00207540600871269

This article was downloaded by: [Indian Institute of Technology Roorkee]On: 04 September 2014, At: 06:09Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of ProductionResearchPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tprs20

The impact of operations capability onfirm performanceK. C. Tan a , V. R. Kannan b & R. Narasimhan ca Department of Management , University of Nevada Las Vegas ,College of Business, Las Vegas, NV 89154-6009, USAb Department of Business Administration , Utah State University ,Logan, UT 84322-3510, USAc Department of Marketing and Logistics , The Eli Broad GraduateSchool of Management , Michigan State University , East Lansing,MI 48824-1122, USAPublished online: 26 Sep 2007.

To cite this article: K. C. Tan , V. R. Kannan & R. Narasimhan (2007) The impact of operationscapability on firm performance, International Journal of Production Research, 45:21, 5135-5156

To link to this article: http://dx.doi.org/10.1080/00207540600871269

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (theContent) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

International Journal of Production Research,Vol. 45, No. 21, 1 November 2007, 51355156

The impact of operations capability on firm performance

K. C. TAN*y, V. R. KANNANz and R. NARASIMHANx

yDepartment of Management, University of Nevada Las Vegas,College of Business, Las Vegas, NV 89154-6009, USA

zDepartment of Business Administration, Utah State University,Logan, UT 84322-3510, USA

xDepartment of Marketing and Logistics, The Eli BroadGraduate School of Management, Michigan State University,

East Lansing, MI 48824-1122, USA

(Revision received June 2006)

We propose that a firms operations capability is manifested through its newproduct design and development, just-in-time, and quality-management efforts.Moreover, we propose that operations capability, so defined, directly andpositively impacts firm performance. Survey data were used to independentlycalibrate and validate a structural equation model linking operations capability,a second-order construct, to firm performance. Results provide support for themodel and demonstrate a positive relationship between operations capability andfirm performance.

Keywords: Just-in-Time; Quality management; New product design anddevelopment; Operations capability; Structural equation modelling; Invariantanalysis

1. Introduction

The notion that operations capability plays a vital role in achieving competitivesuccess is widely accepted (e.g. Hammer and Champy 1993, Tan et al. 2004).The manufacturing strategy literature, for example, has emphasized the importanceof developing and nurturing manufacturing capabilities as a prelude to achievinglong-term, sustainable competitive success (e.g. Roth and Miller 1992, Hayes andPisano 1996). This involves not only strengthening internal capabilities but workingwith suppliers and customers to take advantage of their technologies andcapabilities, for example, by involving strategic suppliers early in new productdesign and development efforts (Ragatz et al. 1997, Tan 2001).

Interest in operations capability and its influence on competitive advantageand performance have generated a large research stream within the operationsstrategy literature (e.g. Flynn and Flynn 2004, Tan et al. 2004). The impetus forthis research lies in the resource-based view of the firm, which has emerged asa theoretical framework for analysing the sources and sustainability of

*Corresponding author. Email: [email protected]

International Journal of Production Research

ISSN 00207543 print/ISSN 1366588X online 2007 Taylor & Francishttp://www.tandf.co.uk/journals

DOI: 10.1080/00207540600871269

Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

competitive success (Barney 1991, Grant 1991). It argues that competitive

advantage is created by firms acquiring and utilizing resources in an inimitable

manner due to the specialization of assets and implicit knowledge and skills

(Barney 1991). This enables the firm to translate process knowledge into unique

operations capabilities that create superior competitive advantage. Prior research

has demonstrated that firms in the same market segment that use similar

functional strategies can exhibit substantial differences in performance levels

(Cool and Schendel 1988). These differences can be attributed to how firms

manage the development of their distinctive competencies (Lawless et al. 1989).

For example, when Toyota emerged as the most cost-efficient manufacturer in the

automotive industry, many competitors attempted, but failed, to replicate

Toyotas success. Similarly, in the retail and air transportation sectors, with the

exception of a few companies such as Tesco and Ryanair in the UK, few

companies have been able to replicate Wal-Marts and Southwest Airlines success

in the US. The resources that firms such as Toyota and Wal-Mart have utilized

are by no means unique. However, how they have been leveraged is the key to

the successes they have yielded.The literature on operations capability has focused on identifying the

dimensions of organizational success that can be attributed to manufacturing,

how capabilities evolve over time, and how they impact a firms performance.

Manufacturing strategies based on cost, delivery, quality, and flexibility have for

many years been touted as supporting corporate objectives (e.g. Leong et al. 1990).

Different schools of thought have also emerged regarding the number of

capabilities a firm can successfully emphasize. Some suggest that firms must

choose between capabilities in making strategy and resource-allocation decisions

(Skinner 1969), while others advocate that firms must necessarily develop multiple

capabilities to respond to increasing competition (e.g. Nakane 1986, Ferdows and

De Meyer 1990). The content of operations capability, however, has not received

much attention from prior studies. Part of the reason is the multi-faceted nature of

operations capability. The current study builds on existing literature by examining

how operations capabilities manifest themselves in terms of initiatives firms use to

achieve high levels of competitiveness. Much of the existing literature focuses on

capability as an output, focusing on dimensions of manufacturing performance or

metrics at which firms excel. Little discussion exists, however, of how specifically

this is accomplished. We posit that operations capability is the result of a strategic

commitment to new product development, quality-improvement and waste-

elimination strategies such as just-in-time (JIT). Excellence on dimensions of

performance such as cost, quality, delivery, and flexibility is the result of systems

that focus organizational resources on product and process improvements. In other

words, we examine capability from an input perspective. For example, JIT can be

characterized as a companys focus on reducing lot sizes and setup times, etc.,

as opposed to the traditional characterization of inventory reduction. This

perspective is useful to firms as they invest in manufacturing practices to develop

high levels of operations capability. Specifically, we explore the multi-dimensional

and higher-order nature of operations capability. A secondary objective of this

study is to validate our position by empirically examining the relationship between

operations capability and firm performance.

