0.81 0.61 19.24 (13) 164.84 (14) 145.60

SR with ILP 0.97 0.93 0.80 0.59 23.20 (13) 175.72 (14) 152.52

SR with POS 0.99 0.98 0.77 0.30 2.52 (4) 184.46 (5) 181.94

SSP with CRP 0.94 0.91 0.74 0.62 67.97 (43) 367.75 (44) 299.78

SSP with IS 0.92 0.88 0.71 0.58 106.77 (53) 472.75 (54) 365.98

SSP with IQ 0.93 0.90 0.63 0.44 74.60 (43) 641.01 (44) 566.41

SSP with ILP 0.92 0.88 0.83 0.75 95.02 (43) 215.32 (44) 120.30

SSP with POS 0.96 0.93 0.84 0.74 35.64 (26) 164.23 (27) 128.59

CRP with IS 0.93 0.90 0.67 0.51 76.54 (43) 521.12 (44) 444.58

CRP with IQ 0.95 0.92 0.66 0.46 48.70 (34) 505.56 (35) 456.86

CRP with ILP 0.95 0.91 0.78 0.66 55.69 (34) 267.41(35) 211.72

CRP with POS 0.98 0.95 0.83 0.70 19.54 (19) 154.32 (20) 134.78

IS with IQ 0.92 0.88 0.75 0.63 92.33 (43) 351.60 (44) 259.37

IS with ILP 0.93 0.89 0.79 0.68 84.45 (43) 292.69 (44) 208.24

IS with POS 0.97 0.94 0.84 0.74 28.87 (26) 165.35 (27) 134.48

IQ with ILP 0.95 0.91 0.78 0.65 56.59 (34) 280.27 (35) 223.68

IQ with POS 0.96 0.92 0.83 0.70 34.47 (19) 154.52 (20) 120.05

ILP with POS 0.94 0.89 0.81 0.66 48.85 (19) 182.01 (20) 133.16

All x2 differences are significant (for 1 degree of freedom) at the less than 0.01 level. GFI, goodness of fit index; AGFI, adjusted goodness of fit

index; d.f., degree of freedom.

Fig. 2. Illustrative example of measurement model testing discriminant validity.


Fig. 3. Illustrative example of structural equation model testing predictive validity.

mance dimension), j the vector of latent variables, and

z is the residuals of endogenous theoretical construct

(see Fig. 3).

This study uses delivery dependability and time to

market to evaluate the predictive validity of the

dimensions of SCM practices. The measures for

delivery dependability and time to market were

adopted from Zhang (2001) and are presented in

Appendix B. Results of tests of unidimensionality,

convergent validity, and reliability for these two

constructs are provided in Tables 4 and 5, respectively.

Table 7 contains the values of Gammas and their

t-values. The t-values show that each of the SCM

constructs do significantly relate to the delivery

dependability and time to market constructs except

supplier rationalization and postponement, thus

establishing the predictive validity of the constructs.

Table 7

Assessment of predictive validity with delivery dependability and

time to market

Delivery

dependability

Time to market

g t-value g t-value

Supplier rationalization – – 0.07 0.69

Strategic supplier partnership 0.14 1.86* 0.32 3.47**

Customer relationship 0.34 4.20** 0.38 4.01**

Information sharing 0.13 1.73* 0.24 2.72**

Information quality 0.20 2.05* 0.18 2.07*

Internal lean practices 0.23 2.62** 0.38 3.89**

Postponement 0.06 0.66 0.09 1.01

Note: The specified model is not converged.** P-value is significant at 0.01.* P-value is significant at 0.05.

The impact of supplier rationalization on performance

outcome may be indirect through strategic partnership

with suppliers, as the rationalization of suppliers is the

basis for building a strategic partnership. Moreover,

since the implementation of postponement is largely

dependent on type of product, its overall impact on

performance outcomes may be not significant.

4.6.2. Predictive validity using quantitative

measures (SCOR model)

The supply chain operations reference model

(SCOR) developed by Supply Chain Council

(www.supply-chain.org) measures the performance

of supply chain by using the 13 metrics (see Table 8).

Those measures can be used as hard, objective

validation of the SCM practices construct.

