supply chain

Supply chain integrationand product modularity

An empirical study of product performance forselected Hong Kong manufacturing industries

Antonio K.W. Lau and Richard C.M. YamThe Department of Manufacturing Engineering and Engineering Management,

City University of Hong Kong, Kowloon Tong, Hong Kong, China, and

Esther P.Y. TangThe Department of Management and Marketing,

The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China

Abstract

Purpose – While the beneficial impact of supply chain integration (SCI) and modular product designare generally acknowledged, few empirical studies have examined how an organization can achievebetter performance through SCI with modular product design. The purpose of this paper is to examinethe relationship between SCI and modular product design, as well as their impact on productperformance.

Design/methodology/approach – By surveying 251 manufacturers in Hong Kong, structuralequation modelling is used to test the research constructs and the hypothesized model.

Findings – The results confirm that information sharing, product co-development andorganizational coordination are crucial organizational processes within SCI. Companies that havehigh levels of product modularity appear to be good at product co-development and organizationalcoordination directly and at information sharing indirectly. Furthermore, companies that have highlevels of product co-development or product modularity appear to have better product performance.

Research limitations/implications – This paper theoretically and empirically identifies threespecific organizational processes within SCI (information sharing, product co-development andorganizational coordination), which affect modular product design and product performance. Thesemore specific findings were previously absent from the literature. However, the study is limited to thecross-sectional nature of a survey study, the operationalization of SCI and product modularity, and thenature of the product types.

Originality/value – This paper empirically examines the relationships between SCI and productmodularity, which has seldom been attempted in previous research. It clearly identifies exactly whichprocesses within SCI are directly and indirectly related to product modularity.

Keywords Supply chain management, Product design, Production planning, Manufacturing industries,Hong Kong

Paper type Research paper

1. IntroductionA growing body of literature on operations management has suggested that acompany will perform well if it has a high degree of supply chain integration (SCI); SCIis defined as organizational processes to integrate suppliers, customers and internalfunctional units in order to optimize the total performance of all partners in the supplychain (Frohlich and Westbrook, 2001; Lee, 2000; Mason-Jones and Towill, 1997;

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

www.emeraldinsight.com/0144-3577.htm

IJOPM30,1

20

International Journal of Operations &Production ManagementVol. 30 No. 1, 2010pp. 20-56q Emerald Group Publishing Limited0144-3577DOI 10.1108/01443571011012361

Armistead and Mapes, 1993; Stevens, 1989). Rungtusanatham et al. (2003) argue thatsupplier and customer integration are useful ways to acquire external resources fromsuppliers and customers, respectively. Such integration also reduces the uncertaintiesin process, supply, control and demand in business operations (Towill et al., 2002). Itpromotes integrative inventory systems or information sharing across the supplychain, which improves customer service and provides for a quick response in adynamic market (Lee and Whang, 2001; Lambert and Cooper, 2000).

In addition to supplier and customer integration, internal integration has focused onproduct development, using different terminologies such as functional coordinationand cross-functional teams (Mentzer, 2004; Vickery et al., 2003). Functionalcoordination measures the interaction and collaboration within a company (Kahn,1996, 2001; Stank et al., 1999; Kahn and McDonough, 1997). Cross-functional teamsbring marketing, research and development (R&D), manufacturing and purchasingpersonnel together to reduce costly product redesign, duplication and maintenance,and to improve product reliability and customer satisfaction (Mentzer, 2000, 2004;Ulrich and Eppinger, 2000). Many “best-practice” companies have taken advantage ofSCI to improve their performance (Morash and Lynch, 2002; Dyer, 2000; Lee, 2000).Table I shows the main literature on organizational processes within SCI.

In general, Table I shows that SCI involves three dimensions, i.e. supplier, customerand internal integration, and it includes many organizational processes that cut acrossthese three dimensions. Thus, SCI should be seen as a multifaceted construct, whichrequires a fine-grained empirical analysis (Campbell and Sankaran, 2005). Recentempirical studies may be too coarse to operationalize SCI and may not identify whichprocesses within SCI affect business performance. In their seminal paper, Frohlich andWestbrook (2001) identify the positive effects of SCI on performance, but theirmeasurement of SCI only includes supplier and customer integration, ignoring internalintegration. Rosenzweig et al. (2003) improve the measurement of SCI with a new SCIintensity construct, measuring supplier, customers and internal integration, in a singlestudy. While the construct broadly shows that SCI improves multiple manufacturingcapabilities and the performance of companies, it does not identify which SCI processesaffect performance. Vickery et al. (2003) conceptualize SCI as three organizationalprocesses (supplier partnerships, close customer relationships and cross-functionalteams), and measure the effectiveness of SCI on customer service and performance.However, that study does not identify which processes of SCI are the best predictors ofperformance. Few studies are, therefore, able to bring together an examination ofmultiple organizational processes within SCI. Statistically, such a study will not onlyallow us to better predict performance, but it will also help to identify the interactionsamong the processes within a single construct (Bagozzi and Heatherton, 1994; Carver,1989).

Modular product design is considered to be an effective approach for masscustomization and cycle time reduction (Duray et al., 2000; Feitzinger and Lee, 1997;Kotha, 1995; Pine, 1993), and thus increases strategic flexibility for manufacturers(Worren et al., 2002; Sanchez, 1995). Modular product design is created by separating aproduct system into relatively independent components and by specifying theinterfaces of the product system across interacting components (Schilling, 2000;Sanchez, 1995). The decision to implement product modularity is an important aspectin modular product design because different levels of modularity require different

SCI and productmodularity

21

product design processes (Duray et al., 2000; Ulrich and Eppinger, 2000; Ulrich, 1995).There is a continuum describing a product system’s degree of separateness, specificityand transferability of product components, according to whether the product systemsare loosely or tightly coupled (Schilling, 2000; Sanchez, 1995; Orton and Weick, 1990;Weick, 1976). Modular product design refers to a product with high productmodularity, whereas integrated product design refers to a product with low productmodularity (Sanchez, 1995).

Although both SCI and modular product design are common practices amongmanufacturers, only a few empirical studies explore them together (Fine et al., 2005).Parker and Anderson (2002) point out that manufacturers need to decide themodularity of their products with supply chain design; otherwise, they may lose theirvalue-adding activities to the suppliers and be edged out of the business. Sako (2002)argues that modular product design is critical to the product, process and supply chaindesign and usually requires the integration of designers, producers and consumers.Most of the literature on product modularity focuses on detailed, technical aspects of

Articles Organizational processes within SCI

Stevens (1989) Functional integration, internal integration and external integrationArmistead and Mapes (1993) Shared ownership of the management process system, level of

adherence to manufacturing plans, job titles spanning traditionalfunctions, integration of information systems, visibility and spread oftransmission of information

Lawrence (1997) Supplier relationship managementBowersox et al. (1999) Customer integration, internal integration, material and service

supplier integration, technology and planning integration,measurement integration, relationship integration

Lee (2000) Information integration, coordination and resource sharing (decisions,alignment of work), organizational relationship linkage(accountability, risks/costs/gain)

Chandra and Kumar (2000) Synchronization of activities at the member entity and aggregatingtheir impact through process, function, business and on to enterpriselevels, either at the member entity or the group entity

Macdonald and Beavis (2001) Integration of supply chain planning and customer interfaceKim and Narasimhan (2001) Independent operation stage, internal integration stage, external

integration stageLee and Whang (2001) Information integration, synchronized planning, workflow

coordination, new business modelsFawcett and Magnan (2002) Internal cross-functional process integration, backward integration

with valued first-tier suppliers, forward integration with valued first-tier customers

Han et al. (2002) Joint inventory management, identification of core competency ofsuppliers, trust building, integrated IT infrastructure

Vaart and Donk (2003) Transparency stage, commitment/coordination stage, integrativeplanning stage

Vickery et al. (2003) Supplier partnership, closer customer relationship, cross-functionalteams

Rosenzweig et al. (2003) Supplier integration, customer integration, internal integration

Note: The present study defines SCI in terms of the organizational processes identified in theliterature

Table I.The organizationalprocesses within SCI

IJOPM30,1

22

product modularity (Mikkola, 2003; Nobelius and Sundgren, 2002; Salvador et al., 2002;Hsuan, 1999). Empirical testing has seldom been used to examine the relationshipbetween product modularity and SCI (Fine et al., 2005).

There is considerable discussion in the literature as to whether modular productdesign and product modularity require closer integration in the supply chain, orwhether they are a mechanism which allows manufacturing systems to operateeffectively without integration (Gerwin, 2004; Leseter and Ramdas, 2002; Nobelius andSundgren, 2002; Fleming and Sorenson, 2001; Galvin and Morkel, 2001; Ulrich andEppinger, 2000; Fine, 1998; Sanchez, 1995, 1996, 1999; Sheu and Wacker, 1997). Thisdebate, which has been conducted at a theoretical level, can only be resolved in thelight of empirical studies of the possible relationship between product modularity andSCI (Parker and Anderson, 2002; Salvador et al., 2002; Krishnan and Ulrich, 2001).

The present study contributes to existing knowledge in two main ways. First, whileprevious studies mostly measure SCI as a single-faceted construct, the current studyidentifies SCI as a multifaceted construct including three organizational processes,i.e. information sharing, product co-development and organizational coordination. Thestudy then examines the impact of each process on product design and performance.As a result, the present study can identify the impact of individual SCI processes onproduct development and performance. In this way, the study follows therecommendation of Rosenzweig et al. (2003) to develop better measurements of SCI.

Second, this study explores the relationships between product modularity, SCI andproduct performance in a single empirical study. The study is thus a response to theliterature on SCI, and the literature on product development, both of which suggest thatSCI and product modularity should be analysed in a comprehensive way (Mikkola,2003; Salvador et al., 2002; Krishnan and Ulrich, 2001). In this study, productperformance refers to a product’s market performance, as measured by customersatisfaction, achievement of sales and profit goals, and the profitability of a company’sproducts (Song and Parry, 1999; Griffin and Page, 1993).

In the following sections the constructs of the study will be defined and thetheoretical development of the hypothesized research model will be discussed. This willbe followed by the statistical analysis and discussion of the results. The implications ofthe research will be discussed, together with suggestions for future study, in theconclusion.

2. Theoretical developmentRecent literature has highlighted SCI as being valuable to improve companyperformance (Froblich and Westbrook, 2001), but includes few discussions of theindirect effect of SCI on company performance (Vickery et al., 2003; Narasimhan andKim, 2002). Thus, the present study investigates whether SCI has a direct impact toimprove company performance. This study also looks into whether informationsharing, product co-development and organizational coordination help manufacturersdesign modular products and improve product performance.

2.1 Supply chain integrationAs noted above, SCI involves multiple organizational processes that integratesuppliers, internal functional units, and customers (Rosenzweig et al., 2003;Vickery et al., 2003; Kim and Narasimhan, 2001). It is not a particular process in


23

any specific business functions. Rather, it reflects multiple organizational activities orprocesses across suppliers, internal functional units and customers. Among theimportant processes whose contribution to SCI is evaluated in this study areinformation sharing, product co-development and organizational coordination acrossthe supply chain. These have been selected for empirical investigation because theliterature suggests they are crucial to product design and development.

2.1.1 Information sharing and product performance. One of the key organizationalprocesses within SCI is information sharing. This refers to the sharing of technological,marketing, production and inventory information across suppliers and customers(Stock and Lambert, 2001; Ayers, 2001; Lee, 2000; Fisher, 1997; Andel, 1997; Balsmeierand Voisin, 1996). Various authors have highlighted the importance of informationsharing in the supply chain in securing competitive advantage in a variety of ways,including improved understanding of market trends and customer needs, theacquisition of new ideas for products, and identification of ways of improvingproduction methods and reducing total cycle time (Mentzer, 2004; Huang et al., 2003;von Hippel, 1988). Other authors have pointed to corresponding weaknesses producedby poor information sharing across the supply chain (Singh et al., 2005). However, onlya few empirical tests have been conducted to verify the inter-relationships betweenmultiple SCI processes and business performance (Rosenzweig et al., 2003). It is alsonot known which SCI processes have direct or indirect relationships with productperformance. In addition, there is a concern about how to capitalize on the informationshared across the supply chain as the cost of information sharing cannot be ignored(Frishammar and Horte, 2005). In view of the above, H1 is suggested as follows:

H1. Information sharing within SCI has a positive relationship with productperformance.

2.1.2 Product co-development and product performance. Product co-development isanother important organizational process within SCI as it refers to joint efforts inproduct development across suppliers, customers and internal functional units(Mentzer, 2004; Takeishi, 2001; Stevens, 1989). Product co-development with suppliersand customers refers to joint product design, process engineering and productionoperations with key suppliers and customers, respectively. Integrated productdevelopment refers to close internal coordination from product design, processdevelopment and production to product launch. Different authors have emphasized theco-development of products with suppliers (Ragatz et al., 1997; Carter and Ellram,1994), customers (Callahan and Lasry, 2004; Song et al., 1997) and between internalfunctional units (Mentzer, 2004). However, as noted above, few empirical tests havebeen conducted to verify the inter-relationships between multiple SCI processes andbusiness performance, and it is not clear if SCI processes have direct or indirectrelationships with product performance. In particular, the coordination cost and time ofproduct co-development (Weele, 2002) and the risks of technological knowledgeleakage to supply chain partners cannot be ignored (Lau and Yam, 2004). To make atestable hypothesis, this study proposes H2 as follows:

H2. Product co-development within SCI has a positive relationship with productperformance.