5136 K. C. Tan et al.

Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

2. Literature review

2.1 Nature of operations capability

While there appears to be a broad consensus that operations capability refers to

a firms leveraging of its manufacturing function to support organizational success,

differences exist in how it is defined. It has for example been characterized as tasks

that a firm can do well (Skinner 1969), internally developed activities that a firm can

do better than its competitors (Hayes and Pisano 1996), stocks of strategic assets

accumulated through time and which cannot be easily imitated, acquired, or

substituted (Dierickx and Cool 1989), or an ability to compete on certain dimensions

(Safizadeh et al. 2000). Operations capability is sometimes used synonymously

with competitive priority. However, competitive priorities reflect manufacturing

dimensions on which a firm needs to excel at to be competitive (Roth and van de

Velde 1991), to develop and enhance a plants position in the marketplace

(Boyer and Lewis 2002), or that management considers to be important (Safizadeh

et al. 2000), rather than those on which it does excel. Lack of discrimination also

exists between operations capability and manufacturing outcome, the former

referring to actionable competencies and the latter to results (Corbett and

van Wassenhove 1993), and between operations capability and manufacturing

competence, the degree to which manufacturing excellence supports a firms broader

strategic goals (Cleveland et al. 1989). Relationships between these terms are

summarized in figure 1. Competitive priorities define the dimensions of operations

capability necessary for success. These in turn drive manufacturing outcomes,

providing support for corporate objectives or manufacturing competence.

Operations Capability

Competitive Priority

a dimension of operations strategythat management considers the firm must

excel at to be successful

an actionable competency that disengagedefines the firms leveraging of itsmanufacturing function to support

organizational success

the degree to which exceptionalmanufacturing capability supports

broader strategic goals

A well-defined set of competitive priorities allows thefirm to translate process knowledge into unique

operations capability

Unique operations capabilities that areexecuted well create superior competitiveadvantage that lead to positive outcomes

Positive manufacturing outcomessupport corporate objectives and enhance

competitive advantage

the output or result achieved due tounique operations capability

Manufacturing Outcome

Manufacturing Competence

Figure 1. Relationship between characterizations of operations capability.

Impact of operations capability on firm performance 5137

Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

Two elements can be observed within the operations capability literature:

(1) identification of the dimensions of capability, and relationships between the

dimensions and performance, and (2) identification of how capabilities evolve.

Several dimensions of capability have been identified as crucial to an organizations

long-term success. In particular, there is theoretical support for the importance

of cost, quality, flexibility, and delivery (e.g. Schmenner and Swink 1998, Ward

et al. 1998). Others have also suggested that innovation (e.g. Hall and Nakane 1990,

Leong et al. 1990) and organizational culture (Hall and Nakane 1990) may be

important elements of manufacturing strategy. Swink and Hegarty (1998)

distinguished between capabilities pertinent to maintaining steady state versus

those pertinent to growth in the context of product differentiation. They suggested

that in addition to innovation, the ability to improve efficiency and productivity,

and to expand to encompass new products and/or technologies, are core growth

capabilities. In contrast, core steady-state capabilities are those related to leveraging

managements insights into process capability and performance, regulating

and directing processes, and switching between manufacturing states at low or

no cost. The first and third of these have parallels with quality and flexibility,

respectively.Several studies have attempted to empirically validate relationships among

individual capabilities and with performance (e.g. White 1996). Moreover, several

shed light on the ongoing debate as to whether capabilities must be traded off or

whether they can coexist. Early work suggested it was unreasonable for a plant to

simultaneously excel on multiple dimensions, so trade-offs must be made in

developing capabilities (e.g. Skinner 1969, Hayes and Wheelwright 1984, Garvin

1993). However, this view has also been interpreted as laying the basis for prioritizing

between capabilities as opposed to saying that they cannot be developed in tandem

(Hayes and Wheelwright 1984). While limited empirical support for the trade-off

model exists (Ward et al. 1998, Boyer and Lewis 2002, Safizadeh et al. 2000), much of

the recent work questions the notion that firms cannot simultaneously excel at

multiple capabilities (e.g. Corbett and van Wassenhove 1993, Garvin 1993, Hayes

and Pisano 1996). The cumulative model (Nakane 1986) suggests that multiple

capabilities can coexist and should be developed in a particular sequence, the

development of quality capability being followed by development of capabilities in

dependability, cost, and flexibility. The sand cone theory (Ferdows and De Meyer

1990) suggests that this sequential development of multiple capabilities is the key to

lasting improvements in performance. Several studies provide empirical support for

the cumulative model, demonstrating that high- and low-performing firms differ in

the number of capabilities they possess (e.g. Roth and Miller 1992, Noble 1995,

Flynn and Flynn 2004). However, while acknowledging the co-existence of multiple

capabilities, a recent study found no evidence that differences in performance can

be attributed to the sequence in which capabilities were developed (Corbett and

Claridge 2002). While the two paradigms may appear to be in conflict, a third

paradigm, the integrative model, suggests they may be complementary (Schmenner

and Swink 1998). Specifically, it suggests that when a plant is close to the limits of its

asset structure, focus is required in making choices regarding the ongoing

development of capabilities. In contrast, when a plant is not limited by existing

assets, potential exists to develop multiple capabilities.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

2.2 Content of operations capability

Several actionable dimensions of operations capability consistent with the definitionsof Skinner (1969) and Hayes and Pisano (1996) have been identified. Hayes andPisano (1996) advocated taking advantage of improvement programmes such as JITor total quality management (TQM). A similar prescription was suggested by Rothand Miller (1992), who identified five areas relevant to the development of capability;total factor resource improvements, quality-management programmes, advancedmanufacturing process technology, information systems, and restructuring. Theapplication of quality-improvement methods, advanced process technology,integrated information systems, as well as enhanced product development andproduction control processes, were also identified as important elements in the plansof Japanese, European, and North American firms (De Meyer et al. 1989).Narasimhan and Jayaram (1998) identified supply management, process improve-ment, and information systems as enablers of capability development. The latterstudy apart, however, little empirical evidence exists to validate the underlyingcontent of operations capability, or its relationship with performance. Tan et al.(2004) proposed a three-factor model of operations capability. Their results providedsupport for the proposition that underlying a firms capability are efforts to improvequality using traditional total quality management methods, processes consistentwith the JIT philosophy, and product development.