The respondents in our survey were asked to fill in the

actual performance of their supply chain in terms of

those metrics. To seewhether the level of SCM practices

predicts the level of supply chain performance, the

respondents were first classified into two groups based

on their mean of SCM practices and t-tests were

conducted to see whether there exists significant

difference between these two groups in terms of each

metric in SCOR model except the last four items, which

were ignored from the analysis since most of the

respondent left them blank. The results are presented in

Table 8. Significant differences between those two

groups were found in most of the metrics. Compared to

organizations with lower level of SCM practices,

organizations with high level of SCM practices have

better performance in term of delivery performance to

commit date (increased from 84%to 90%or 6% higher),

http://www.supply-chain.org/


Table 8

Assessment of predictive validity with SCOR model

Supply chain performance Actual performance

Organizations with

higher level of

SCM practices

Organizations

with lower level

of SCM practices

Mean difference t-test

Value Percentage t-value a

Delivery performance to commit date 90.4% 83.5% – 6.9 2.32 0.022

Fill rate 94.0% 91.8% – 2.2 0.67 0.509

Perfect order fulfillment 88.9% 84.8% – 4.1 0.87 0.39

Order fulfillment lead time 15 days 19 days 4 26.7 �0.81 0.419

Supply chain response time 11 days 33 days 22 200 �2.24 0.035

Production flexibility 6 days 11 days 5 83.3 �1.45 0.159

Cash-to-cash cycle time 33 days 97 days 64 193.9 �1.77 0.092

Inventory days of supply 30 days 87 days 57 190 �4.52 0.000

Net asset turns (working capital) 11 turns 5 turns 6 120 2.79 0.008

Cost of goods sold*

Total supply chain management costs*

Value-added productivity*

Warranty/returns processing costs*

Note: Items marked by an asterisk were removed from the analysis since most of respondents left them blank.

supply chain response time (decreased from 33 days to

11 days or 200% faster), cash-to-cash cycle time

(decreased from 97 days to 33 days, or 194% faster),

inventory days of supply (decreased from 87 days to 30

days, or 190% shorter), and net asset turns (from 5 turns

to 11 turns, or 120% faster).

Table 8 also shows that organizations with a high

level of SCM practices is associated with a 2%

increase in fill rate, a 4% increase in perfect order

fulfillment, a 27% decrease in order fulfillment lead

time, and a 83% increase in product flexibility.

Overall, the implementation of SCM practices has

improved the performance of supply chain as defined

by SCOR model and thus provides the support for the

predictive validity of SCM practices construct.

5. Implications and conclusions

The major contribution of the represent study is the

development of a set of SCM practices constructs as

well as a rigorously validated measurement instrument

for collecting data in further studies. The confirmation

process is according to the typical standards of scale

development (Raghunathan et al., 1999; Sethi and

King, 1994; Anderson and Gerbing, 1988). We believe

the instrument developed in this paper is parsimonious

and will be of use to researchers for further studies of

SCM practices and their relationships with other

organizational processes and outcomes like competi-

tive advantage, SCM performance, and organizational

performance.

Many organizations still tend to consider supply

chain management as being the same as integrated

logistics management or as a synonym for supplier

management though they are not. Although some

organizations have realized the importance of SCM,

they lack an understanding of what constitutes a

comprehensive set of SCM practices. The measures of

SCM practices provided in this paper can be useful to

SCM managers in evaluating their current SCM

practices. This can help the managers to identify the

strengths and weaknesses of their SCM practices.

In conclusion this research was an attempt to

conceptualize and develop measures of SCM practices

and a parsimonious measurement instrument. The

instrument was rigorously tested for content validity,

unidimensionality, discriminant validity, predictive

validity and reliability. The development of the SCM

practices measure is expected to motivate and

facilitate further theory development and empirical

investigation in this field.

6. Limitation of the study and future research

As with most of empirical research, there are a few

limitations of the present study. This study evaluates


SCM practices from the standpoint of a manufacturing

firm (the centric firm in a supply chain). Some

constructs, such as internal lean practices and

postponement, may be not appropriate for distributors

and retailers (the firms at the end of a supply chain).

For a manufacturing firm, the level of postponement

may be associated with make-to-order versus make-

to-stock production systems. The instrument thus fits

best manufacturers with a make-to-order system.

As the concept of SCM is complex and involves a

network of companies in the effort of producing and

delivering a final product, its entire domain can not be

covered in just one study. Future study can develop

additional measurement for the practices of internal

supply chain, such as total quality management (Tan

et al., 2002), cross-functional coordination (Chen and

Paulraj, 2004), and internal integration (Pagell, 2004;

Braganza, 2002). Furthermore, inter-organizational

relationships, such as trust, commitment, shared vision

(Li, 2002), risk and award sharing, and agreed supply

chain leadership (Min and Mentzer, 2004) can also be

incorporated into the SCM practices construct as they

are thefoundation forbuildinganeffective supply chain.