IJOPM30,1

24

2.1.3 Organizational coordination and product performance. Some studies suggest thatorganizational coordination is crucial for SCI. This refers to internal and externalcoordination activities, sharing the right to make business decisions, and jointassessment/design of business systems across the supply chain (Lee, 2000). Externalcoordination activities are important to improve trust and commitment across thesupply chain partners and to help the partners to delegate decision making (Lee, 2000).Mentzer (2000) suggests that joint system development and shared decision makingwith suppliers and customers enhances the understanding of management decisionsacross the partners and, consequently, promotes the sharing of risks and resourceswithin the supply chain. This tends to reduce development cost and time, and improveprofit margins, in product development.

Internal coordination activities involve functional coordination, creating interactionand collaboration within a company (Kahn, 1996, 2001; Stank et al., 1999; Kahn andMcDonough, 1997; Kahn and Mentzer, 1996). Kahn (1996) argues that internalcoordination activities increase the understanding of the goals and activities amongdifferent functional units, which improves mutual trust and commitment to theorganization. As people trust each other and are more committed to their organizations,they are motivated to seek further coordination, which in turn improves productdevelopment performance (Bstieler, 2006). Frishammar and Horte (2005) also found thatcollaboration among internal units is positively related to product innovation. However,there is no empirical test to verify direct and indirect relationships among multiple SCIprocesses and performance (Rosenzweig et al., 2003). Thus, H3 of the study is posited:

H3. Organizational coordination within SCI has a positive relationship withproduct performance.

2.2 Product modularityThe concepts of product modularity emerged in the 1960s. Simon (1962) viewedproducts as complex systems made up of many interacting parts. Each part issubordinated to the product system hierarchically. To simplify the system, the productcan be designed as a set of subsystems so that the assembly of different subsystemscan develop new products. If a customer requests a product, the manufacturer canmake the product by assembling the subsystems with a short production lead time(Alexander, 1964; Simon, 1962). Starr (1965) defined the subsystems as“interchangeable parts modules”, which can be transferred between product lines.The process of designing, developing and producing modules that can be combined indifferent ways to produce new products is called product modularization (Ernst andKamrad, 2000).

In the 1970s and 1980s, while operations research scholars studied the optimizationof modular design (Shaftel and Thompson, 1977; Rutenberg and Shaftel, 1971; Evans,1970), group technology in product design was proposed for streamlining productdesign, production planning and manufacturing operations (Hyer and Wemmerlov,1984; Holler, 1980; Rajagopalan and Batra, 1975). Group technology is a concept tocapitalize on similarities in recurring tasks and component parts by regrouping themin product and process design (Hyer, 1984). In product design, group technology wasanalogous to product modularization (Jose and Tollenaere, 2005) and aimed to classifythe components based on geometric similarities such as shapes, dimensions ormanufacturing processes, by using coding and clustering systems (Hyer, 1984), such as


25

production flow analysis (Burbidge, 1982), single linkage clustering (McAuley, 1972)and rank order clustering (King and Nakornchai, 1982). The classification by codingsystems was useful for the efficient retrieval of previous design, design standardizationand variety reduction (Hyer, 1984).

From the 1990s onwards, modularity literature has been adopted by strategicmanagement and organizational behaviour to describe the nature of industry life cycle,strategic flexibility and supply chain management (Salvador et al., 2002; Schilling,2000; Fine, 1998; Sanchez, 1995; Henderson and Clark, 1990). For example, Sanchez(1995) argues that modularity leads to dis-integration of an organization. There-conceptualization of modularity has also been flourishing in diverse managementfields (Salvador, 2007).

In brief, the literature on product modularity deals with a number of features ofproduct components, including the extent to which modules are independent orseparate, the extent to which components are specific, and the extent to which modulesare transferable or reusable within the production process (Garud et al., 2003; Schilling,2000; Ulrich and Eppinger, 2000; Baldwin and Clark, 1997, 2000; Sanchez andMahoney, 1996; Sanchez, 1995; Ulrich, 1995; Ulrich and Tung, 1991; Orton and Weick,1990). The main, relevant literature on the definition of product modularity issummarized in Table II. Salvador (2007) and Jose and Tollenaere (2005) presentextensive literature reviews on defining product modularity.

In alignment with the above literature, the present study defined productmodularity as a continuum describing separateness, specificity and transferability ofproduct components in a product system. Separateness referred to the degree to whicha product could be disassembled and recombined into new product configurationswithout loss of functionality (Schilling, 2000). Specificity referred to the degree towhich a product component had a clear, unique and definite product function with itsinterfaces in the product system (Ulrich, 1995). Transferability referred to the degree towhich product components in a product system could be handed over and reused byanother system (Starr, 1965). Since most product components can be more or lessseparated, specified and transferred in a product system, all products may have somedegree of product modularity. If a product had high product modularity (i.e. modularproduct design), the product system had separate modules with well-specifiedinterfaces across the modules, such as those found in personal computers. The productmodules could be transferred to different product lines and progressive productdevelopment projects. Conversely, if a product had low product modularity (i.e.integrated product design), the product components were highly interlinked withoutwell-specified interfaces across the components. It was very difficult for the productcomponents to be transferred to other product lines and future product developmentprojects.

The literature on product modularity also includes some studies that examinedifferent types of modularity (Salvador et al., 2002; Ulrich, 1995). The present studymeasures the degree of product modularity, without distinguishing between types ofmodularity. There are two reasons for this. First, there is an extensive literature whereproduct modularity is operationalized as a single measure (Lin, 2003; Sako, 2002;Worren et al., 2002; Duray et al., 2000; Fixson, 1999; Sako and Murray, 1999; Sako andWarburton, 1999). Second, the present study collects empirical data relating to differentaspects of product modularity, and reviews the data in two pilot studies, as discussed

IJOPM30,1

26

Art

icle

sD

efin

itio

ns

Con

stru

cts

use

din

the

defi

nit

ion

s

Sta

rr(1

965)

Th

eob

tain

ing

ofth

em

axim

um

var

iety

ofas

sem

bli

esb

yco

mb

inin

ga

giv

enn

um

ber

ofp

arts

Com

pon

ent

re-c

omb

inab

ilit

y

Dog

ram

aci

(197

9)C

omp

onen

tco

mm

onal

ity

acro

ssp

rod

uct

lin

esC

omp

onen

tco

mm

onal

ity

McC

lell

and

and

Ru

mel

har

t(1

988)

Hig

hly

con

nec

ted

wit

hin

mod

ule

sb

ut

wea

kly

con

nec

ted

acro

ssm

odu

les

Con

nec

tiv

ity

Gol

ber

tgan

dZ

hu

(198

9)M

odu

lar

pro

du

ctd

esig

nco

nsi

sts

ofu

sin

gst

and

ard

par

tsan

dsu

bas

sem

bli

esin

av

arie

tyof

pro

du

cts

Sta

nd

ard

izat

ion

,p

rod

uct

var

iety

Ulr

ich

and

Tu

ng

(199

1)S

imil

arit

yb

etw

een

ph

ysi

cal

and

fun

ctio

nal

pro

du

ctar

chit

ectu

re,

min

imiz

atio

nof

inci

den

tal

inte

ract

ion

sb

etw

een

ph

ysi

cal

com

pon

ents

Sta

nd

ard

izat

ion

,in

ter-

chan

gea

bil

ity

Gar

ud

and

Ku

mar

asw

amy

(199

3)C

onfo

rmin

gto

stan

dar

diz

edin

terf

ace

spec

ifica

tion

sre

pre

sen

tsa

mod

ula

rd

esig

nin

wh

ich

inte

r-te

mp

oral

sub

stit

uti

onof

com

pon

ents

can

occu

rea

sily

Inte

rfac

est

and

ard

izat

ion

,su

bst

itu

tion

Ulr

ich

(199

5)T

he

way

offu

nct

ion

-com

pon

ent

map

pin

gan

dth

ed

egre

eof

dec

oup

lin

gof

pro

du

ctin

terf

aces

Fu

nct

ion

-to-

ph

ysi

cal

com

pon

ent

map

pin

g,

dec

oup

lin

gof

inte

rfac

esS

anch

ezan

dM

ahon

ey(1

996)

Inte

rdep

end

ent

and

clos

ely

cou

ple

dw

ith

inm

odu

les

bu

tin

dep

end

ent

and

loos

ely

cou

ple

dac

ross

mod

ule

sIn

dep

end

ence

,lo

ose

cou

pli

ng

Sch

illi

ng

(200

0)P

rod

uct

mod

ule

sar

esp

ecifi

ed,

dec

oup

led

,ca

nb

ere

com

bin

edan

dse

par

ated

acro

ssm

odu

les

Sep

arat

enes

s,sp

ecifi

city

Bal

dw

inan

dC

lark

(200

0)In

form

atio

nis

hid

den

wit

hin

mod

ule

san

din

form

atio

nis

vis

ible

acro

ssm

odu

les

Info

rmat

ion

hid

ing

and

vis

ibil

ity

Sak

o(2

002)

Ab

un

dle

ofch

arac

teri

stic

sco

nce

rnin

gm

odu

lein

terf

aces

,th

efu

nct

ion

-to-

com

pon

ent

map

pin

gan

dh

iera

rch

ies

ind

iffe

ren

tp

has

esof

the

pro

du

ctli

fecy

cles

Mod

ule

inte

rfac

es,

fun

ctio

n-t

o-co

mp

onen

tm

app

ing

and

hie

rarc

hie

sof

com

pon

ents

and

fun

ctio

ns

Mik

kol

aan

dG

assm

ann

(200

3)T

he

com

bin

atio

nef

fect

sof

the

rati

oof

the

nu

mb

erof

new

and

stan

dar

dco

mp

onen

tsw

ith

the

deg

ree

ofco

up

lin

gan

dsu

bst

itu

tab

ilit

y

Sta

nd

ard

and

new

com

pon

ents

,d

egre

eof

cou

pli

ng

,su

bst

itu

tab

ilit

y

Ger

shen

sonet

al.

(200

3)In

dep

end

ence

acro

ssm

odu

les,

low

con

nec

tiv

ity

bet

wee

nm

odu

les,

sim

ilar

com

pon

ents

are

gro

up

edS

imil

arit

y,

con

nec

tiv

ity

,in

dep

end

ence

Sal

vad

or(2

007)

Inth

ed

omai

nof

tan

gib

le,

asse

mb

led

arti

fact

sa

pro

du

ctsy

stem

ism

odu

lar

toth

eex

ten

tth

atit

sse

par

able

com

pon

ents

,or

mod

ule

s,ar

eco

mb

inab

le

Com

pon

ent

com

bin

abil

ity

,co

mp

onen

tse

par

abil

ity

Table II.Definitions of product

modularity


27

in the section on methodology. Different aspects of product modularity, and theircontribution to an overall construct of product modularity, can thus be examinedempirically. Not distinguishing modularity types simplified the questionnaire designso that the respondents could easily understand the questions. It also helped to rankdifferent levels of product modularity in different types of company. The disadvantageis that it prevented us from understanding the impact of specific modularity typeson SCI.

2.2.1 Information sharing and product modularity. Designing a product with highproduct modularity implies that the product is assembled from a set of independentmodules with standardized interfaces across different modules (Ernst and Kamrad,2000; Sanchez and Mahoney, 1996). The modules are highly differentiated and tightlyspecified. Consequently, they can be successfully outsourced to module suppliers(Novak and Eppinger, 2001; Schilling, 2000).

As product modules are outsourced to external partners, information sharingbecomes more important to specify and create modular products. In order to designmodular product interfaces effectively, it is necessary to obtain information on marketand customer preferences (Du et al., 2001), share information among different designersin the organizations involved (Brusoni and Prencipe, 2001), and share information onengineering parameters with supply chain partners (Erixon, 1996). Marketing,production and technological information obtained from suppliers and customers inexisting modular product development projects can be identified and reused to createbetter modular products in the future (Kotha, 1995).

However, some authors argue that as product modules and architectures are clearlydefined at the outset, the information within the modules could be hidden (Baldwin andClark, 2000) and iterative information and communications across the supply chainpartners may be reduced in the product development (Fine, 1998). As the relationshipbetween information sharing and modularity is not confirmed, this study proposes H4as follows:

H4. Information sharing within SCI has a positive relationship with productmodularity.

2.2.2 Product co-development and product modularity. Other literature suggests thatco-development with the module suppliers is an advantage in product development(Mikkola, 2003; Ragatz et al., 2002; Hsuan, 1999). Sabel and Zeitlin (2004) argue that thedevelopment of modular products requires a process of iterative co-development withsuppliers to redefine the interface specifications for new products. It also requiresco-development with customers to reduce costs and improve performance in futureproducts. In particular, if the modules are to be reused for the future productdevelopment projects, product co-development may help manufacturers to anticipatechanges in customer needs. Sanchez (1999) also suggests that integration amongmarketing, engineering design, manufacturing and logistics departments in the use ofmodular product design is required to address the full range of benefits of modularproduct development. Many case studies have suggested that product co-developmentwith suppliers, internal functional units and customers is important in modularproduct design (Brusoni and Prencipe, 2001.