It is evident that several initiatives or programmes, either under the control ofmanufacturing or over which manufacturing has significant influence, drive theattainment of manufacturing excellence. These include new product-developmentprocesses, improvements in processes aimed at quality and productivity, and processcontrol. Consistent with capability being task- or activity-based (Skinner 1969,Hayes and Pisano 1996) it is these that we refer to as operations capabilities whendescribing operations capability from an input perspective. Operations capabilitiesare in turn enabled by investments and initiatives typically driven by high-levelcorporate decision-making. These, while impacting operations capabilities, havebroader implications and may be motivated by factors other than developingmanufacturing excellence. Such investments include those in advanced technology,information technology, and broader organizational change. The focus of this paperis on the operations capabilities themselves rather than their enablers. Thesecapabilities are important since they not only impact the ability to introduceproducts and compete in different markets and segments but reflect the internallydeveloped infrastructure that drives sustainable success (Hayes and Pisano 1996).It is this competitive advantage that others have referred to as inimitable capability(e.g. Barney 1991). Based on this rationale and building on the model proposed byTan et al. (2004), we conclude that there are three critical elements of operationscapability; new product design and development, JIT, and quality management.

3. Elements of operations capability

3.1 New product design and development capability

With the increasing importance of customer focus, and pressure being put onproduct life cycles by competition, rapid and innovative product development has


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

emerged as a major focus of many organizations. It is the ability of firms to rapidlysense and respond to market signals that provides them with a competitive edge.New product development is, however, inherently costly and risky, particularly whennew technology is involved. This creates opportunities for firms that effectivelymanage development processes, whether via internal capabilities or collaborativeefforts with suppliers (e.g. Ragatz et al. 1997, Tan 2001). The literature identifiesseveral elements of product development processes that drive competitive advantage.For example, part reduction and standardization, concurrent engineering, the use ofcross-functional teams, vendor management, and empowerment are all related toreductions in development cycle times (e.g. Zirger and Hartley 1996, Griffin 1997).Concurrent engineering is also associated with improvements in product qualityand reductions in both development time and cost (Wheelwright and Clark1992, Standish et al. 1994, Hoedemaker et al. 1999). Toyota Motor Corporationrealized a 60% reduction in development costs on new car programmes that theyattributed to the use of cross-functional teams (Chase et al. 1998). Several studieshave found that concurrent engineering also facilitates external integration withsuppliers and customers (Langowitz 1988, Millson et al. 1992). Quality functiondeployment and value analysis/engineering are also associated with enhancementsin product-development processes.

3.2 JIT capability

The elements and impact of JIT capability are well documented. Monden (1983),for example, described JIT practices based on the Toyota Production System.These included product simplification and standardization, simplified and efficientmaterial flow, setup-time reduction, preventive maintenance, improved quality, andcommitment to continuous improvement. Lee and Ebrahimpour (1984) concludedthat top management support, cooperation from the labour force, good processdesign, and effective supplier relationships are also important JIT practices.The results of these efforts can be observed in terms of reductions in inventoryand increases in inventory turnover, and improved product quality and throughput(e.g. Nakamura et al. 1997, Fullerton and McWatters 2001).

3.3 Quality-management capability

An extensive literature exists on how quality-management efforts can be used toimpact a firms success (e.g. Anderson et al. 1995, Flynn et al. 1995b, Ahire et al.1996). Corporate leadership, senior management commitment to a quality strategy,and a customer focus are, for example, key drivers of any quality effort. Theyprovide the basis for efforts to design products and processes, and monitor systemperformance consistent with achieving high levels of quality. They also are drivers ofthe infrastructure that must develop the human resource side of quality management.While not all quality-improvement efforts are successful (e.g. Hendricks and Singhal1997, Easton and Jarrell 1998), there is ample evidence to suggest that customersatisfaction (Anderson et al. 1995), product quality (Ahire et al. 1996, Dow et al.1999), as well as broader measures of manufacturing performance (Flynn et al.1995a, Samson and Terziovski 1999) are positively impacted by quality-managementprogrammes. Not only can strategic quality management affect performance,


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

but so can the effective deployment of quality tools, such as process improvement,statistical process control, and quality training (Kannan et al. 1999).

4. Research model

We posit that a firms competitive position is enhanced by actions it takes to developand execute a strategy based on superior product development, quality focus, andadoption of JIT manufacturing. While these elements of capability have beensuggested previously, the relationship between capability and performance hasyet to be tested. Moreover, unlike prior empirical studies of operations capability,we explore how these capabilities are achieved rather than their outcomes.Specifically, we propose

Proposition 1. Operations capability is a second-order construct comprisingthree interrelated facets; new product design and development capability,JIT capability, and quality management capability.

The elements of operations capability are operationalized similar to the approachused in Tan et al. (2004). New product design and development capability, forexample, is operationalized by a firms commitment to modular design of parts, earlysupplier involvement, concurrent engineering, simplification of parts, standardiza-tion of parts, and value analysis/engineering. JIT capability is operationalized by anorganizations commitment to reducing lot sizes and setup times, maintainingprocess integrity using preventive maintenance, increasing delivery frequencies, andreducing inventory to free up capital and expose manufacturing/schedulingproblems. Quality-management capability is operationalized by an organizationscommitment to maintaining process integrity using statistical process control,designing quality into the product, process improvement, training of employeesin quality management and control, empowering operators to correct qualityproblems, and senior management communicating quality goals to the organization.Further, we propose:

Proposition 2. Operations capability positively affects firm performance.

As the literature suggests, each proposed facet of capability has a direct andpositive impact on both manufacturing and broader measures of businessperformance (e.g. Wheelwright and Clark 1992, Anderson et al. 1995, Fullertonand McWatters 2001, Tan 2001). Firms no longer rely solely on financialperformance measures such as earnings per share or return on investment.Increasingly, strategic objectives and metrics are described in terms of customersatisfaction, quality, and market share, instead of or in addition to traditionalfinancial measures (e.g. Kaplan and Norton 1992). Given an increasingly competitivemarketplace, performance must necessarily be defined and measured on multipledimensions, both financial and non-financial. We therefore operationalize perfor-mance in terms of market share, return on assets, overall product quality, overallcompetitive position, and overall customer service levels.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

Propositions 1 and 2 can be represented by a second-order structural equationmodel (figure 2). The four measurement models, NPDD, JIT, QLT, and PERF,denote the new product design and development, JIT, quality management, and per-formance constructs, respectively. and " represent error variances for the measuredvariables, and represents error variances for the measurement models. representsthe first-order factor loadings, the second-order factor loadings, and the structuralparameter denoting the impact of operations capability on firm performance.