Future research should expand the SCM practices

construct by including the above dimensions and focus

on testing and validating the combined construct.

It should be noted that the implementation of various

SCM practices may be influenced by contextual factors,

such as firm size, a firm’s position in the supply chain,

supply chain length, and channel structure. For

example, the larger organizations may have higher

levels of SCM practices since they usually have more

complex supply chain networks necessitating the need

for more effective management of supply chain. As

pointed out before, postponement and internal lean

Appendix A. Demographic data for the respondents (s

Variables Total responsesa

Number of employees (194)

100–250 74 (38.1%)

251–500 27 (13.9%)

501–1000 19 (9.8%)

>1000 74 (38.1%)

Sales volume in millions of US$ (190)

<10 5 (2.6%)

10 to <25 37 (19.5%)

25 to <50 28 (14.7%)

practice are not appropriate for firms at the end of a

supply chain (distributors and retailers). The level of

information quality may be influenced negatively by the

length of a supply chain. Since information suffers from

delay and distortion as it travels along the supply chain,

the shorter the supply chain, the less chances it will get

distorted. The higher level of postponement may be

associated with make-to-order versus make-to-stock

production systems. Future study can study the impact

of such factors on the SCM practices.

The use of single respondent may generate some

measurement inaccuracy. Future research should

survey multiple respondents (marketing, IT and

operations managers) from a single organization

using the instrument developed in this study; the

discrepancies of SCM perception between the groups

and the impact of such discrepancies on overall

performance can thus be examined. It will also be of

interest to examine the relationships between seven

constructs of SCM practices. For example, supplier

rationalization, strategic supplier partnership and

customer relationship can be combined into external

relationship management; information sharing and

information quality can be combined into information

management; and internal lean practices and post-

ponement can be combined into internal supply chain

practices. The interactions among external relation-

ship management, information management, and

internal supply chain practices can be investigated.

Future research can also compare the SCM practices

among all the participants within a supply chain

(suppliers, manufactures, distributors, wholesales,

retailers, etc.). It is of interest to investigate how

the SCM practices differ across each participating

organization within a supply chain.

ample size 196)

First wavea Second and third wavesa

36 (38.7%) 38 (37.6%)

12 (12.9%) 15 (14.6%)

7 (7.5%) 12 (11.9%)

38 (40.9%) 36 (35.6%)

4 (4.4%) 1 (1.0%)

18 (20.0%) 19 (19.0%)

9 (10.0%) 19 (19.0%)


Appendix A (Continued)

Variables Total responsesa First wavea Second and third wavesa

50 to <100 26 (13.7%) 14 (15.6%) 12 (12.0%)

>100 94 (49.5%) 45 (50.0%) 49 (49.0%)

Job title (194)

CEO/President/Vice President 14 (7.2%) 10 (10.6%) 4 (4.0%)

Director 35 (18.0%) 17 (18.3%) 18 (17.8%)

Manager 121 (63.4%) 54 (58.1%) 67 (66.3%)

Other 24 (12.4%) 12 (12.9%) 12 (11.9%)

Years stayed at the organization (194)

<2 15 (7.7%) 12 (12.9%) 3 (3.0%)

2–5 29 (14.9%) 12 (12.9%) 17 (16.8%)

6–10 32 (16.5%) 15 (16.1%) 17 (16.8%)

>10 118 (60.8%) 54 (58.1%) 64 (63.4%)

a Frequency in percentage.

Appendix B. Instrument for SCM practices, delivery dependability, and time-to-market

Strategic supplier partnership (SSP)

SSP1* We rely on a few dependable suppliers

SSP2* We rely on a few high quality suppliers

SSP3 We consider quality as our number one criterion in selecting suppliers

SSP4* We strive to establish long-term relationship with our suppliers

SSP5 We regularly solve problems jointly with our suppliers

SSP6 We have helped our suppliers to improve their product quality

SSP7 We have continuous improvement programs that include our key suppliers

SSP8 We include our key suppliers in our planning and goal-setting activities

SSP9 We actively involve our key suppliers in new product development processes

SSP10* We certify our suppliers for quality

Customer relationship (CR)

CR1* We frequently evaluate the formal and informal complaints of our customers

CR2 We frequently interact with customers to set reliability, responsiveness, and other standards for us