However, some authors argue that well-defined interfaces of each module helpexternal suppliers to work on their particular modules alone and assure that the

IJOPM30,1

28

modules will interact effectively in the product development (Schilling, 2000). Thus, theintensity of supplier involvement in product development is lower (Leseter andRamdas, 2002). This loosely integrated supply chain would extend downstream andcould also benefit downstream customers. Thus, the relationship between productco-development and modular design is not clearly defined in the literature. H5 isproposed as follows:

H5. Product co-development within SCI has a positive relationship with productmodularity.

2.2.3 Organizational coordination and product modularity. Empirical studies foundthat manufacturers tend to coordinate their supply chain strategically andorganizationally when adopting modular design. Volkswagen has developed a“modular consortium” in Brazil (Dyer, 2000). In a case study of Chrysler Jeeps, closeand trusting relationships with customers and suppliers are required to resolveproblems of interface compatibilities between different modules (Hsuan, 1999). Sabeland Zeitlin (2004) argue that, when adopting modular product design, organizationsmust mutually assess each other’s internal business processes in order to find solutionsto technical and marketing problems in the development process.

However, some authors suggest that modular products are outsourced to modulesuppliers in a loosely integrated manner (Fine, 1998; Sanchez and Mahoney, 1996). Thisloosely coordinated supply chain allows manufacturers to flexibly change theirsuppliers for major components to gain cost or technological advantages (Fine, 1998).To take advantage of this loosely coordinated supply chain, manufacturers shouldallow their supply chain partners to stay at any physical distance, have independentmanagerial and ownership structures, and have diverse cultures with a low level ofelectronic connectivity (Fine, 1998). The organizational coordination can be loose asmodular design is adopted. The literature, therefore, suggests that the relationshipbetween organizational coordination and product modularity may not be clearlyidentified. To make a testable hypothesis, H6 is suggested:

H6. Organizational coordination within SCI has a positive relationship withproduct modularity.

2.2.4 Product modularity and product performance. The literature on productmodularity identifies a number of benefits of modularity. First and foremost among theadvantages are the reduced production costs which arise from economies of scalewhere modules have multiple applications, reducing the costs of setting up productionprocesses and the cost of keeping inventory (Fisher et al., 1999; Feitzinger and Lee,1997; Ulrich and Tung, 1991).

There are also benefits in terms of reduced development costs, where designprocesses have to be carefully specified and managed, and where the development ofnew modules is less complicated that the development of major integrated projects(Mikkola and Grassmann, 2003; Hargadon and Eisenhardt, 2000; Sanchez, 1999;Erixon, 1996; Ulrich and Tung, 1991).

In addition, modularity may improve a company’s ability to provide customerservice by quickly solving technical problems and delivering common parts andservices to clients, as well as making it possible to offer incremental upgrades andimprovements at marginal cost (Ulrich and Tung, 1991; Karmarkar and Kubat, 1987).


29

The overall effect of these advantages would be improved product performance andcustomer service.

However, some negative effects of modularity on product performance are alsofound in the literature. For example, the need to design modules for multipleapplications may prevent them from being optimized in any specific application, andthe use of standard modules may lead to a lack of differentiation in the final products(Desai et al., 2001; Robertson and Ulrich, 1998). These disadvantages may beparticularly important where the modules are visible to the customer, or where thephysical size or mass of the products is important (Ulrich and Ellison, 1999; Robertsonand Ulrich, 1998; Ulrich and Tung, 1991). Ramdas (2003) argues that empirical researchis needed to study the impact of modularity on product performance.

The literature, therefore, suggests that the impact of product modularity on productperformance may be mixed. However, more scholars have taken a positive view ofproduct modularity and, as a result, the following hypothesis is suggested:

H7. Product modularity has a positive relationship with product performance.

3. MethodologyTo test the research hypotheses, a survey was conducted among Hong Kongmanufacturers. This section explains the selection of the instruments for the researchconstructs, i.e. SCI, product modularity and product performance. Sampling andconstruct validity are discussed at the end of this section.

3.1 InstrumentsThe Appendix to this paper shows the five-point Likert-type measurement scales developedfor the evaluation of product modularity, SCI and product performance. The scales of SCIwere adapted from Narasimhan and Kim (2002), Frohlich and Westbrook (2001),International Manufacturing Strategy Survey (IMSS) II (1996) and Kahn (1996). Aftertesting the measures in two pilot studies as discussed below (Section 3.2), 39 items weredeveloped to measure the organizational processes within SCI (see the Appendix, Table AI).

Many operational definitions for the concept of product modularity have beendeveloped. Examples of these are product components’ separateness, specificity,standardization, interchangeability, reusability, transferability and decomposability(Table II). Some researchers have adapted these concepts to measure productmodularity in empirical studies (Worren et al., 2002; Duray et al., 2000). The presentstudy initially adapted measurement items from the work of Worren et al. (2002) andDuray et al. (2000). These items were further changed based on the results of two pilotstudies discussed later (Section 3.2).

Product performance, as measured in this study, was defined as customersatisfaction, achievement of sales and profit goals, and the profitability of a company’sproducts (Song and Parry, 1999; Griffin and Page, 1993). It measured how well thecompany did routinely in moving key products through the product developmentprocess to become commercial goods (Griffin and Page, 1993). Customer satisfaction wasa forward-looking performance indicator of how well customers would respond to acompany’s product performance. Achievement of sales and profit goals and profitabilitywere performance indicators of how well the company had done in the past (Best, 2003).By using these indicators, the study could assess how modular product designand SCI affected product performance in both forward and backward-looking ways.

IJOPM30,1

30

This method of measurement also facilitated comparisons among companies operatingin different market situations and company sizes (Ledwith, 2000).

Perceptual measurements were used as companies are reluctant to share objectiveperformance data because of confidentiality issues (Ward et al., 1996). Pagell andKrause (2004) further argue that where survey studies are cross-industry in nature, theindividual objective performance might vary in different industries, which wouldaffect the survey results. It is inappropriate to use objective measurements of salesvolumes, return on investment or profitability to rate the performance of companiesdiffering in size and market segment as company resources differ in each case(Ledwith, 2000). Furthermore, while there were quantitative modularity indexes tomeasure a company’s product modularity (Holtta-Otto, 2007; Jose and Tollenaece,2005), the indexes were not commonly used by the sampled industries and could not beclearly understood by the respondents in our pilot studies.

3.2 Two pilot studiesIn order to improve the comprehensibility of the draft questionnaires, two pilot studieswere conducted. In the first pilot study, 13 experts were interviewed to discuss thecontent and wording of the questionnaire. On average, interviews lasting betweenone hour and one and a half hours were conducted for each expert to review thequestionnaire. The experts were ten senior managers in the sample industries, and onerepresentative from each of the Federation of Hong Kong Industries, The Institute forSupply Management Hong Kong, and the Hong Kong Logistics Association. Thesecond pilot study was a pre-test carried out with a convenience sample of 37 part-timeMaster of science students working in local manufacturing industries. The studentshad an average of more than five years’ supervisory experience. Simple statisticalanalyses were used to test the reliability of the scales and the questionnaire wasimproved further (Flynn et al., 1990).

3.3 SamplingThe scope of the present research was limited to the plastics, electronics and toyindustries in Hong Kong. These industries were chosen for two reasons. First, they hadhigh product variety, which made them particularly suitable for research on the topicof the present study. The literature shows that industries with high product variety aremore likely to use modular product design. Salvador (2007) argues that productmodularity is intrinsically related to product variety. Ulrich (1995) argues that highproduct variety can be achieved by any product system, but modular product design isan economical way of increasing product variety by combining relatively few productcomponents. Worren et al. (2002) argue that modular product design minimizes thedesign and development costs of product variety by allowing the reuse of existingcomponents and production lines. A number of previous studies used industries withhigh product variety as subjects for studying modularity, e.g. the electronics (Meyerand Roberts, 1986), automobile (Nobeoka and Cusumano, 1997) and home appliance(Worren et al., 2002) industries.

The Hong Kong electronics, toy and plastics industries usually develop newproducts with a high product variety (SQW, 2001; Hong Kong Trade DevelopmentCouncil (HKTDC), 1998, 1999, 2000). HKTDC (2000) found that Hong Kong electronicsmanufacturers provide a wide range of consumer electronics, electrical home


31

appliances and electronic parts and components in a cost effective way (HKTDC, 2007).HKTDC (1998) found that Hong Kong plastics manufacturers make a wide variety ofproducts and their products serve diverse manufacturing sectors, from simple plasticcontainers to electronics and moulding machines. Hong Kong’s toy industry also has ahigh product variety, ranging from relatively simple and low-priced products tocomputer-controlled robotics and fashionable stuffed dolls with lively features (SQW,2001). Table III summarizes the product ranges of these three industries. Thus,companies in the three industries could develop both components and end-products ina diverse way for their customers (Enright et al., 2005; SQW, 2001; HKTDC, 1998, 1999,2000).

The second reason for choosing these industries is that they are representative ofglobal manufacturing as the leading export industries in the world (HKTDC, 1998).The electronics, toy and plastics industries are three of Hong Kong’s major industries,with manufacturing plants in the Pearl River Delta region of China and exports in 2006to the value of approximately HK$1,180 billion (HKTDC, 2007). The decisions ofmanagers in these industries have a significant impact upon the region.

The targeted respondents of the survey were senior product development managers,vice presidents or directors. They were requested to fill out the questionnaire. Follow-upfaxes and phone interviews were conducted by two trained interviewers to ensure dataquality. For some companies, multiple phone interviews were conducted to verify thedata quality.

Of the 1,452 companies contacted, 1,371 were reached (81 letters were undeliveredbecause of a change of address or the contact person having left the company) of whichseven companies were not in the targeted industries. Of the 1,364 targeted companiessuccessfully contacted, 285 responded to the survey, for a response rate of 20.9 per cent.The sample profile is shown in Table IV (Section 5.1). After the data cleaning process,34 cases were deleted because the companies had not completed the questionnaireproperly and refused to clarify their answers over the phone. Finally, 251 completedquestionnaires were analysed in this study, for an effective response rate of 18.4 per cent.

4. Data analysis4.1 Non-respondent bias and common method varianceTo detect non-response bias, a test was conducted to see if there were differencesbetween early respondents and late respondents in terms of variables relevant to theresearch hypotheses (Armstrong and Overton, 1977). The average values found by thesurvey instruments of the first 10 per cent of respondents were compared with those of

Industry Major product types

Electronics Parts and components, consumer electronics, computers and peripherals,telecommunications equipment, automotive electronics, and medical and healthcareelectronics

Toys Baby and children toy, educational toy, electronic toy, outdoor playground, and videogames

Plastics Polymers, plastic parts and components, plastic houseware, plastic constructionmaterials, plastic packaging, moulding machines, and plastic moulds and dies

Sources: SQW (2001); HKTDC (1998, 1999, 2000)

Table III.A summary of the majorproduct types in theindustries

IJOPM30,1

32

n Percentage

Type of industryElectronics 115 45.8Toys 57 22.7Plastics 79 31.5

Company size in Hong Kong a

1-49 168 66.950-99 44 17.5100-199 19 7.6200-500 11 4.4.500 6 2.4Nil 3 1.2

Company size in the mainland a

No operations 17 6.81-99 27 10.8100-199 33 13.0200-999 79 31.51,000-5,000 74 29.5.5,000 16 6.4Nil 5 2.0

Location of operations in Hong Kong b

Sales and marketing 210 83.6Logistics 133 52.9Production 34 13.5R&D 139 55.3

Location of operations in the mainland b

Sales and marketing 45 17.9Logistics 115 45.8Production 220 87.6R&D 117 46.6

Customer characteristicsc

Industrial customer 103 41.0Dealer/retailer 153 60.9End-user/consumer 56 22.3Others 1 –

Supplier characteristicsc

Raw material suppliers 186 74.1Standard component suppliers 102 40.6New component suppliers 44 17.5Module suppliersd 43 17.1Others 3 1.1

Notes: aThe total is different from sample size because a few respondents did not provide thisinformation. Considering that company size is not our research construct and the analysis techniqueused in this study could provide accurate parameter estimates under conditions of missing data (Miles,2000; Arbuckle and Wothke, 1995), this study used the other data from the respondents; bthe sampleincludes companies that have operations in both Hong Kong and the Mainland of China, so the total islarger than the sample size; cthe total is larger than sample size because the sample companies mayhave more than one type of customer or supplier for their main product; dmodule suppliers refers tosuppliers that provide a customer-specific component to the sampled company; n ¼ 251

Table IV.Demographic

characteristicsof the sample


33

the last 10 per cent of respondents using a t-test. The results of the t-test showed nostatistically significant differences between the two groups in terms of the means foritems, indicating that non-response bias should not be a problem in this study.Common method variance was also tested by using the Harman one-factor test (Kotabeet al., 2003). No single factor was apparent in the unrotated factor structure. This can beseen as evidence that common method variance problem may not be present.

4.2 Scale purification and construct validationScale purification was achieved by using a reliability test and exploratory factoranalysis. Cronbach’s alpha (a) was used to assess the scale reliability of each constructin the model (Figure 1). The reliability of all factors is reported in the Appendix,Table AI. The Cronbach alpha of every factor was greater than 0.7, which is a goodstatistical result (Johnson and Wichern, 1998). The instruments for the constructs werethen validated by exploratory factor analysis (i.e. principal components analysis, withvarimax orthogonal rotation). The results confirmed the structure of the constructs.For example, in product modularity items, the factor analysis produced a one-factorsolution from five items. The factor loadings were generally over 0.7, indicating goodfactor structures (Johnson and Wichern, 1998). Overall, a total of 39 measurement itemswere explored and ten factors identified.