5. Survey methodology

A survey instrument was developed based on the items described above. Five-pointLikert scales (1 low, 5 high) asked how important each of the dimensions of

PERF4

Q4B

Q4C

Q4D

Q4E

Q4A

1. 0

CAP1

JIT2

Q2B

Q2C

Q2D

Q2E

Q2F

Q2A

1. 0

Q3B

Q3C

Q3D

Q3E

Q3F

Q3A

1. 0

QLT3

3

Q1F

Q1B

Q1C

Q1D

Q1E

1.

NPDD1

1. 0

1. 0

1. 0

3

Q1A

1.

1. 0

4,1

1. 0

20

21

22

23

1.020,421,422,423,4

4

1. 0

1. 0

3

1.

1,1

2,1

3,1

1. 0

1.0

8,29,210,211,212,2

1. 0

31.014,315,316,317,318,3

3

1.

1

6

7

8

9

10

11

12

13

14

15

16

17

18

1.02,1

4,15,16,1

3

2

4

5

3,1

1

219

Figure 2. Operations capability model.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

new product design and development, JIT, and quality-management capability werein the reporting firms activities, and how the firm performed relative to majorcompetitors (table 1). Two pre-tests were conducted to assess the content validityof the survey instrument and, where necessary, modifications made to thequestionnaire. Target respondents were identified from membership lists of theInstitute for Supply Management (ISM) and the Association for OperationsManagement (APICS). To increase the likelihood that respondents would befamiliar with their firms strategic goals and operations, the membership lists werepre-screened for only senior supply and operations managers of manufacturingfirms. To further ensure that the survey was administered accurately and consistentwith the goals of the research, it was administered by a professional firm. Standardmail survey procedures were used (Dillman 1999). Specifically, surveys were mailedto the 5470 respondents followed by two follow-up reminders, each 2 weeks apart.The survey was conducted in three phases, and the last survey was received inmid-2000. A total of 527 usable surveys were returned, yielding a responserate comparable to that of prior studies that used similar membership mailing lists(e.g. Fawcett and Magnan 2001).

To test for the homogeneity of the samples derived from the two membershiplists, t-tests were carried out on responses to a number of randomly selectedquestions from each sample as well as the number of employees and annual sales.Results indicated no statistically significant differences in mean responses, therebyenabling the two samples to be combined. To test for non-response bias, sample datawere separated into two groups based on return date, late arriving surveys consideredrepresentative of non-respondents (Lambert and Harrington 1990). The t-tests wereagain carried out on responses to a number of randomly selected questions items andthe number of employees, and annual sales. No statistically significant differencesin mean responses were observed, indicating the absence of non-response bias.Harmans single-factor test was used to test for common method variance(Podsakoff et al. 2003). If common method variance is a serious problem, either asingle factor will emerge or a general factor will account for a large amount of thevariance in the data. A principal-components factor analysis of the data yielded ninefactors, each with eigenvalues greater than 1.0, with no one factor accounting formore than 15% of the total variance. We concluded that common method variancewas not a concern.

6. Statistical analysis

Much of the existing structural modelling research relies on a single sample to testa proposed research model and ignores the need to further validate the proposedtheory with a second sample. This validation is critical however when, as is commonpractice, the proposed model has been re-specified and an alternate model accepted(Hoyle 1995). When a model fits a sample, it implies only that the model providesa plausible representation of the structure that produced the observed data. Ideally,a re-specified model should be validated using data separate from those used tocalibrate the original model. Since the sample size in this study was sufficiently large,the data were randomly divided into two sub-groups. The calibration sample(264 observations) was used to test the measurement models and explore the


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

Table1.

Descriptivestatisticsofsurvey

item

s.

Calibrationsample(n264)

Validationsample(n263)

Constructs

MSE

MSE

1.New

productdesignanddevelopmentcapability(NPDD,1)

(A)

Modulardesignofparts,1,1

3.09

0.078

3.02

0.080

(B)

EarlySupplier

Involvem

ent,2,1

3.33

0.070

3.38

0.076

(C)

Use

ofconcurrentengineering,3,1

3.18

0.071

3.16

0.076

(D)

Simplificationofcomponentparts,4,1

3.31

0.071

3.25

0.080

(E)

Standardizationofcomponentparts,5,1

3.58

0.067

3.63

0.077

(F)

Use

ofvalueanalysis/valueengineering,6,1

3.06

0.070

2.97

0.074

2.Just-in-timecapability(JIT,2)

(A)

Reduce

lotsize,7,2

3.26

0.076

3.20

0.078

(B)

Reduce

setuptime,

8,2

3.56

0.082

3.54

0.078

(C)

PreventiveMaintenance,9,2

3.48

0.072

3.43

0.070

(D)

Increase

deliveryfrequencies,10,2

3.42

0.074

3.46

0.068

(E)

Reduce

inventory

tofree

upcapitalinvestm

ent,11,2

4.09

0.066

4.12

0.067

(F)

Reduce

inventory

toexpose

mfg/schedulingproblems,12,2

3.41

0.079

3.40

0.078

3.Quality-managem

entcapability(QLT,3)

(A)

Statisticalprocesscontrol,13,3

3.23

0.075

3.23

0.079

(B)

Designquality

into

theproduct,14,3

4.03

0.064

4.01

0.067

(C)

Processimprovem

ent(m

odificationofprocess),15,3

3.88

0.062

3.91

0.064

(D)

Employee

trainingin

quality

managem

entandcontrol,16,3

3.83

0.063

3.84

0.068

(E)

Empower

shopoperatorsto

correctquality

problems,17,3

3.73

0.068

3.70

0.069

(F)

Topmanagem

entcommunicationofquality

goals,18,3

3.94

0.062

3.89

0.069

4.Firmperform

ance

(PERF,4)

(A)

Marketshare,20,4

3.71

0.061

3.77

0.059

(B)

Return

onassets,21,4

3.55

0.056

3.56

0.054

(C)

Overallproductquality,22,4

4.25

0.043

4.17

0.045

(D)

Overallcompetitiveposition,23,4

3.94

0.050

3.98

0.050

(E)

Overallcustomer

servicelevels,24,4

4.08

0.049

3.95

0.049

Note:Respondentswereasked

howimportantthepractices/toolswereintheirfirm

sactivities(1lowimportance,5highimportance),andthelevelsofperform

ance

relativeto

thatofmajorcompetitors(1lowperform

ance,5highperform

ance).