CR3* We have frequent follow-up with our customers for quality/service feedback

CR4 We frequently measure and evaluate customer satisfaction

CR5 We frequently determine future customer expectations

CR6 We facilitate customers’ ability to seek assistance from us

CR7* We share a sense of fair play with our customers

CR8 We periodically evaluate the importance of our relationship with our customers

Information sharing (IS)

IS1* We share our business units’ proprietary information with trading partners

IS2 We inform trading partners in advance of changing needs

IS3 Our trading partners share proprietary information with us

IS4 Our trading partners keep us fully informed about issues that affect our business

IS5 Our trading partners share business knowledge of core business processes with us

IS6 We and our trading partners exchange information that helps establishment of business planning

IS7 We and our trading partners keep each other informed about events or changes that may affect the other partners

Information quality (IQ)

IQ1 Information exchange between our trading partners and us is timely

IQ2 Information exchange between our trading partners and us is accurate

IQ3 Information exchange between our trading partners and us is complete

IQ4 Information exchange between our trading partners and us is adequate

IQ5 Information exchange between our trading partners and us is reliable


Appendix B. (Continued)

Internal lean practices (ILP)

ILP1 Our firm reduces set-up time

ILP2 Our firm has continuous quality improvement program

ILP3 Our firm uses a ‘‘Pull’’ production system

ILP4 Our firm pushes suppliers for shorter lead-times

ILP5 Our firm streamlines ordering, receiving and other paperwork from suppliers

Postponement (POS)

POS1 Our products are designed for modular assembly

POS2* Our production process modules can be re-arranged so that customization can be carried out later at distribution centers

POS3 We delay final product assembly activities until customer orders have actually been received

POS4 We delay final product assembly activities until the last possible position (or nearest to customers) in the supply chain

POS5* Our goods are stored at appropriate distribution points close to the customers in the supply chain

Delivery dependability: an organization is capable of providing on time the type and volume of product required by customer(s)

DD1* We deliver the kind of products needed

DD2 We deliver customer order on time

DD3 We provide dependable delivery

Time to market: an organization is capable of introducing new products faster than major competitors

TM1 We deliver product to market quickly

TM2 We are first in the market in introducing new products

TM3 We have time-to-market lower than industry average

TM4 We have fast product development

Items marked by an asterisk were removed in the final instrument.

Appendix C. Description of modification process and assessment of unidimensionality and convergent

validity of SCM constructs

Items Fit indices

SCM practices-strategic supplier partnershipInitial model SSP1, SSP2, SSP3, SSP4, SSP5,

SSP6, SSP7, SSP8, SSP9, SSP10

x2 = 224.83; P = 0.00; GFI = 0.81; AGFI = 0.71; NFI = 0.65

l Coefficients of items SSP1, SSP2, and SSP10 in the above mode were very low (0.25, 0.37, and 0.40, respectively). Item SSP1 with lowest l

coefficients was dropped for the next iteration

Iteration 1 SSP2, SSP3, SSP4, SSP5, SSP6,

SSP7, SSP8, SSP9, SSP10

x2 = 70.84; P = 0.00; GFI = 0.93; AGFI = 0.88; NFI = 0.90

l Coefficients of items SSP2 and SSP10 were very low (0.34 and 0.39, respectively). Item SSP2 with lowest l coefficients was dropped for the

next iteration

Iteration 2 SSP3, SSP4, SSP5, SSP6, SSP7,

SSP8, SSP9, SSP10

x2 = 64.51; P = 0.00; GFI = 0.92; AGFI = 0.86; NFI = 0.91;

l Coefficient of item SSP10 was still very low (0.40). Item SSP10 was removed for the next iteration

Iteration 3 SSP3, SSP4, SSP5, SSP6,

SSP7, SSP8, SSP9

x2 = 53.47; P = 0.00; GFI = 0.93; AGFI = 0.85; NFI = 0.92;

Although all l coefficient were good, the AGFI (0.85) was a little low indicating possibility of error correlation. The modification index indicated

high error correlation between SSP4 and SSP5 (20.92). It was decided to drop item SSP4 since, on an examination of the description of the two

items, it appeared that item SSP4 was too general


Appendix C (Continued)

Iteration 4 SSP3, SSP5, SSP6, SSP7, SSP8, SSP9 x2 = 22.50; P = 0.01; GFI = 0.96; AGFI = 0.91; NFI = 0.95

The final model had both satisfactory l coefficient and excellent model fit. Therefore, no further modifications were done. The items are listed

below

Strategic supplier partnership (SSP)