Construct validation included tests for content, convergent and discriminantvalidity. Content validity is usually ensured by expert judgment or through anextended literature search. The present study has a high degree of content validity ofscales as the survey instruments were mainly adopted from the extant literature(Worren et al., 2002; Narasimhan and Kim, 2002; IMSS II, 1996), and then confirmed bythe two pilot studies discussed above. A confirmatory factor analysis was conducted tocheck the convergent validity and discriminant validity of the prior factor structures.

Figure 1.The hypothesizedresearch model

Supply chain integration

Supplier integration–information sharing

Customer integration–information sharing

Supplier integration–productco-development

Customer integration–productco-development

Internal integration–integrated productdevelopment

Supplier integration–organizationalcoordination

Customer integration–organizationalcoordination

Internal integration–functional coordinationOrganizationalcoordination

Product co-development

Informationsharing

Productmodularity

Productperformance

H1

H2

H3

H4

H5

H6

H7

IJOPM30,1

34

The results are reported in the Appendix, Table AI. The overall fit of the confirmatoryfactor analysis was good (x 2 ¼ 1,079.8925, df ¼ 644, x 2/df ¼ 1.6769, comparative fitindex (CFI) ¼ 0.9838, incremental fit index (IFI) ¼ 0.9839). All standardized factorloadings were highly significant, which indicates good convergent validity among theinstruments of each construct. The modification indices in the confirmatory factoranalysis for omitted paths shown no significant cross-loadings among the items (i.e. allresults are below 0.85), indicating good discriminant validity (Kline, 1998). Theseresults support the overall validity of the proposed factor structure of the model.

4.3 Model and hypothesis testingThe hypothesized research model was tested by structural equation modelling. AMOS4.02 was used to solve the structural equation models (Arbuckle and Wothke, 1995).

As the model involves a multifaceted concept of SCI, a partial disaggregationmodelling technique was adopted, as suggested by Bagozzi and Heatherton (1994). Thethree components of SCI (i.e. information sharing, product co-development andorganizational coordination) are each addressed using at least two indicators, wherebythe components are allowed to covary (Bollen, 1989, p. 244). Composites of validateditems for each dimension (e.g. supplier integration-product co-development, customerintegration-product co-development, and internal integration) are treated as indicatorsof a single component (in this case, product co-development). A statistically significantmodel suggests that indicators measure a single component which can predict productmodularity and performance. A partial disaggregation model is a more fine-grainedrepresentation of a multifaceted construct. It can represent the hierarchical structure ofcomponents within a single construct, while keeping the concept of the construct as awhole (Bagozzi and Heatherton, 1994). It can also estimate the correlations betweendimensions, components and constructs, while taking into account measurement errorsin the model. In practice, it has low levels of random error in items, a few parametersthat must be estimated, and a small number of items per factor. This modellingtechnique thus leads to better model specification (Bagozzi and Heatherton, 1994).

In the analysis of the model, maximum likelihood estimation and standardizedregression weighting were used for interpretation (Schumacker and Lomax, 1996).Composite scales of the corresponding constructs determine each organizational processof SCI, as well as product modularity and performance (Swafford et al., 2006; Bagozziand Heatherton, 1994). Multiple indices of fit were used to specify the overall model fit,including CFI, IFI and relative x 2. The combination of maximum likelihood estimationwith CFI and IFI has little bias towards underestimating the fit of the model andsampling variability, leading to accurate model testing (Bentler, 1990). The values ofboth CFI and IFI, both over 0.9, indicate a good model fit. In addition, the relative x 2, alsocalled thex 2/df ratio, is suggested to test the model fit (Arbuckle and Wothke, 1995). Thex 2/df ratio of less than five indicates that there was acceptable model fit (Marsh andHocevar, 1985). The research hypotheses were validated with the significance of a t-testin each path with parameter estimates from the structural equation modelling.

5. Results5.1 Company profilesTable IV gives details of the companies included in the sample. It indicates the proportionsfrom each of the electronics, toy and plastics industries and the size of the companies.


35

Table IV also indicates the proportion of the companies having specific functionsbased in Hong Kong and the mainland of China, distinguishing sales and marketing,R&D, logistics and production. Finally, it gives some details of the companies’ majorsuppliers and customers. The figures shown in Table IV indicate that the sampleis similar to those in previous surveys of Hong Kong’s manufacturing industry (FHKI,2003; HKTDC, 1998), and is representative of the general situation in the sampledindustries.

5.2 Descriptive statisticsTable V shows descriptive statistics for all measures in Figure 1. In the table, seven outof eight variables of SCI are positively and significantly ( p , 0.05) associated withproduct modularity; the exception is internal integration-functional coordination(II-FC). In addition, six out of eight variables of SCI are positively and significantly( p , 0.05) associated with product performance; the exceptions are organizationalcoordination with customers and supplier integration (CI-OC and SI-OC). A positiveassociation between product modularity and performance was found ( p , 0.01).

5.3 Model testingFigure 2 shows the structural equation model of the hypothesized research model. Thetwo-way arrows represent the correlation coefficients between two connected variablesand the one-way arrows represent the regression coefficients from an independentvariable to a dependent variable (arrowhead). The standardized path coefficients withthe significance of the t-test are displayed beside the arrows to represent the significanceof the correlation or regression coefficients, respectively, (Worren et al., 2002).

According to the empirical results, H1 and H3 are not supported, indicating thatinformation sharing and organizational coordination do not lead to improved productperformance. But, H2 is supported, indicating that product co-development acrosssuppliers, customers and internal functional units directly leads to superior productperformance (r ¼ 0.21, p , 0.01). The result suggests that customer satisfaction, salesand profit goals, and profitability can be achieved if manufacturers co-develop productswith external and internal parties. As posited in H4-H6, the empirical results show thatproduct co-development (r ¼ 0.20, p , 0.05) and organizational coordination (r ¼ 0.18,p , 0.05) are positively and significantly associated with product modularity. Asposited in H7, the results show that product modularity is significantly associated withproduct performance (r ¼ 0.11, p , 0.1) although the coefficient is small. This suggeststhat products with high product modularity have slightly better performance thanproducts with low product modularity in the sampled industries.

Apart from testing the hypotheses, the results show that product co-development issignificantly correlated with organizational coordination (r ¼ 0.49, p , 0.01) andinformation sharing (r ¼ 0.53, p , 0.01). Organizational coordination is also correlatedwith information sharing (r ¼ 0.44, p , 0.01). According to the partial disaggregationmodelling, these inter-correlations among the three organizational processes indicatethat they are facets of a single underlying factor (Bagozzi and Heatherton, 1994),which, in this study, is SCI. The results should not be used to verify any indirectrelationship if that relationship goes through these inter-correlations in the model.To be conservative, the present study only proposes indirect relationships if thoserelationships are supported by the existing literature.

IJOPM30,1

36

Mea

nS

DP

MP

PS

I-IS

SI-

PC

SI-

OC

CI-

ISC

I-P

CC

I-O

CII

-FC

II-I

PD

Pro

du

ctm

odu

lari

ty(P

M)

2.73

631.

0511

1P

rod

uct

per

form

ance

(PP

)3.

4183

0.71

820.

176

**

1S

up

pli

erin

teg

rati

on-i

nfo

rmat

ion

shar

ing

(SI-

IS)

2.76

491.

0327

0.18

7*

*0.

163

**

1S

up

pli

erin

teg

rati

on-p

rod

uct

co-d

evel

opm

ent

(SI-

PC

)2.

6707

1.02

480.

197

**

0.19

6*

*0.

401

**

1S

up

pli

erin

teg

rati

on-o

rgan

izat

ion

alco

ord

inat

ion

(SI-

OC

)1.

8008

0.91

280.

207

**

0.05

30.

351

**

0.38

5*

1C

ust

omer

inte

gra

tion

-in

form

atio

nsh

arin

g(C

I-IS

)2.

9920

1.03

070.

170

**

0.12

8*

0.70

8*

*0.

269

*0.

271

**

1C

ust

omer

inte

gra

tion

-pro

du

ctco

-dev

elop

men

t(C

I-P

C)

3.15

941.

0546

0.22

7*

*0.

174

**

0.22

2*

*0.

525

*0.

221

**

0.40

0*

*1

Cu

stom

erin

teg

rati

on-o

rgan

izat

ion

alco

ord

inat

ion

(CI-

OC

)1.

8825

0.96

160.

272

**

0.07

10.

299

**

0.35

3*

0.78

8*

*0.

365

**

0.30

9*

*1

Inte

rnal

inte

gra

tion

-fu

nct

ion

alco

ord

inat

ion

(II-

FC

)3.

6922

0.88

070.

103

0.20

7*

*0.

351

**

0.27

7*

0.09

40.

275

**

0.18

8*

*0.

090

1In

tern

alin

teg

rati

on-i

nte

gra

ted

pro

du

ctd

evel

opm

ent

(II-

IPD

)3.

547

0.97

460.

128

**

0.12

9*

0.23

9*

*0.

256

*0.

054

0.15

7*

0.17

4*

*0.

009

0.66

5*

*1

Notes:

* ,*

* Cor

rela

tion

sar

esi

gn

ifica

nt

atth

e0.

05an

d0.

01le

vel

s(t

wo-

tail

ed),

resp

ecti

vel

y;n¼

251

Table V.Correlation matrix built

on Pearson correlation


37

On the other hand, the study found that the measurement error of functionalcoordination is correlated with that of integrated product development, as indicated bymodification indices. Clearly, functional coordination could be related to integratedproduct development (Kahn, 1996). In fact, periodic interdepartmental meetings andformal teamwork are common practices usually adopted in integrated productdevelopment (Kahn and McDonough, 1997; Kahn and Mentzer, 1996). To further checkthe correlation among measurement errors, an alterative model was tested. Thealternative model is similar to the model in Figure 2, except that both functionalcoordination (II-FC) and integrated product development (II-IPD) are excluded from theproposed components of SCI. The excluded variables are specified as two indicators ofa new component, called internal integration, within the construct of SCI. In otherwords, in the alternative model, there are four components within SCI, i.e. informationsharing, product co-development, organizational coordination, and internal integration.The overall fit of the alterative model was poorer than the original model. This resultempirically supports the model shown in Figure 2.

6. DiscussionThe overall model in Figure 2 shows that SCI includes, but may not be limited to, threeorganizational processes (i.e. information sharing, product co-development andorganizational coordination). These processes are interrelated and have differentimpacts on product modularity and performance. This broadly agrees with the view in theliterature that SCI has significant and positive effects on product design and development.This study further suggests that SCI involves three organizational processes and one ofthem (product co-development) can directly improve product performance. The othertwo processes (product co-development and organizational coordination) may be

Figure 2.The structural modelresults

Supplier integration–informationsharing

Customer integration–informationsharing

Supplier integration–product co-development

Customer integration–Product co-development

Internal integration–integratedproduct development

Supplier integration–organizationalcoordination

Customer integration–organizational coordination

Internal integration–functionalcoordination

Organizationalcoordination

Product co-development

Informationsharing

Productmodularity

Productperformance

n.s.

0.21***

n.s.

n.s.

0.20**

0.18**

0.11*

0.84***

0.84***

0.78***

0.68***

0.13**

0.14**

0.85***

0.93***Overall model fit

χ2/df = 4.4310; CFI = 0.98; IFI = 0.98*P<0.1; **P<0.05; ***P<0.01

Notes: Correlation between information sharing and product co-development is 0.53***; correlationbetween information sharing and organizational coordination is 0.44***; correlation between productco-development and organizational coordination is 0.49***

e1

e2

e3

e4

e5

e6

e7

e8

e9 e10

0.65***

Supply chain integrationIJOPM30,1

38

complementary in nature in that they can indirectly improve product performancethrough modular product design. Detailed discussion on each hypothesis is developedbelow.

6.1 SCI and product performanceThe results of testing H1-H3 provide more evidence and specific information on howSCI is related to product performance.

6.1.1 Information sharing and product performance (H1). The empirical results ofthe test on H1 are contrary to the expectations based on the literature. According to theliterature, information sharing across suppliers and customers on performance shouldbe firmly rooted in product development and supply chain management. However, thepresent study is not unique in finding evidence against this hypothesis, as similarresults have been reported in the field of market orientation (Frishammer and Horte,2005). Some literature argues that information shared by customers may be restrictedto what is familiar to the customers (Bennett and Cooper, 1981). Hamel and Prahalad(1994) suggest that customers cannot foresee future changes, and that companies mayfail to develop innovative products because they are attentive to the needs of currentcustomers. Katz (2003) also argues that customers sometimes ask for familiar productsand encourage manufacturers not to innovate, since new products usually require thecustomer to put in new supporting resources for the product and this leads to a wasteof some customers’ existing resources. Similarly, suppliers may provide familiar ideasto manufacturers in product development in order to protect the value of their existingresources, such as production capacity and engineering knowledge. By limitingthemselves to information acquired from current customers and suppliers,manufacturers might restrict their innovation capabilities in product development.