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

structural relationships among the latent variables. The validation sample(263 observations) was then used to validate the proposed structural equationmodel. Table 1 presents descriptive statistics of the observed indicators for the fourconstructs in each sample. The risk exists with survey data that responses may beinfluenced by the status and background of the target respondents, in this case seniorsupply and operations managers. They may tend to focus more on measures ofmanufacturing rather than financial performance, and have access to betterinformation on these rather than other metrics. This underscores the importanceof using a composite measure of performance as used in this research instead ofa single observed indicator. However, correlation of performance measures with dataacquired from the Dunn and Bradstreet database, Standard and Poor publications,and company financial reports was statistically significant (p50.05) suggesting theabsence of bias.

A two-step structural equation modelling approach was used to analyse thecalibration sample (Anderson and Gerbing 1988). This approach assesses the validityof the structural model independently of the measurement models. Measurementmodels address the reliability and validity of observed variables in measuringlatent variables, and provide an assessment of convergent and discriminantvalidity (Schumacker and Lomax 1996). The structural equation model specifiesrelationships among latent variables and describes the amount of explained andunexplained variance in the model. This provides an assessment of predictive validity(Byrne 1998). Analysis was carried out with LISREL-SIMPLIS 8.72 using themaximum-likelihood estimation method (Joreskog and Sorbom 1993). After testingthe individual measurement models, the second-order structural model was tested toassess whether operations capability can be adequately measured by the threeproposed constructs (proposition 1). Finally, the impact of operations capabilityon firm performance was tested (proposition 2). To validate the analysis, multiple-group analysis was used to investigate whether the second-order structural equationmodel is invariant across the two samples. Model invariance examines whethera model, when applied to multiple samples, has the same number of latent variableswith the same indicators and specification of fixed and free parameters inthe matrices of factor loadings, structural parameters, and measurement errors(Bollen 1989).

6.1 Analysis of measurement models (g1, g2, g3, g4)

Analysis of the new product design and development (NPDD) measurement modelindicated a correlation between supplier involvement (Q1B) and simplification ofparts (Q1D). Tan et al. (2004) suggested that involving key suppliers early in productdesign activities may adversely impact simplification efforts. Suppliers may,for example, propose using component parts that utilize new technologies andmaterials. While this may enhance product quality, it may also slow down thedevelopment process. An error covariance term was thus added linking the twovariables. (While allowing error terms to correlate can improve model fit, it isappropriate to do so only if there is a theoretical basis to support the correspondingcorrelations (Byrne 1998).) Similarly, error covariance terms were added to linkstandardization of parts (Q1E) with part simplification (Q1D) and value analysis/engineering (Q1F). Value analysis/engineering can increase part standardization


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

by simplifying product and component design while improving performance.The revised model and its corresponding fit indices are shown in figure 3.Standardized factor loadings range from 0.71 to 0.85, and exhibit the expectedpositive sign. Table 2 presents a list of widely used goodness-of-fit criteria and theircorresponding acceptable values. Values for commonly used goodness-of-fit indicessuggest that the NPDD measurement model fits the sample data well.

2 11.01df 62/df 1.8352 p-value 0.088RMSEA 0.056NFI 0.99NNFI 0.98CFI 0.99IFI 0.99RFI 0.97




NPDD

0.43 Q1B Early supplier involvement

0.32 Q1C Concurrent Engineering

0.28 Q1D Simplification of parts

0.50 Q1E Standardization of parts

0.49 Q1F Value Analysis/Engineering

0.47 Q1A Modular design of parts

0.73

0.82

0.71

0.71

0.85

0.76

0.14

0.09

0.10

JIT

0.57 Q2B Reducing setup time

0.72 Q2C Preventive maintenance

0.32 Q2D Increasing delivery frequencies

0.33 Q2E Reducing inventory (Investment)

0.46 Q2F Reducing inv (Expose problems)

0.42 Q2A Reducing lot size

0.76

0.53

0.82

0.73

0.82

0.650.19

0.15

0.08

QLT

0.74 Q3B Design quality into the product

0.58 Q3C Process improvement

0.28 Q3D Employee training in quality

0.37 Q3E Employee empowerment

0.41 Q3F Communication of quality goals

0.71 Q3A Statistical process control

0.54

0.65

0.79

0.77

0.85

0.51

0.13

0.17

PERF

0.68 Q4B Return on assets

0.66 Q4C Overall product quality

0.40 Q4D Overall competitive position

0.57 Q4E Overall customer service levels

0.80 Q4A Market share

0.45

0.58

0.66

0.77

0.56

0.15

0.17

Figure 3. Measurement models and fit indicescalibration sample.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

Table2.

Goodness-of-fitcriteria1.

Goodness-of-fitindex

Acceptablelevel

Comment

2/degrees

offreedom

3.0

2is

sensitiveto

sample

size

anddeparture

from

multivariate

norm

ality

assumption.Largesamplesize(4200)tendsto

resultinsignificant2statistics.

Non-significant2p-valueimplies

thedata

fitthehypothesized

model.

2p-value

Non-significant

Rootmeansquare

errorof

approximation(RMSEA)

0.05

RMSEA0.05indicatesagoodmodelfit.

p-valuefortestofclose

fit

0.50

Averynarrowconfidence

intervalargues

forgoodprecisionoftheRMSEA

value.