SSP1* We rely on a few dependable suppliers

SSP2* We rely on a few high quality suppliers

SSP3 We consider quality as our number one criterion in selecting suppliers

SSP4* We strive to establish long-term relationship with our suppliers

SSP5 We regularly solve problems jointly with our suppliers

SSP6 We have helped our suppliers to improve their product quality

SSP7 We have continuous improvement programs that include our key suppliers

SSP8 We include our key suppliers in our planning and goal-setting activities

SSP9 We actively involve our key suppliers in new product development processes

SSP10* We certify our suppliers for quality

Further analysis To make sure no important items were deleted from the purification process, the four removed items for strategic supplier

partnership were observed carefully. It was found that SSP1, SSP2, and SSP10 are associated with supplier rationalization,

which is an important component in the partnership with suppliers

To test whether these three items form an independent construct, two two-factor models were tested by adding the six items

of strategic supplier partnership

Iteration 1 SSP1, SSP2, SSP10 x2 = 72.80; P = 0.00; GFI = 0.92; AGFI = 0.87; NFI = 0.91

SSP3, SSP5, SSP6, SSP7, SSP8, SSP9

The model tested in iteration 1 included the 3 items of supplier rationalization and the items from strategic supplier partnership. l Coefficient of

item SSP10 in iteration 1 was low (0.02). Item SSP10 was removed for the next iteration

Iteration 2 SSP1, SSP2 x2 = 35.75; P = 0.01; GFI = 0.96; AGFI = 0.92; NFI = 0.95


The model resulting from iteration 2 had both satisfactory l coefficient and excellent model fit. Therefore, no further modifications were done. A

new construct, supplier rationalization, was included in the further analysis. The items are listed below

Supplier rationalization (SR)

SSP1 We rely on a few dependable suppliers

SSP2 We rely on a few high quality suppliers

SCM practices-customer relationshipInitial Model CRP1, CRP2, CRP3, CRP4, CRP5, CRP6, CRP7, CRP8 x2 = 128.77; P = 0.00; GFI = 0.86; AGFI = 0.74; NFI = 0.85

l Coefficient of item CRP7 in the above model was low (0.46). Item CRP7 was removed for the next iteration

Iteration 1 CRP1, CRP2, CRP3, CRP4, CRP5, CRP6, CRP8 x2 = 90.56; P = 0.00; GFI = 0.88; AGFI = 0.77; NFI = 0.88

Although all l coefficient were good, the; AGFI (0.77) was low indicating possibility of error correlation. The modification index indicated high

error correlation between CRP2 and CRP3 (30.12). It was decided to drop item CRP3 since, on an examination of the description of the two

items, it appeared that item CRP3 could be constructed as part of CRP2.

Iteration 2 CRP1, CRP2, CRP4, CRP5, CRP6, CRP8 x2 = 33.07; P = 0.00; GFI = 0.95; AGFI = 0.88; NFI = 0.93

The model resulting from iteration 2 showed the improvements in all fit indices. However, the modification index still indicated some moderately

error correlation between CRP1 and CRP2 (17.97). It was decided to drop CRP1, based on the examination of the description of the two items, it

appeared that CRP2 subsumed CRP1



Iteration 3 CRP2, CRP4, CRP5, CRP6, CRP8 x2 = 9.69; P = 0.08; GFI = 0.98; AGFI = 0.94; NFI = 0.97


below

Customer relationship (CR)

CR1* We frequently evaluate the formal and informal complaints of our customers

CR2 We frequently interact with customers to set reliability, responsiveness, and other standards for us

CR3* We have frequent follow-up with our customers for quality/service feedback

CR4 We frequently measure and evaluate customer satisfaction

CR5 We frequently determine future customer expectations

CR6 We facilitate customers’ ability to seek assistance from us

CR7* We share a sense of fair play with our customers

CR8 We periodically evaluate the importance of our relationship with our customers

Information sharingInitial Model IS1, IS2, IS3, IS4, IS5, IS6, IS7 x2 = 80.42; P = 0.00; GFI = 0.89; AGFI = 0.79; NFI = 0.87

Although all l coefficient were good, the AGFI (0.79) was low indicating possibility of error correlation. The modification index indicated high

error correlation between IS1 and IS3 (41.19) and between IS1 and IS2 (15.53), it was decided to drop item IS1 since this item has error

correlation with the other two

Iteration 1 IS2, IS3, IS4, IS5, IS6, IS7 x2 = 18.44; P = 0.03; GFI = 0.97; AGFI = 0.93; NFI = 0.97


below

Information sharing (IS)