Frishammar and Horte (2005) argue that the information which is provided bycustomers and suppliers may not be used adequately in product development.Sometimes it is difficult to translate the external information into a useful form that canbe used in product development in a short period of time. If a product market ischaracterized by constantly changing technologies and customer preferences,manufacturers may not have enough time to apply the information, and if it is notapplied it cannot affect product performance.

6.1.2 Product co-development and product performance (H2). Consistent withthe hypothesized model, the results of testing H2 support the existing literature in theconclusion that product co-development is required to improve product performance(Griffin, 2002; Hargadon and Eisenhardt, 2000; Brown and Eisenhardt, 1995; Clark,1989). The participation of multiple internal parties in product development facilitatesknowledge sharing, iterative learning and concurrent problem solving (Clark, 1989).It can potentially bring together multiple functional units so as to address the marketrequirements of new products. It also allows manufacturers to manage design changesflexibly as design problems can be addressed at an earlier stage in the process. A failureto bring together all the internal parties in product development may extend productdevelopment time, increase costs and result in products that do not match marketrequirements, all of which can negatively affect product performance.

The results also show that product co-development with suppliers and customershelps manufacturers to develop new products (Dyer, 2000; Brown and Eisenhardt,1995; Clark, 1989). Where a new product is complex and the product market is rapidly


39

changing, technological and marketing capabilities required for product developmentare often not owned by a single manufacturer (Dyer, 2000). Manufacturers thustend to keep core capabilities in-house while working together with suppliers andcustomers in product development. This allows manufacturers to take advantage oftheir capabilities and to capitalize on supply chain partners’ capabilities in productdevelopment (Takeishi, 2001; Clark, 1989).

In addition, the results show that product co-development with suppliers, integratedproduct development, and product co-development with customers can be combined asan organizational process in SCI. This is in line with the finding of Sherman et al. (2000)that manufacturers that put an emphasis on one integration activity also stress thedevelopment of other integration activities. In this vein, the present study suggests thatmanufacturers that integrate internal functional units in product development alsoemphasize the opportunities of product co-development with suppliers and customers.Similarly, manufacturers that neglect integrated product development also ignore theopportunities of product co-development with suppliers and customers.

6.1.3 Organizational coordination and product performance (H3). The results oftesting H3 show that organizational coordination is not correlated with productperformance. One possible explanation for this finding is that there is a trade-off betweenthe cost of organizational coordination and its benefits. The adoption of organizationalcoordination is costly as it may involve frequent travel to partners’ offices,inter-organizational information technology (IT) systems, mutual understanding andtrust building activities and complex contract agreements (Weele, 2002). A high level ofcommitment from the supply chain partners is required. Tidd et al. (2001) argue that,although closely integrating supply chain partners may help to reduce manufacturingcosts, it complicates the product development processes. Thus, it is possible that the highcost of organizational coordination offsets its benefits in product development. A numberof empirical studies also find negative effects of organizational coordination with externalparties, such as increased development time for building organizational coordination(Zirger and Hartley, 1994), and expensive coordination costs (Ittner and Larcker, 1997).

Another possible explanation for this finding is that organizational coordinationefforts may facilitate modular product design, which in turn affects productperformance indirectly. As discussed in Section 2.1.3, organizational coordinationrefers to sharing the right to make business decisions, and joint assessment/design ofbusiness systems across the supply chain. These activities probably promote trust andmutual commitment in the long-term and, initially, may involve high coordinationcosts, as discussed above. Thus, it is possible that the benefits of organizationalcoordination are not significant in the short-term. Rather, after a period of time, afterthe organizationally coordinated supply chain partners perform well in productdevelopment or other business activities, the benefits of organizational coordinationcould be significant. Based on the structural model in Figure 2, the present studyindicates that after the organizationally coordinated supply chain performs wellin modular product design the manufacturer can improve product performance.This cross-sectional empirical study cannot test this argument. Future research will beconducted to examine it on a longitudinal basis.

6.2 SCI and product modularityH4-H6 provide more evidence on how SCI interacts with product modularity.

IJOPM30,1

40

6.2.1 Information sharing and product modularity (H4). In contrast to thehypothesized model, information sharing is not significantly related to productmodularity. The empirical results of the present study suggest, however, thatinformation sharing is related to product co-development and organizationalcoordination, as shown in Figure 2. This suggests that information sharing has anindirect effect on product modularity. Sabel and Zeitlin (2004) argue that productco-development requires supply chain organizations to share information onbenchmarking, parallel engineering and design problems and modifications.Information sharing is required to hold the developers in organizations together(Orton and Weick, 1990). For example, Dell, as one of the leading manufacturers in themodular computer market, has an internet-based information system sharing a rangeof information with their supply chain partners to ensure system integration andon-time delivery (Sabel and Zeitlin, 2004; Lee, 2000).

6.2.2 Product co-development and product modularity (H5). Consistent with thehypothesized model, the present study found that product co-development issignificantly correlated with product modularity. This finding supports the argumentsfrom the product design literature that product co-development with suppliers, internalfunctional units and customers is crucial in solving technical problems and specifyingthe interfaces of product modules in the early stages of product development (Sanchez,1995). Product co-development is also identified as a crucial component within SCIwhen solving modularity problems (Sabel and Zeitlin, 2004; Mikkola, 2003; Dyer, 2000;Hsuan, 1999).

6.2.3 Organizational coordination and product modularity (H6). Consistent with thehypothesized model, the present study found that there is a positive relationshipbetween organizational coordination and product modularity. This finding supportsthe arguments from the product design literature that modular product design requiresextensive internal and external coordination in the development processes in order tospecify and modify the interface of modules and overall product design (Sabel andZeitlin, 2004; Brusoni and Prencipe, 2001). Therefore, organizational coordination iscrucial to solving the modularity problems, as indicated in the product developmentliterature (Sabel and Zeitlin, 2004; Mikkola, 2003; Dyer, 2000; Hsuan, 1999).

Overall, in reviewing the various discussions about product modularity and SCI, thepresent study concludes that modular product design generally requires the directsupport of product co-development and organizational coordination and indirectsupport from information sharing. In contrast to the findings of Fine (1998) that looselyintegrated supply chains are preferred for modular product design, this study showsthat a tightly integrated supply chain is required if modular product design is adopted.In product development, a lot of coordination effort is required to ensure that theinteractions among product components are well-developed because it is difficult toestablish a set of clearly defined interfaces across product modules (Sabel and Zeitlin,2004). Malerba and Orsenigo (2000) also argue that using tightly integrated designersto co-develop product modules is essential to make sure that the overall productmodules are properly designed, evaluated and refined with appropriate costings inproduct development. In fact, some leading companies have developed integrationmechanisms for suppliers, customers and internal functional units to ensure goodinteractions among components in product development (Sabel and Zeitlin, 2004; Dyer,2000). As the first large-scale empirical study (as far as can be ascertained) directly to


41

test the relationship between modularity and SCI, the present study supports the needfor further research on this topic.

6.3 Product modularity and product performance (H7)From the survey results, product modularity is significantly correlated with productperformance. The present study supports H7 that, overall, modularity leads to betterperformance. As discussed in Section 2.2.4, the adoption of modular product designimproves design and manufacturing capabilities and thus improves productperformance.

However, the low coefficient in the relationship may suggest that modularity hassome negative effects on performance, as discussed in Section 2.2.4. For example, Kimand Chhajed (2000) found that common modules reduce the perceived difference inquality between premium and economic products and adversely affect profits if there isa higher expectation for the difference in quality. In fact, Toyota is cautious on productmodularization as the modular approach may lead to lower standards of productintegrity than Toyota demands in its vehicles (Dyer, 2000). Thus, future research willneed to be conducted to further examine the relationships between modularity andperformance by inserting mediators such as production cost (Robertson and Ulrich,1998), quality (Ulrich and Tung, 1991) or strategic flexibility (Worren et al., 2002) in themodel development.

7. Conclusion7.1 Implications for researchThe role of tightly integrated supply chains in improving company performance hasbeen identified in previous research (Rosenzweig et al., 2003; Frohlich and Westbrooks,2001). The present study theoretically and empirically identifies three specificorganizational processes within SCI (information sharing, product co-development andorganizational coordination), which affect modular product design and productperformance. These more specific findings were previously absent from the literature.

Consistent with the literature, the findings of the present study indicate a direct,positive relationship between product co-development and product performance. Thissuggests that product co-development across suppliers; customers and internalfunctional units directly affect product performance in the sampled industries.However, in contrast to previous studies, no empirical evidence was found to supportdirect effects between information sharing and product performance, or betweenorganizational coordination and product performance. Rather, the findings suggestthat it is difficult to capitalize on the benefits of information sharing and organizationalcoordination. A great deal of effort may be required to manage these processes in orderto achieve better product performance. This study supports the literature in identifyingthe importance of product co-development on product performance but, in contrast tothe existing literature, suggests that the impact of information sharing andorganizational coordination on product development has been overestimated.Information sharing and organizational coordination may benefit other aspects ofbusiness performance, such as productivity, market share, return on investment or leadtime (Rosenzweig et al., 2003; Frohlich and Westbrook, 2001). To further investigatethis, future studies might build upon the present study by including additionalperformance indicators.

IJOPM30,1

42

The present study empirically examines the relationships between SCI and productmodularity, which has seldom been attempted in previous research. The empiricalfindings show that product co-development and organizational coordination have directrelationships with product modularity, whereas information sharing may be indirectlyassociated with product modularity. The literature indicates that decisions on modularityrequire consideration of the supply chain design and coordination (Parker and Anderson,2002; Sako, 2002; Nobelius and Sundgren, 2002; Salvador et al., 2002) with no reference tospecific processes within SCI. The current study clearly identifies exactly which processeswithin SCI are directly and indirectly related to product modularity. This study thus raisesinteresting questions that require further investigation with Fine’s (1998) theory ofmodular product design Can we reject Fine’s theory? If not, can our empirical findings bealigned with Fine’s theory? Specifically, which supply chain processes should be kept, oreven strengthened, for modular product design?

Although the relationship between product modularity and SCI has been anassumption behind much of the product development literature, this is probably thefirst time that it has been demonstrated empirically with a large group of companies inthe sampled industries. The result has the important implication that, although theassociation between modularity and performance is weak, modular product designwould improve product performance across industries. Researchers should make moreeffort to study this relationship for companies of different sizes and in differentindustries. For example, researchers may study the impact of modularity in theclothing and food industries, which have recently adopted modular design incustomizing new apparel with different colours and styles, e.g. Nike and Reebok(Kumar et al., 2007) and in personalizing colours, shapes or ingredients in snacks, suchas My M&M’s custom candy (Mars, 2008).

7.2 Managerial implicationsThe present study has demonstrated that information sharing, productco-development and organizational coordination affect product performance indifferent ways. Managers should put more emphasis on these specific aspects of SCI,especially when linked with modular product design (Rosenzweig et al., 2003; Parkerand Anderson, 2002).

The study also suggests that SCI affects modular product design. Managers shouldconsider involving their suppliers, internal functional units and customers in earlydesign stages, especially in the decisions relating to product modularity. To achieveproduct co-development with suppliers and customers, managers should identify,assess and qualify competent suppliers as a major supply base for the module designand production (Mentzer, 2004). This would enhance the company’s capability tomodularize products successfully by leveraging the technological resources from thesupply base. By involving customers in modular product design, managers and thecustomers would derive mutual benefit from sharing technological knowledge andR&D skills. Managers would also understand better how their customers will use theirproducts and anticipate their needs. To develop a robust coordination environment forproduct co-development, managers should share production plans, inventorymix/level, marketing and technological information with the supply chain partners.

The present study has demonstrated that product modularity has a direct effect onproduct performance. Manufacturers should consider how to modularize products by


43

adopting proper design methods, such as modular function deployment (Erixon, 1996),design for modularity life cycle (Kamrani and Salhieh, 2002) or design rules proposedby Baldwin and Clark (2000).

7.3 Study limitationsThis study had limitations as specified below, which help to identify potential areas forfuture studies.

Hong Kong industries consist of a large volume of small and medium-sizedenterprises working on contract manufacturing. This structure may be different fromthat in other regions. Thus, the conclusions of this study may not be directlygeneralizable to other industries (Rosenzweig et al., 2003). Future studies of a similarkind in other regions would enhance the understanding of the inter-relationshipsamong SCI, product modularity and performance.

The present study used a single key informant for data collection. The underlyingassumption behind this method is that a senior manager, by virtue of his or herposition in the company, is capable of providing opinions and perceptions that canreflect the company’s behaviour (Philips, 1981). While there might be a concern thatrespondent bias could be a problem, all the reliability and validity tests in this studyindicated that it was not. This study also adopted multiple item scales to measure eachconstruct in the survey questionnaire and had multiple follow-up phone interviews toclarify the survey data. In future research, a multiple informant approach could beadopted. However, the complications of conducting research using multiple informantsand the practical difficulties of using information from such research should not beunder-estimated (Kumar et al., 1993; Philips, 1981).

The findings of the present study are limited to the cross-sectional data used.However, product modules may affect multiple product families across variousproduct life cycles as the modules can be reused several times for future productdevelopment. Thus, the relationship of modularity to both short-term and long-termproduct performance could be measured. In future research, multiple cross-sectionalanalyses in different time frames could be used to study the impact of modularity onperformance over an extended period.