Expectedcross-validation

index

(ECVI)

Comparesto

alternative

models

Measuresthediscrepancy

between

thefitted

covariance

matrix

and

the

expectedcovariance

matrixin

another

sampleofequalsize.

Independence

Akaikes

inform

ationcriterion(AIC)

Comparesto

alternative

models

Measurestheextentto

whichestimateswillcross-validate

infuture

samples.

Smaller

valueisdesirable.

Independence

Bozdogans

consistentversionofAIC

(CAIC)

Comparesto

alternative

models

Measurestheextentto

whichestimatedparameterswillcross-validate

infuture

samples.CAIC

considerssamplesize.Smaller

valueisdesirable.

Standardized

rootmeansquare

residual(RMSR)

0.05

Measures

the

average

discrepancy

between

the

sample

observed

and

hypothesized

correlationmatrix.Canbeinterpretedastheaverageerrorof

thecorrelationexplained

bythemodel.

Goodness-of-fitindex

(GFI)

0.90

Indexes

the

relative

amountofthe

observed

variance

and

covariance

accountedforbyamodel.

Adjusted

GFI(AGFI)

0.80

GFIadjusted

fordegrees

offreedom.

ParsimonyGFI(PGFI)

0.50

Addresses

theissueofparsimonyin

SEM.

Norm

edfitindex

(NFI)

0.90

Comparesaproposedmodel

withanull(independence)model.It

tendsto

underestimatefitin

smallsamples.

Non-norm

edfitindex

(NNFI)

0.90

Comparesthelack

offitofatargetmodeltothelack

offitofabaselinemodel.

ParsimonyNFI(PNFI)

Addresses

theissueofparsimony.

Comparativefitindex

(CFI)

0.90

Revised

NFIthattakes

samplesize

into

account.

Increm

entalfitindex

(IFI)

Valueclose

to1

Addressesparsimonyandsamplesize

issuesthatwereknownto

beassociated

withNFI.

Similarto

NFI,

exceptthatdegrees

offreedom

are

taken

into

account.

Relativefitindex

(RFI)

Valueclose

to1

Equivalentto

CFIin

mostSEM

applications.

CriticalN(CN)

200

Focusesdirectlyontheadequacy

ofsamplesize

(rather

thanmodelfit)that

would

besufficientto

yield

anadequatemodelfit.

1Sources:Hoyle(1995),Byrne(1998),andRaykovandMarcoulides

(2000).


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

As indicated by analysis of the JIT measurement model, error covariance termswere added to link reducing setup time (Q2B) with reducing lot size (Q2A),preventive maintenance (Q2C), and reducing inventory to expose production andscheduling problems (Q2F). The underlying premise of the JIT philosophy is thatinventory hides scheduling, production and other problems. JIT manufacturers usingsmall lot sizes to reduce cycle inventory must significantly improve their ability in thearea of rapid changeover. Preventive maintenance is also critical to ensure thatmachinery and equipment operate smoothly despite frequent setups for mixed-modelproduction. The revised model is shown in figure 3. Standardized factor loadingsrange from 0.53 to 0.82, and exhibit the expected positive sign and magnitude.Values for goodness-of-fit indices again suggest that the model fits the sample data.

Analysis of the quality-management measurement model (QLT) suggested thatprocess improvement (Q3C) is correlated with designing quality into the product(Q3B) and statistical process control (Q3A). Process improvements not only enhancea firms ability to design quality into the product, but one manifestation of processimprovement is the use of statistical process control techniques. Error covarianceterms were thus added to the model (figure 3). Standardized factor loadings rangefrom 0.51 to 0.85, and goodness-of-fit index values again suggest good model fit.Similarly, factor loadings exhibit the expected positive sign. Examination ofunstandardized solutions of all three measurement models suggests that allparameter estimates are both reasonable and statistically significant, and that allstandard errors are of acceptable magnitude.

Initial analysis of the firm performance (PERF) measurement model suggestedthat market share (Q4A) is correlated with return on assets (Q4B) and overallcompetitive position (Q4D). As market share increases, return on assets is expectedto improve as a result of increased sales, profitability, and growth opportunities.Increasing market share also strengthens a firms position in the marketplace whichin turn creates further opportunity for consolidation and growth. Conversely, asa firm loses market share, profits and return on assets can be expected to decline,adversely affecting competitiveness. The model was modified to reflect theserelationships by adding corresponding error covariance terms. The modifiedperformance measurement model, with standardized factor loadings ranging from0.45 to 0.77, fits the calibration sample data well (figure 3).

6.2 Second-order operations capability model (m1, g1, g2,g3)

A second-order factor model is one in which correlations among first-order factorscan be represented by a single factor, and in which the higher-order factor ishypothesized to explain all covariance among its first-order constituents. Sinceoperations capability has been shown previously to be adequately measured bynew product design and development, JIT, and quality-management capabilities(Tan et al. 2004), it is logical to extend the model to test operations capability as asecond-order factor model (figure 4). Single-headed arrows from the second-orderfactor (CAP) to each of the first-order factors (NPDD, JIT, QLT) represent freelyestimated regression paths or second-order factor loadings (1,1, 2,1, 3,1).Standardized factor loadings exhibit the expected positive sign and reasonablemagnitude, and values of goodness-of-fit indices suggest that the model fits the datawell. All the three path coefficients are statistically significant. The standardized


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

factor loadings of new product design and development (1,1), JIT capability (2,1),and quality management (3,1), are 0.71, 0.87, and 0.75, respectively. Given theseobservations, it appears that operations capability can be adequately representedby the three latent variables, providing initial support for proposition 1.

6.3 Structural equation model: (b4,1)

The saturated structural equation model was examined to determine whetherestimated parameters agree with a priori specified positive signs and size, andwhether the strengths of the relationships are statistically significant and sufficientlylarge (figure 5). Goodness-of-fit indices suggest that the model fits the data well.The standardized structural parameter (4,1 0.42) exhibits the expected positivesign and is of reasonable magnitude. Moreover, its significance provides supportfor proposition 2 that operations capability positively affects firm performance.