IS1* We share our business units’ proprietary information with trading partners

IS2 We inform trading partners in advance of changing needs

IS3 Our trading partners share proprietary information with us

IS4 Our trading partners keep us fully informed about issues that affect our business

IS5 Our trading partners share business knowledge of core business processes with us

IS6 We and our trading partners exchange information that helps establishment of business planning

IS7 We and our trading partners keep each other informed about events or changes that may affect the other partners

SCM practices-information qualityInitial Model IQ1, IQ2, IQ3, IQ4, IQ5 x2 = 10.78; P = 0.06; GFI = 0.98; AGFI = 0.94; NFI = 0.98

The initial model had both satisfactory l coefficients and excellent model fit. Therefore, no modifications were done. The items are listed below

Information quality (IQ)

IQ1 Information exchange between our trading partners and us is timely

IQ2 Information exchange between our trading partners and us is accurate

IQ3 Information exchange between our trading partners and us is complete

IQ4 Information exchange between our trading partners and us is adequate

IQ5 Information exchange between our trading partners and us is reliable

SCM practices-postponementInitial Model (POS1, POS2, POS3, POS4, POS5) x2 = 39.71; P = 0.00; GFI = 0.92; AGFI = 0.77; NFI = 0.83

l Coefficient of item POS1 in the above mode was very low (0.11). Item POS1 was removed for the next iteration

Iteration 1 POS1, POS2, POS3, POS4 x2 = 20.99; P = 0.00; GFI = 0.95; AGFI = 0.74; NFI = 0.89

Although all l coefficient were good, the AGFI (0.74) was very low indicating possibility of error correlation. A model run with either one of

these four items removed would not have yielded model fit statistics since only three items remained resulting in the degrees of freedom being

zero. Two two-factor models were tested by adding the items of strategic supplier partnership



Iteration 2 POS1, POS2, POS3, POS4 x2 = 74.62; P = 0.00; GFI = 0.93; AGFI = 0.89; NFI = 0.92


Iteration 3 POS1, POS3, POS4 x2 = 37.01; P = 0.07; GFI = 0.96; AGFI = 0.93; NFI = 0.94


The model tested in iteration 2 included the items from strategic supplier partnership and the items from the iteration 1 of postponement. The

model in iteration 3 also had all the items from strategic supplier partnership and all except item POS2 from postponement. Given the indicated

error correction between items POS1 and POS2 (19.16), it was decided to drop item POS2. Based on the examination of the description of the two

items, it appeared that POS1 subsumed POS2. The model resulting from iteration 3 showed improvement. No further modifications were done.

The items are listed below

Postponement (POS)

POS1 Our products are designed for modular assembly

POS2* Our production process modules can be re-arranged so that customization can be carried out later at distribution

centers

POS3 We delay final product assembly activities until customer orders have actually been received

POS4 We delay final product assembly activities until the last possible position (or nearest to customers) in the supply

chain

POS5* Our goods are stored at appropriate distribution points close to the customers in the supply chain

Time to marketInitial Model TM1, TM2, TM3, TM4 x2 = 4.73; P = 0.09; GFI = 0.99; AGFI = 0.94; NFI = 0.98

The initial model had both satisfactory l coefficients and excellent model fit. Therefore, no modifications were done. The items are listed below

Time to market (TM)

TM1 We deliver product to market quickly

TM2 We are first in the market in introducing new products

TM3 We have time-to-market lower than industry average

TM4 We have fast product development

Delivery dependabilityInitial Model DD1, DD2, DD3

Iteration 1 DD1, DD2, DD3 x2 = 82.89; P = 0.00; GFI = 0.89; AGFI = 0.77; NFI = 0.84

TM1, TM2, TM3, TM4

Since a model run with three items would not have yielded model fit statistics. A two-factor model was tested by adding the items of time to

market. l Coefficient of item DD1 was very low (0.11). Item DD1 was removed for the next iteration

Iteration 2 DD2, DD3 x2 = 47.90; P = 0.00; GFI = 0.92; AGFI = 0.80; NFI = 0.91

TM1, TM2, TM3, TM4

The model resulting from iteration 2 showed significant improvement. No further modification can be done. The items are listed below

Delivery dependability (DD)

DD1* We deliver the kind of products needed

DD2 We deliver customer order on time

DD3 We provide dependable delivery


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