The present study only examines three organizational processes within SCI, leavingother organizational processes within SCI unexamined. For example, the adoption ofintegrative ITs may be an important supply chain activity, which affects businessperformance (Vickery et al., 2003). Future research may identify more organizationalprocesses within SCI and examine how these processes affect company performance,which would contribute significantly to the supply chain management literature.

The present study is limited to the operationalization of SCI and productmodularity. Worren et al. (2002) found that product modularity cannot be capturedentirely using existing scales. The multifaceted nature of SCI also means that manyorganizational processes within SCI could be explored. Even though this research hasconsidered all instruments in the literature, and validated the instruments used via twopilot studies and a large-scale survey, the measure of modularity is limited to differentmodularity types and other quantitative measurements. The research findings are alsolimited to the nature of the product types (i.e. end-products against components), whichis not assessed in this study. Future research should re-examine the possibility ofdeveloping further measurements.

IJOPM30,1

44

References

Alexander, C. (1964), Notes on the Synthesis of Form, Harvard University Press, Cambridge, MA.

Andel, T. (1997), “Information supply chain: set and get your goals”, Transportation &Distribution,Vol. 8 No. 2, pp. 33-6.

Arbuckle, J.L. and Wothke, W. (1995), Amos 4.0 User’s Guide, SmallWaters Corporation,Chicago, IL.

Armistead, C.G. and Mapes, J. (1993), “The impact of supply chain integration on operatingperformance”, Logistics Information Management, Vol. 6 No. 4, p. 9.

Armstrong, J.S. and Overton, T.S. (1977), “Estimating non-response bias in mail surveys”,Journal of Marketing Research, Vol. 16, pp. 396-400.

Ayers, J.B. (2001), Handbook of Supply Chain Management, APICS Series on ResourceManagement, The St Lucie Press, Boca Raton, FL.

Bagozzi, R.P. and Heatherton, T.F. (1994), “A general approach to representing multifacetedpersonality constructs: application to state self-esteem”, Structural Equation Modeling,Vol. 1 No. 1, pp. 35-67.

Baldwin, C.Y. and Clark, K.B. (1997), “Managing in an age of modularity”, Harvard BusinessReview, September-October, pp. 84-93.

Baldwin, C.Y. and Clark, K.B. (2000), Design Rules, MIT Press, Cambridge, MA.

Balsmeier, P.W. and Voisin, W.J. (1996), “Supply chain management: a time-based strategy”,Industrial Management, Vol. 38 No. 5, pp. 24-7.

Bennett, R.C. and Cooper, R.G. (1981), “The misuse of marketing: an American tragedy”, BusinessHorizons, Vol. 24, pp. 51-61.

Bentler, P.M. (1990), “Comparative fit indexes in structural models”, Psychological Bulletin,Vol. 107 No. 2, pp. 238-46.

Best, R.J. (2003), Market-based Management: Strategies for Growing Customer Value andProfitability, 3rd ed., Prentice-Hall, Upper Saddle River, NJ.

Bollen, K.A. (1989), Structural Equations with Latent Variables, Wiley, New York, NY.

Bowersox, D.J., Closs, D.J. and Stank, T.P. (1999), 21st Century Logistics: Making Supply ChainIntegration A Reality, Council of Logistics Management, Oak Brook, IL.

Brown, S.L. and Eisenhardt, K.M. (1995), “Product development: past research, present findings,and future directions”, Academy of Management Review, Vol. 20 No. 2, pp. 343-78.

Brusoni, S. and Prencipe, A. (2001), “Unpacking the black box of modularity:technologies, products and organizations”, Industrial and Corporate Change, Vol. 10No. 1, pp. 179-205.

Bstieler, L. (2006), “Trust formation in collaborative new product development”, Journalof Product Innovation Management, Vol. 23, pp. 56-72.

Burbidge, J.L. (1982), “The simplication of material flow systems”, International Journalof Production Research, Vol. 20, pp. 339-59.

Callahan, J. and Lasry, E. (2004), “The importance of customer input in the development of verynew products”, R&D Management, Vol. 34 No. 2, pp. 107-20.

Campbell, J. and Sankaran, J. (2005), “An inductive framework for enhancing supply chainintegration”, International Journal of Production Research, Vol. 43 No. 16, pp. 3321-51.

Carter, J.R. and Ellram, L.M. (1994), “The impact of interorganizational alliances in improvingsupplier quality”, International Journal of Physical Distribution & Logistics Management,Vol. 24, pp. 15-24.


45

Carver, C.S. (1989), “How should multifaceted personality constructs be tested? Issues illustratedby self-monitoring, attributional style, and hardiness”, Journal of Personality and SocialPsychology, Vol. 56, pp. 577-85.

Chandra, C. and Kumar, S. (2000), “Supply chain management in theory and practice: a passingfad or a fundamental change”, Industrial Management & Data Systems, Vol. 100 No. 3,pp. 100-13.

Clark, K.B. (1989), “Project scope and project performance: the effect of parts strategy and supplierinvolvement on product development”, Management Science, Vol. 35 No. 10, pp. 1247-63.

Desai, P., Kekre, S., Radhakrishnan, S. and Srinivasan, K. (2001), “Product differentiation andcommonality in design: balancing revenue and cost drivers”, Management Science, Vol. 47No. 1, pp. 37-51.

Dogramaci, A. (1979), “Design of common components considering implications of inventory costand forecasting”, AIIE Trans., Vol. 11, pp. 129-35.

Du, X., Jiao, J. and Tseng, M.M. (2001), “Architecture of product family: fundamentals andmethodology”, Concurrent Engineering: Research and Applications, Vol. 9 No. 4, pp. 309-25.

Duray, R., Ward, P.T., Milligan, G.W. and Bery, W.L. (2000), “Approaches to mass customization:configurations and empirical validation”, Journal of Operations Management, Vol. 18,pp. 605-25.

Dyer, J.H. (2000), Collaborative Advantage, Oxford University Press, New York, NY.

Ellinger, A.F., Daugherty, P.J. and Keller, S.B. (2000), “The relationship between marketing/logisticsinterdepartmental integration and performance in US manufacturing firms: an empiricalstudy”, Journal of Business Logistics, Vol. 21 No. 1, pp. 1-22.

Enright, M., Scott, E. and Chang, K.M. (2005), Regional Powerhouse: The Greater Pearl RiverDelta and the Rise of China, Wiley, London.

Erixon, G. (1996), “Design for modularity”, in Huang, G.Q. (Ed.), Design for X ConcurrentEngineering Imperatives, Chapman & Hall, London.

Ernst, R. and Kamrad, B. (2000), “Evaluation of supply chain structures throughmodularization and postponement”, European Journal of Operational Research, Vol. 124,pp. 495-510.

Evans, D. (1970), “A note on modular design – a special case in non-linear programming”,Operations Reseach, Vol. 18, pp. 562-4.

Fawcett, S.E. and Magnan, G.M. (2002), “The rhetoric and reality of supply chain integration”,International Journal of Physical Distribution & Logistics Management, Vol. 32 No. 5,pp. 339-61.

Feitzinger, E. and Lee, H.L. (1997), “Mass customization at Hewlett-Packard: the powerof postponement”, Harvard Business Review, January-February, pp. 116-21.

FHKI (2003), Made in PRD – The Changing Face of Hong Kong Manufacturers, Federationof Hong Kong Industries, Kowloon, available at: www.fhki.org.hk

Fine, C.H. (1998), Clockspeed-Winning Industry Control in the Age of Temporary Advantage,Perseus Books, Reading, MA.

Fine, C.H., Golany, B. and Naseraldin, H. (2005), “Modeling tradeoffs in three-dimensionalconcurrent engineering: a goal programming approach”, Journal of OperationsManagement, Vol. 23 Nos 3/4, p. 389.

Fisher, M.L. (1997), “What is the right supply chain for your product? A simple frameworkcan help you figure out the answer”, Harvard Business Review, March-April, pp. 105-17.

IJOPM30,1

46

Fisher, M.L., Ramdas, K. and Ulrich, K. (1999), “Component sharing in the management ofproduct variety: a study of automotive braking systems”, Management Science, Vol. 45No. 3, pp. 297-315.

Fixson, S. (1999), “Economics of materials competition-effects of product architecture changesand the level of analysis”, International Motor Vehicle Program Annual Meeting,Cambridge, 7 October, pp. 1-11.

Fleming, L. and Sorenson, O. (2001), “Technology as a complex adaptive system: evidence frompatent data”, Research Policy, Vol. 30, pp. 1019-39.

Flynn, B.B., Sakakibara, S., Schroeder, R.G., Bates, K.A. and Flynn, E.J. (1990), “Empiricalresearch methods in operations management”, Journal of Operations Management, Vol. 9No. 2, pp. 250-85.

Frishammar, J. and Horte, S.A. (2005), “Managing external information in manufacturing firms:the impact on innovation performance”, Journal of Product Innovation Management,Vol. 22, pp. 251-66.

Frohlich, M.T. and Dixon, J.R. (2001), “A taxonomy of manufacturing strategies revisited”,Journal of Operations Management, Vol. 19, pp. 541-58.

Frohlich, M.T. and Westbrook, R. (2001), “Arcs of integration: an international study of supplychain strategies”, Journal of Operation Management, Vol. 19, pp. 185-200.

Galvin, P. and Morkel, A. (2001), “The effect of product modularity on industry structure: the caseof the world bicycle industry”, Industry and Innovation, Vol. 8 No. 1, pp. 31-47.

Garud, R. and Kumaraswamy, A. (1993), “Changing competitive dynamics in network industries:an exploration of Sun Microsystems’ open systems strategy”, Strategic ManagementJournal, Vol. 14 No. 5, pp. 365-9.

Garud, R., Kumaraswamy, A. and Langlois, R.N. (2003), Managing in the Modular Age,Blackwell, Malden, MA.

Gershenson, J.K., Prasad, G.J. and Zhang, Y. (2003), “Product modularity: definitions andbenefits”, Journal of Engineering and Design, Vol. 14 No. 3, pp. 295-313.

Gerwin, D. (2004), “Coordinating new product development in strategic alliances”, Academy ofManagement Review, Vol. 29 No. 2, pp. 241-57.

Goldberg, J. and Zhu, J. (1989), “Module design with substitute parts and multiple vendors”,European Journal of Operation Research, Vol. 41, pp. 335-46.

Griffin, A. (2002), “Product development cycle time for business-to-business products”, IndustrialMarketing Management, Vol. 31, pp. 291-304.

Griffin, A. and Page, A.L. (1993), “An interim report on measuring product development successand failure”, Journal of Product Innovation Management, Vol. 10, pp. 291-308.

Hamel, G. and Prahalad, C.K. (1994), Competing for the Future, Harvard Business School Press,Boston, MA.

Han, D., Kwon, I.G., Bae, M. and Sung, H. (2002), “Supply chain integration in developingcountries for foreign retailers in Korea: Wal-Mart experience”, Computers & IndustrialEngineering, Vol. 43, pp. 111-21.

Hargadon, A.B. and Eisenhardt, K.M. (2000), “Speed and quality in new product development”,in Cole, R.E. and Scot, W.R. (Eds), The Quality Movement Organization Theory, Sage,London, pp. 331-46.

Henderson, R.M. and Clark, K.B. (1990), “Architectural innovations: the reconfiguration ofexisting product technologies and the failure of established firms”, Administrative ScienceQuarterly, Vol. 35, pp. 9-30.


47

HKTDC (1998), Hong Kong’s Manufacturing Industries: Current Status and Future Prospects,Hong Kong Trade Development Council, Hong Kong.

HKTDC (1999), 1998/99 Techno-Economic and Market Research Study on Hong Kong’sElectronics Industry, Hong Kong Trade Development Council, Hong Kong.

HKTDC (2000), China’s WTO Accession: Challenges and Opportunities for Hong Kong’sElectronics Industry, Hong Kong Trade Development Council, Hong Kong.

HKTDC (2007), Hong Kong’s Export Outlook for 2007. Research Department, Hong Kong TradeDevelopment Council, Hong Kong.

Hollier, R.H. (1980), “The grouping concept in manufacturing”, International Journal ofOperations & Production Management, Vol. 1 No. 1, pp. 71-8.

Holtta-Otto, K. (2007), “Degree of modularity in engineering systems and products with technicaland business”, Concurrent Engineering, Vol. 15 No. 2, pp. 113-26.

Hsuan, J. (1999), “Impacts of supplier-buyer relationships on modularization in new productdevelopment”, European Journal of Purchasing & Supply Management, Vol. 5, pp. 197-209.

Huang, G.Q., Lau, J.S.K. and Mak, K.L. (2003), “The impacts of sharing production information onsupply chain dynamics: a review of the literature”, International Journal of ProductionResearch, Vol. 41 No. 7, pp. 1483-517.

Hyer, N.L. (1984), “The potential of group technology for US manufacturing”, Journal ofOperations Management, Vol. 4 No. 3, pp. 183-202.

Hyer, N.L. and Wemmerlov, U. (1984), “Group technology and productivity”, Harvard BusinessReview, July-August, pp. 140-9.

IMSS II Research Network (1996), The International Manufacturing Strategy Survey, IMSS IIResearch Network.