6.4 Testing for invariance across calibration and validation samples

To validate the structural equation model, model invariance analysis was carried outby using the validation sample to verify the structural model and relationshipsdepicted in figure 2. A baseline structural equation confirmatory factor analysismodel was established using the validation sample (figure 6). An identical set ofindicators, error covariance terms, and structural relationships as depicted in figures3 and 5, were used in developing the model. All model specifications are identical forboth groups since the main goal of a cross-validation application is to determinewhether the final model derived from the calibration sample can be replicated across

Fit Indices


CAP1

0.71

0.87

0.75

JIT2

0.25

QLT2

0.44

0.50

NPDD1

0.71

0.87

0.75

0.25

0.44

0.50

Figure 4. Operations capability modelcalibration sample.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

Fit Indices


CAP1

0.66

0.73

0.85

JIT2

0.47

QLT3

0.28

0.56

NPDD1

PERF4

0.76

0.49

0.66

0.73

0.85

0.47

0.28

0.56

0.76

0.49

Figure 6. Operations capability model validation.

Fit Indices

2 383.41df 2162 /df 1.7752 p-value 0.000RMSEA 0.054NFI 0.95NNFI 0.97CFI 0.98IFI 0.98RFI 0.95

CAP1

0.71

0.82

0.79

JIT2

0.33

QLT3

0.37

0.49

NPDD1

PERF4

0.83

0.42

0.71

0.82

0.79

0.33

0.37

0.49

0.83

0.42

Figure 5. Operations capability modelcalibration model.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

the validation sample (Byrne 1998). Model fit indices suggest the model fits thevalidation sample well, confirming that operations capability can be adequatelymeasured by the proposed three-factor, second order structural equation model. Thestandardized operations capability path coefficients (1,1 0.66, 2,1 0.73,3,1 0.85) and the standardized structural parameter (4,1 0.49) again exhibitthe expected positive sign and are of reasonable magnitude.

To compare whether the calibration and validation baseline models are invariant,we first estimate both models simultaneously without imposing the equalityconstraints (Byrne 1998). The goodness-of-fit indices, which reflect the simultaneousestimation of the model for both the calibration and validation samples, show that2df459 880.60, NFI 0.95, RFI 0.94, and NNFI, CFI, and IFI all equal 0.97.The value of GFI is 0.88. This model is used as a benchmark against which toestablish the tenability of imposed equality constraints. Next, we estimate the mostrestrictive model by imposing equality constraints on the error covariance terms,factor loadings, and structural paths across the two groups. The most restrictivemodel and its corresponding goodness-of-fit indices are shown in figure 7. Althoughthe value of 2df33 40.24 is only marginally insignificant, the values of NFI,NNFI, CFI, IFI, and RFI remained unchanged after the equality constraints wereimposed. (2 920.84 880.60 40.24; df 492 459 33 (10 error covarianceterms 23 factor loadings 4 structural paths 4 fixed paths 33).) We can thusconclude that the model in figure 7 is invariant across the calibration and validationsamples. This analysis provides additional support for representing operationscapability as a second-order construct, and for the proposition that operationscapability positively affects performance. The most restrictive invariant modelin figure 7 shows that quality-management capability has the greatest impact on

Global goodness of fit statistics


Group goodness of fit statistics

Contribution to 2 427.78% Contribution to 2 46.87RMR 0.098Standardized RMR 0.078GFI 0.88

CAP1

0.69

0.76

0.83

JIT2

QLT3

0.53

NPDD1

PERF4

0.790.69

0.76

0.83

0.42

0.32

0.53

0.46

Figure 7. Invariant model with equality constraints imposed.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

operations capability (3,1 0.83), followed by JIT (2,1 0.76) and new productdesign and development capability (standardized 1,1 0.69). The structuralparameter (4,1 0.46) verifies that operations capability positively impactsperformance.

7. Discussion and managerial implications

Support for proposition 1 is consistent with the results of Tan et al. (2004),suggesting that firms need to build capability in the areas of quality management,new product design and development, and JIT. It does not, however, preclude therebeing other dimensions of capability. This is consistent with the literature thatsuggests that improvement programmes are at the core of the content of operationscapability but that capability also reflects broader efforts to improve productivityfirm wide (e.g. De Meyer et al. 1989, Roth and Miller 1992). What distinguishesquality, just in time, and product development however is that not only do they haveroots in the operations domain, but they represent direct efforts to improveoperations performance as opposed to factors such as the enhancement ofinformation technology and changes in organizational structure. These enablerscut across the organization in addition to facilitating improvements withinoperations. Examination of their role in facilitating the development of operationscapability represents a logical extension of the current study,

The global model (figure 7) suggests that quality-management capability has thelargest impact on operations capability followed by JIT and new product design anddevelopment capabilities. The implication is that while the relative importance ofeach varies, potential benefit exists from building capabilities in all three areas.Moreover, it should be noted that given inherent relationships between the three,not only can improvements in one area be expected to result in improvements inothers, but failure to develop in one area may hinder efforts elsewhere.The observation that all three dimensions of capability have positive and significantcoefficients is consistent with a firm being able to simultaneously possess multiplecapabilities. While the results do not explicitly address the issue of the developmentsequence, the fact that quality management has the greatest individual impact maybe an indication that it has had the longest time to develop. This is consistentwith the contention of the sand cone theory that quality capability should bedeveloped first.

While it is widely accepted that operations capability positively affects firmperformance, this study provides empirical support for this relationship. As a firmimproves its operations capability, it enhances its overall competitive position andability to achieve customer satisfaction. This translates into pricing flexibilityand faster diffusion of new products, allowing the firm to increase market share andimprove financial and market performance. The implication is that the developmentof operations capability is a key to improving not only manufacturing-relatedperformance but broader measures of financial and market-based performance.However, the standardized structural parameter (figure 7; 4,1 0.50) suggests thatoperations capability as defined here does not fully explain all variation inperformance. This is again consistent with other factors such as managing thesupply chain and developing and implementing integrated information systems,


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

being related dimensions of capability. It should be noted that the results do notallow judgements to be made about the relative contribution of individualdimensions of capability to performance. This would, for example, provide insightinto how organizations should deploy scarce resources across the three dimensions ofcapability and whether in fact it is necessary to do so to enhance businessperformance. The focus of this study however was on the relationship betweencapability as a multi-dimensional construct and performance. The literature onquality management, JIT, and product development, have amply demonstrated therelationship between each individually and performance. Moreover, prior studieshave demonstrated the relative importance to performance of combinations of thethree (e.g. Flynn et al. 1995, Nakamura et al. 1997)