Ittner, C.D. and Larcker, D.F. (1997), “Product development cycle time and organizationalperformance”, Journal of Marketing Research, Vol. 34 No. 1, pp. 13-23.

Johnson, R.A. and Wichern, D.W. (1998), Applied Multivariate Statistical Analysis, Prentice-Hall,New York, NY.

Jose, A. and Tollenaere, M. (2005), “Modular and platform methods for product family design:literature analysis”, Journal of Intelligent Manufacturing, Vol. 16 No. 3, pp. 371-90.

Kahn, K.B. (1996), “Interdepartmental integration: a definition with implications for productdevelopment performance”, Journal of Product Innovation Management, Vol. 13, pp. 137-51.

Kahn, K.B. (2001), “Market orientation, interdepartmental integration, and product developmentperformance”, Journal of Product Innovation Management, Vol. 18, pp. 314-23.

Kahn, K.B. and McDonough, E.F. (1997), “An empirical study of the relationships amongco-location, integration, performance, and satisfaction”, Journal of Production InnovationManagement, Vol. 14, pp. 167-78.

Kahn, K.B. and Mentzer, J.T. (1996), “Logistics and interdepartmental integration”, InternationalJournal of Physical Distribution & Logistics Management, Vol. 26 No. 8, pp. 6-14.

Kamrani, A.K. and Salhieh, S.M. (2002), Product Design for Modularity, 2nd ed., KluwerAcademic, Dordrecht.

Karmarkar, U.S. and Kubat, P. (1987), “Modular product design and product support”, EuropeanJournal of Operational Research, Vol. 29, pp. 74-82.

Katz, R. (2003), The Human Side of Managing Technological Innovation, 2nd ed., OxfordUniversity Press, New York, NY.

Kim, K. and Chhajed, D. (2000), “Commonality in product design: cost saving, valuation changeand cannibalization”, European Journal of Operational Research, Vol. 125, pp. 602-21.

IJOPM30,1

48

Kim, S. and Narasimhan, R. (2001), “Information system utilization strategy for supply chainintegration efforts”, International Journal of Production Research, Vol. 40 No. 18,pp. 4585-609.

King, J.R. and Nakornchai, V. (1982), “Machine-component group formation in group technology:review and extension”, International Journal of Production Research, Vol. 20, pp. 117-33.

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

Kotabe, M., Martin, X. and Domoto, H. (2003), “Gaining from vertical partnerships: knowledgetransfer, relationship duration, and supplier performance improvement in the US andJapanese automotive industries”, Strategic Management Journal, Vol. 24, pp. 293-316.

Kotha, S. (1995), “Mass customization: implementing the emerging paradigm for competitiveadvantage”, Strategic Management Journal, Vol. 16, pp. 21-42.

Krishnan, V. and Ulrich, K.T. (2001), “Product development decisions: a review of the literature”,Management Science, Vol. 47 No. 1, pp. 1-21.

Kumar, A., Gattoufi, S. and Reisman, A. (2007), “Mass customization research: trends, directions,diffusion intensity, and taxonomic frameworks”, International Journal of FlexibleManufacturing Systems, Vol. 19 No. 4, pp. 637-65.

Kumar, N., Stern, L.W. and Anderson, J.C. (1993), “Conducting interorganizational research usingkey informants”, Academy of Management Journal, Vol. 36 No. 6, pp. 1633-51.

Lambert, D.M. and Cooper, M.C. (2000), “Issue in supply chain management”, IndustrialMarketing Management, Vol. 29, pp. 65-83.

Lau, A.K.W. and Yam, R.C.M. (2004), “Product modularity in supply chain coordination:experiences on Pearl River Delta”, paper presented at PICMET’ 04 Symposium, July.

Lawrence, A. (1997), “Customer power forces supply chain integration”, Works Management,April, pp. 43-7.

Ledwith, A. (2000), “Management of new product development in small electronics firms”,Journal of European Industrial Training, Vol. 24 Nos 2-4, pp. 137-48.

Lee, H.L. (2000), “Creating value through supply chain integration”, Supply Chain ManagementReview, September-October, pp. 30-6.

Lee, H.L. and Whang, S. (2001), “E-business and supply chain integration”, Stanford GlobalSupply Chain Management Forum, November, pp. 1-20.

Leseter, T.M. and Ramdas, K. (2002), “Product types and supplier roles in product development:an exploratory analysis”, IEEE Transactions on Engineering Management, Vol. 29 No. 2,pp. 107-18.

Lin, B.W. (2003), “Cooperating for supply chain effectiveness: manufacturing strategy forChinese OEMs”, International Journal of Manufacturing Technology and Management,Vol. 5 No. 3, pp. 232-45.

McAuley, J. (1972), “Machine grouping for efficient production”, Production Engineer, Vol. 51,pp. 53-7.

McClelland, J.L. and Rumelhart, D.E. (1988), “Explorations in parallel distributed processing”,A Handbook of Models, Programs, and Exercises, MIT Press, London, pp. 75-7.

Macdonald, A. and Beavis, D. (2001), “Seize the moment supply chain integration”,Manufacturing Engineer, Vol. 80 No. 4, pp. 175-8.

Malerba, F. and Orsenigo, L. (2000), “Knowledge, innovation activities and industrial evolution”,Industrial and Corporate Change, Vol. 9 No. 2, pp. 289-314.


49

Mars (2008), Custom Printed My M&M’s, Mars, Hackettstown, NJ, available at: www.mymms.com/customprint/ (accessed 22 March 2008).

Marsh, H.W. and Hocevar, D. (1985), “Application of confirmatory factor analysis to the study ofself-concept: first- and higher-order factor models and their invariance across groups”,Psychological Bulletin, Vol. 97, pp. 145-52.

Mason-Jones, R. and Towill, D.R. (1997), “Information enrichment: designing the supply chain forcompetitive advantage”, Supply Chain Management, Vol. 2 No. 4, pp. 137-48.

Mentzer, J.T. (2000), Supply Chain Management, Sage, Thousand Oaks, CA.

Mentzer, J.T. (2004), Fundamentals of Supply Chain Management, Sage, Thousand Oaks, CA.

Meyer, M. and Roberts, E. (1986), “New product strategy in small technology-firms: a pilotstudy”, Management Science, Vol. 32, pp. 806-21.

Mikkola, J.H. (2003), “Modularity, component outsourcing, and inter-firm learning”, R&DManagement, Vol. 33, pp. 439-52.

Mikkola, J.H. and Gassmann, O. (2003), “Managing modularity of product architectures: towardan integrated theory”, IEEE Transactions on Engineering Management, Vol. 50 No. 2,pp. 204-18.

Miles, J. (2000), “Statistical software for microcomputer AMOS 4.0”, British Journal ofMathematical & Statistical Psychology, Vol. 53, pp. 141-4.

Morash, E.A. and Lynch, D.F. (2002), “Public policy and global supply chain capabilities andperformance: a resource-based view”, Journal of International Marketing, Vol. 10 No. 1,pp. 25-51.

Narasimhan, R. and Kim, S.W. (2002), “Effect of supply chain integration on the relationshipbetween diversification and performance: evidence from Japanese and Korean firms”,Journal of Operations Management, Vol. 20, pp. 303-23.

Nobelius, O. and Sundgren, N. (2002), “Managerial issues in parts sharing among productdevelopment projects: a case study”, Journal of Engineering & Technology Management,Vol. 19, pp. 59-73.

Nobeoka, K. and Cusumano, M. (1997), “Multi-project strategy and sales growth: the benefitsof rapid design transfer in new product development”, Strategic Management Journal,Vol. 18 No. 3, pp. 169-86.

Novak, S. and Eppinger, S.D. (2001), “Sourcing by design: product complexity and the supplychain”, Management Science, Vol. 47 No. 1, pp. 189-204.

Orton, J.D. and Weick, K.E. (1990), “Loosely coupled systems: a reconceptualization”, Academy ofManagement Review, Vol. 15 No. 2, pp. 203-23.

Pagell, M. and Krause, D.R. (2004), “Re-exploring the relationship between flexibility and theexternal environment”, Journal of Operations Management, Vol. 21, pp. 629-49.

Parker, G.G. and Anderson, E.G. Jr (2002), “Supply chain integration: putting humpty-dumptyback together again”, in Boone, T. and Ganeshan, R. (Eds), New Directions in Supply ChinaManagement: Technology, Strategy, and Implementation, AMACOM, New York, NY,pp. 352-76.

Philips, L.W. (1981), “The reliability and validity of key informant reports: a methodological noteon organizational analysis in marketing”, Journal of Marketing Research, Vol. 18,pp. 395-415.

Pine, J.B. II (1993), Mass Customization: The New Frontier in Business Competition, HarvardBusiness School Press, Cambridge, MA.

IJOPM30,1

50

Ragatz, G.L., Handfield, R.B. and Petersen, K.J. (2002), “Benefits associated with supplierintegration in new product development under conditions of technology uncertainty”,Journal of Business Research, Vol. 55, pp. 389-400.

Ragatz, G.L., Handfield, R.B. and Scannell, T.V. (1997), “Success factors for integrating suppliersinto new product development”, Journal of Product Innovation Management, Vol. 14,pp. 190-202.

Rajagopalan, R. and Batra, J.L. (1975), “Design of cellular production systems a graph-theoreticapproach”, International Journal of Production Research, Vol. 13 No. 6, pp. 567-679.

Ramdus, K. (2003), “Managing product variety: an integrative review and research directions”,Production and Operations Management, Vol. 12 No. 1, pp. 79-101.

Robertson, D. and Ulrich, K. (1998), “Planning for product platforms”, Sloan ManagementReview, Summer, pp. 19-31.

Rosenzweig, E.D., Roth, A.V. and Dean, J.W. Jr (2003), “The influence of an integration strategyon competitive capabilities and business performance: an exploratory study of consumerproducts manufacturers”, Journal of Operations Management, Vol. 21 No. 4, pp. 437-56.

Rungtusanatham, M., Salvador, F., Forza, C. and Choi, T.Y. (2003), “Supply-chain linkages andoperational performance: a resource-based view perspective”, International Journal ofOperations & Production Management, Vol. 23 No. 9, pp. 1084-100.

Rutenberg, D. and Shaftel, T. (1971), “Product design: subassemblies for multiple markets”,Management Science, Vol. 18, pp. 220-31.

Sabel, C.F. and Zeitlin, J. (2004), “Neither modularity nor relational contracting: inter-firmcollaboration in the new economy”, Enterprise & Society, Vol. 5 No. 3, pp. 388-403.

Sako, M. (2002), “Modularity and outsourcing: the nature of co-evolution of product architectureand organization architecture in the global automotive industry”, in Prencipe, A.,Davies, A. and Hobday, M. (Eds), The Business of Systems Integration, Oxford UniversityPress, Oxford, pp. 1-40.

Sako, M. and Murray, F. (1999), “Modular strategies in cars and computers”, International MotorVehicle Program, White Paper, June, pp. 1-4.

Sako, M. and Warburton, M. (1999), “MIT International Motor Vehicle ProgrammeModularization and Outsourcing Project Preliminary Report of European ResearchTeam”, IMVP Annual Forum, MIT Press, Boston, MA, 6-7 October, pp. 1-60.

Salvador, F. (2007), “Toward a product system modularity construct: literature review andreconceptualization”, IEEE Transactions on Engineering Management, Vol. 52 No. 2,pp. 219-39.

Salvador, F., Forza, C. and Rungtusanatham, M. (2002), “Modularity, product variety, productionvolume, and component sourcing: theorizing beyond generic prescriptions”, Journal ofOperations Management, Vol. 20, pp. 549-75.

Sanchez, R. (1995), “Strategic flexibility in product competition”, Strategic Management Journal,Vol. 6, pp. 135-59.

Sanchez, R. (1996), “Strategic product creation: managing new interactions of technologies,markets, and organizations”, European Management Journal, Vol. 14 No. 2, pp. 121-38.

Sanchez, R. (1999), “Modular architectures in the marketing process”, Journal of Marketing,Vol. 63, pp. 92-111.

Sanchez, R. and Mahoney, J.T. (1996), “Modularity, flexibility, and knowledge management inproduct and organization design”, Strategic Management Journal, Vol. 17, pp. 63-76.

Schilling, M.A. (2000), “Toward a general modular systems theory and its application tointerfirm product modularity”, Academy of Management Review, Vol. 25 No. 2, pp. 312-34.


51

Schumacker, R.E. and Lomax, R.G. (1996), A Beginner’s Guide to Structural Equation Modelling,Lawrence Erlbaum Associates, Mahwah, NJ.

Shaftel, T.L. and Thompson, G.L. (1977), “A simplex-like algorithm for the continuous modulardesign problem”, Operations Research, Vol. 25 No. 5, pp. 788-807.

Sherman, J.D., Souder, W.E. and Jenssen, S.A. (2000), “Differential effects of the primary forms ofcross functional integration on product development cycle time”, Journal of ProductInnovation Management, Vol. 17, pp. 257-67.

Sheu, C. and Wacker, J.G. (1997), “The effects of purchased parts commonality on manufacturinglead time”, International Journal of Operations & Production Management, Vol. 17 No. 8,pp. 725-45.

Simon, H.A. (1962), “The architecture of complexity: hierarchic systems”, Proceedings of theAmerican Philosophical Society, Vol. 78, December, pp. 467-82.

Singh, P.J., Smith, A. and Sohal, A.S. (2005), “Strategic supply chain management issues in theautomotive industry: an Australian perspective”, International Journal of ProductionResearch, Vol. 43 No. 16, pp. 3375-99.

Song, X.M. and Parry, M.E. (1999), “Challenges of managing the development of breakthroughproducts in Japan”, Journal of Operations Management, Vol. 17, pp. 665-88.

Song, X.M., Montoya-Weiss, M.M. and Schmidt, J.B. (1997), “Antecedents and consequences ofcross-functional cooperation: a comparison of R&D, manufacturing, and marketingperspectives”, Journal of Product Innovation Management, Vol. 14 No. 1, pp. 35-47.

SQW (Asia), Rapra International, Hong Kong Plastics Technology Centre (2001),Techno-economic and Market Research Study on Hong Kong’s Plastics Industry,1999/2000, Hong Kong Trade and Industry Department, Hong Kong.

Stank, T.P., Daugherty, P.J. and Ellinger, A.E. (1999), “Marketing/logistics integration and firmperformance”, The International Journal of Logistics Management, Vol. 10 No. 1, pp. 11-24.

Starr, M.K. (1965), “Modular production – a new concept”, Harvard Business Review,November-December, pp. 131-42.

Stevens, G.C. (1989), “Integrating supply chain”, International Journal of Physical distribution &Logistics Management, Vol. 19 No. 8.

Stock, J.R. and Lambert, D.M. (2001), Strategic Logistics Management, 4th ed., McGraw-Hill,New York, NY.

Swafford, P.M., Ghosh, S. and Murthy, N. (2006), “The antecedents of supply chain agility of afirm: scale development and model testing”, Journal of Operations Management, Vol. 24No. 2, pp. 170-88.

Takeishi, A. (2001), “Bridging inter- and intra-firm boundaries: management of supplierinvolvement in automobile product development”, Strategic Management Journal, Vol. 22No. 5, pp. 41-53.

Tidd, J., Bessant, J. and Pavitt, K. (2001), Managing Innovation, 2nd ed., Wiley, New York, NY.

Towill, D.R., Childerhouse, P. and Disney, S.M. (2002), “Integrating the automotive supply chain:where are we now”, International Journal of Physical Distribution & Logistics Management,Vol. 32 No. 2, pp. 79-95.

Ulrich, K.T. (1995), “The role of product architecture in the manufacturing firm”, Research Policy,Vol. 24, pp. 419-40.

Ulrich, K.T. and Ellison, D.J. (1999), “Holistic customer requirements and the design-selectdecision”, Management Science, Vol. 45 No. 5, pp. 641-58.

IJOPM30,1

52

Ulrich, K.T. and Eppinger, S.D. (2000), Product Design and Development, 2nd ed., McGraw-Hill,Boston, MA.

Ulrich, K.T. and Tung, K. (1991), “Fundamentals of product modularity”, Proceedings of the 1991ASME Winter Annual Meeting Symposium on Issues in Design ManufacturingIntegration, Atlanta, GA, pp. 1-14.

Vaart, T.V.D. and Donk, D.P.V. (2003), “Buyer focus: evaluation of a new concept for supplychain integration”, International Journal of Production Economics, Vol. 92 No. 1, pp. 21-30.

Vickery, S.K., Jayaram, J., Droge, C. and Calantone, R. (2003), “The effects of an integrativesupply chain strategy on customer service and financial performance: an analysis of directversus indirect relationships”, Journal of Operations Management, Vol. 21, pp. 523-39.

von Hippel, E. (1988), The Source of Innovation, Oxford University Press, New York, NY.

Ward, P.T. and Duray, R. (2000), “Manufacturing strategy in context: environment, competitivestrategy and manufacturing strategy”, Journal of Operation Management, Vol. 18,pp. 123-38.

Ward, P.T., Bickford, D.J. and Leong, G.K. (1996), “Configurations of manufacturing strategy,business strategy, environment and structure”, Journal of Management, Vol. 22 No. 4,pp. 597-626.

Weele, A.J.V. (2002), Purchasing and Supply Chain Management Analysis, Planning and Practice,3rd ed., Thomson Learning, London.

Weick, K.E. (1976), “Educational organizations as loosely coupled systems”, AdministrativeScience Quarterly, Vol. 21, March, pp. 1-19.

Worren, N., Moore, K. and Cardona, P. (2002), “Modularity, strategic flexibility, and firmperformance: a study of the home appliance industry”, Strategic Management Journal,Vol. 23, pp. 1123-40.

Zirger, B.J. and Hartley, J.L. (1994), “A conceptual model of product development cycle time”,Journal of Engineering & Technology Management, Vol. 11, pp. 229-51.

(The Appendix follows overleaf.)

Corresponding authorsAntonio K.W. Lau and Richard C.M. Yam can be contacted at: [email protected] [email protected], respectively.


53

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

Appendix

Mea

sure

men

tit

emsa

Lit

erat

ure

sup

por

tS

tan

dar

diz

edfa

ctor

load

ing

s*

Inform

ationsharing

Towhatextentdoesyourorganization

integrate/coordinate

activitieswithyoursuppliers?(None¼

1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),F

roh

lich

and

Dix

on(2

001)

,Fro

hli

chan

dW

estb

rook

(200

1)an

dIM

SS

IIR

esea

rch

Net

wor

k(1

996)

Su

pp

lier

inte

gra

tion

-in

form

atio

nsh

arin

g(S

I-IS

)a¼

0.84

92S

har

ep

rod

uct

ion

pla

ns

0.66

66S

har

ein

ven

tory

mix

/lev

elin

form

atio

n0.

5157

Sh

are

tech

nol

ogic

alin

form

atio

n0.

8977

Sh

are

mar

ket

ing

info

rmat

ion

0.91

16Towhatextentdoesyourorganization


activitieswithyourcustom

ers?(None¼

1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),F

roh

lich

and

Dix

on(2

001)

,Fro

hli

chan

dW

estb

rook

(200

1)an

dIM

SS

IIR

esea

rch

Net

wor

k(1

996)

Cu

stom

erin

teg

rati

on-i

nfo

rmat

ion

shar

ing

(CI-

IS)a¼

0.87

59S

har

ep

rod

uct

ion

pla

ns

0.69

34S

har

ein

ven

tory

mix

/lev

elin

form

atio

n0.

6923

Sh

are

tech

nol

ogic

alin

form

atio

n0.

8447

Sh

are

mar

ket

ing

info

rmat

ion

0.86

83Productco-development




1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),F

roh

lich

and

Dix

on(2

001)

,Fro

hli

chan

dW

estb

rook

(200

1)an

dIM

SS

IIR

esea

rch

Net

wor

k(1

996)

Su

pp

lier

inte

gra

tion

-pro

du

ctco

-dev

elop

men

t(S

I-P

C)

a¼

0.88

82Jo

int

pro

du

ctd

esig

n0.

7413

Join

tp

roce

ssen

gin

eeri

ng

0.90

05Jo

int

pro

du

ctio

nop

erat

ion

s0.

9054


engage

inintegrating/

coordinatingactivitiesacrossdepartmentsor

business

functions?(None¼

1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),

Ell

ing

eret

al.

(200

0),

IMS

SII

Res

earc

hN

etw

ork

(199

6)an

dK

ahn

and

Men

tzer

(199

6)

Inte

rnal

inte

gra

tion

-in

teg

rate

dp

rod

uct

dev

elop

men

t(I

I-IP

D)

a¼

0.91

83C

lose

coor

din

atio

nin

pro

du

ctd

esig

nan

dd

evel

opm

ent

0.82

63In

form

atio

nin

teg

rati

onin

pro

du

ctio

np

roce

ss0.

8964

Inte

ract

ive

syst

emb

etw

een

pro

du

ctio

nan

dsa

les

0.86

75C

lose

coor

din

atio

nin

pro

du

ctla

un

ch0.

8900

(continued

)

Table AI.Results of confirmatoryfactor analysis

IJOPM30,1

54

Mea

sure

men

tit

emsa

Lit

erat

ure

sup

por

tS

tan

dar

diz

edfa

ctor

load

ing

s*




ers?(None¼

1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),F

roh

lich

and

Dix

on(2

001)

,Fro

hli

chan

dW

estb

rook

(200

1)an

dIM

SS

IIR

esea

rch

Net

wor

k(1

996)

Cu

stom

erin

teg

rati

on-p

rod

uct

co-d

evel

opm

ent

(CI-

PC

)a¼

0.89

14Jo

int

pro

du

ctd

esig

n0.

7667

Join

tp

roce

ssen

gin

eeri

ng

0.93

43Jo

int

pro

du

ctio

nop

erat

ion

s0.

8807

Organizationalcoordination




1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),F

roh

lich

and

Dix

on(2

001)

,Fro

hli

chan

dW

estb

rook

(200

1)an

dIM

SS

IIR

esea

rch

Net

wor

k(1

996)

Su

pp

lier

inte

gra

tion

-org

aniz

atio

nal

coor

din

atio

n(S

I-O

C)

a¼

0.86

36D

ecid

ete

chn

ical

faci

lity

loca

tion

0.82

96C

omm

onu

seof

log

isti

cal

equ

ipm

ent/

con

tain

ers

0.64

57M

utu

alb

usi

nes

ssy

stem

sd

esig

n0.

8642

Acc

ess

toco

mp

any

pla

nn

ing

syst

ems

0.80

49Towhatextentdoesyourorganization

engage

inintegrating/

coordinatingactivitiesacrossdepartmentsor

business

functions?(None¼

1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),

Ell

ing

eret

al.

(200

0),

Kah

nan

dM

entz

er(1

996)

and

IMS

SII

Res

earc

hN

etw

ork

(199

6)

Inte

rnal

inte

gra

tion

-fu

nct

ion

alco

ord

inat

ion

(II-

FC

)a¼

0.91

77P

erio

dic

inte

rdep

artm

enta

lm

eeti

ng

s0.

7778

Dat

ain

teg

rati

onam

ong

inte

rnal

fun

ctio

ns

0.78

23F

orm

alte

amw

ork

0.89

37C

lose

coor

din

atin

gac

tiv

itie

s0.

9022




ers?(None¼

1,extensive

¼5)

Nar

asim

han

and

Kim

(200

2),F

roh

lich

and

Dix

on(2

001)

,Fro

hli

chan

dW

estb

rook

(200

1)an

dIM

SS

IIR

esea

rch

Net

wor

k(1

996)

Cu

stom

erin

teg

rati

on-o

rgan

izat

ion

alco

ord

inat

ion

(CI-

OC

)a¼

0.87

43D

ecid

ete

chn

ical

faci

lity

loca

tion

0.79

49C

omm

onu

seof

log

isti

cal

equ

ipm

ent/

con

tain

ers

0.66

37M

utu

alb

usi

nes

ssy

stem

sd

esig

n0.

8877

Acc

ess

toco

mp

any

pla

nn

ing

syst

ems

0.84

99

(continued

)

Table AI.


55

Mea

sure

men

tit

emsa

Lit

erat

ure

sup

por

tS

tan

dar

diz

edfa

ctor

load

ing

s*

Productmodularity

How

would

youdescribeyourmain

product(s)?(None¼

1,

extensive

¼5)

Lin

(200

3),

Wor

renet

al.

(200

2)an

dD

ura

yet

al.

(200

0)

Pro

du

ctm

odu

lari

ty(P

M)a¼

0.87

22P

rod

uct

can

be

dec

omp

osed

into

sep

arat

em

odu

les

0.62

98W

eca

nm

ake

chan

ges

inth

ek

eyco

mp

onen

tw

ith

out

red

esig

nin

got

her

s0.

7023

Pro

du

ctco

mp

onen

tsca

nb

ere

use

din

var

iou

sp

rod

uct

s0.

9075

Pro

du

cth

ash

igh

deg

ree

ofco

mp

onen

tca

rry

-ov

er0.

9236

Pro

du

ct’s

com

pon

ents

are

stan

dar

diz

ed0.

5749

Productperformance

Towhatextentdoyoudescribethemainproduct

performances?(None¼

1,extensive

¼5)

War

dan

dD

ura

y(2

000)

,E

llin

ger

etal.

(200

0)an

dS

ong

and

Par

ry(1

999)

Pro

du

ctp

erfo

rman

cea¼

0.87

60T

he

pro

du

cth

asac

hie

ved

our

sale

sg

oal

0.84

16T

he

pro

du

cth

asac

hie

ved

our

pro

fit

goa

l0.

9334

Th

ep

rod

uct

has

had

gre

atp

rofi

tab

ilit

y0.

7972

Cu

stom

ers

are

ver

ysa

tisfi

edw

ith

the

pro

du

ctp

erfo

rman

ce0.

6618

x2

val

ue

Deg

ree

offr

eed

omx

2v

alu

e/d

egre

eof

free

dom

1.67

69C

FI

0.98

38IF

I0.

9839

Notes:

* All

stan

dar

diz

edre

gre

ssio

nw

eig

hts

are

sig

nifi

can

tat

p-v

alu

e,

0.01

;aon

lyit

ems

that

are

reli

able

and

con

firm

edb

yfa

ctor

anal

ysi

sar

esh

own

;n¼

251

Table AI.

IJOPM30,1

56

supply chain

Documents

product development

product reliability

product types

better product performance

modular product design

costly product redesign

advantage of sci

operationalization of