8. Conclusion

While operations capability has been researched extensively, little empirical evidenceexists of its content. This study confirms case and anecdotal evidence that a firmsoperations capability, when viewed from an input, activity-based perspective, ismultifaceted. While results suggest that quality-management capability has thegreatest individual influence on operations capability followed by JIT, and newproduct design and development capabilities, this should not be interpreted asquality management being more important than others. Rather, it suggests that toachieve operations capability, an organization must develop capabilities in all threeareas concurrently to achieve long-term, sustainable competitive advantage. Resultsalso provide support for the proposition that operations capability positively impactsperformance. This is consistent with findings in the literature that emphasize theimportance of developing and nurturing operations capabilities as a means toachieving long-term, sustainable competitive success. This implicitly providessupport for the notion that firms should develop multiple capabilities, and refutesthe notion that firms cannot simultaneously excel on multiple dimensions ofcapability.

The results are also significant in that they demonstrate that operations capabilityis positively related to a comprehensive measure of both internal and market-oriented performance. This is important for a number of reasons. Prior research hasfocused on measures of financial or manufacturing performance. This has precludedexploration of the impact of capability on a firms ability to position itself in themarketplace. The results here suggest that by positively impacting performance thatreflects competitive position and customer service levels, superior operationscapability may provide the firm with leverage in the marketplace. This may enableit, for example, to adopt more aggressive pricing strategies, achieve more rapidmarket penetration with new products, or respond more rapidly to changes incustomer preferences. These in turn drive market share and return on assets,reinforcing the cycle of performance improvement. The results also underscorethe importance of JIT capability. It is generally recognized that new product designand development, and quality-management capabilities are essential to competeeffectively. To the extent that JIT capability is a reflection of an orientationtowards leanness, it demonstrates that leanness is an important aspect of operationscapability. This corresponds with the experiences of companies like Dell Inc.,


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

which have restructured and/or focused on waste reduction in response to fiercecompetition.

From a methodological perspective, a significant contribution of this study hasbeen the use of multiple samples to calibrate and validate a structural model. The useof structural equation modelling has become commonplace in the operationsliterature in recent years. However, the results of many of the studies to use thismethodology are compromised by the fact that models have been re-specified buttested using the same data used to develop the original model. The use of multiplesamples in this study provides evidence of the robustness of the results. The study isnot however without its limitations. We have relied upon information collected froma single respondent from each organization and assumed that they have theknowledge of their firms operations necessary to make reasonable judgements onthe issues of interest. Moreover, despite using two professional organizations todevelop the sampling frame and achieving a response rate similar to that obtained inseveral similar studies, it was lower than others in this domain. While steps weretaken to mitigate the impact of these factors, one cannot overlook the potential biasattributable and thus the ability to generalize from the results.

References

Ahire, S.L., Golhar, D.Y. and Waller, M.A., Development and validation of TQMimplementation constructs Decis. Sci., 1996, 27, 2356.

Anderson, J.C. and Gerbing, D.W., Structural equation modeling in practice: a review andrecommended two-step approach Psychol. Bull., 1988, 103, 411423.

Anderson, J.C., Rungtusanatham, M., Schroeder, R.G. and Devaraj, S., A path analyticmodel of a theory of quality management underlying the Deming management method:preliminary empirical findings Decis. Sci., 1995, 26, 637658.

Barney, J., Firm resources and sustained competitive advantage J. Manage., 1991, 17, 99120.Bollen, K.A., Structural Equations with Latent Variables, 1989 (Wiley: New York).Boyer, K.K. and Lewis, M.W., Competitive priorities: Investigating the need for trade offs

in operations strategy Prod. Oper. Manage., 2002, 11, 920.Byrne, B.M., Structural Equation Modeling with Lisrel, Prelis, and Simplis: Basic Concepts,

Applications, and Programming, 1998 (Erlbaum: Mahwah, NJ).Chase, R.B., Aquilano, N.J. and Jacobs, F.R., Production and Operations Management:

Manufacturing and Services, 8th ed., 1998 (Irwin/McGraw-Hill: New York).Cleveland, G., Schroeder, R.G. and Anderson, J.C., A theory of production competence

Decis. Sci., 1989, 20, 655668.Cool, K. and Schendel, D., Performance differences among strategic group members

Strat. Manage. J., 1988, 9, 207223.Corbett, C. and van Wassenhove, L., Trade-offs? What trade-offs? Competence and

competitiveness in manufacturing strategy Calif. Manage. Rev., 1993, 35, 197222.Corbett, L.M. and Claridge, G.S., Key manufacturing capability elements and business

performance Int. J. Prod. Res., 2002, 40, 109131.De Meyer, A., Nakane, J., Miller, J.G. and Ferdows, K., Flexibility: the next competitive

battle, the manufacturing futures survey Strat. Manage. J., 1989, 10, 135144.Dierickx, I. and Cool, K., Asset stock accumulation and sustainability of competitive

advantage Manage. Sci., 1989, 35, 15041510.Dillman, D., Mail and Internet Surveys: The Tailored Designed Method, 2nd ed., 1999 (Wiley:

New York).Dow, D., Samson, D. and Ford, S., Exploding the myth: Do all quality management practices

contribute to superior quality performance? Prod. Oper. Manage., 1999, 8, 127.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

Easton, G.S. and Jarrell, S.L., The effects of total quality management on corporateperformance: an empirical investigation J. Bus., 1998, 71, 1535.

Fawcett, S.E. and Magnan, G.M., Achieving World-Class Supply Chain Alignment: Barriers,Bridges, and Benefits, 2001 (Center for Advanced Purchasing Studies Press: Tempe,AZ).

Ferdows, K. and De Meyer, A., Lasting improvements in manufacturing performance:in search of a new theory J. Oper. Manage., 1990, 9, 168184.

Flynn, B.B. and Flynn, J.E., An exploratory study of the nature of cumulative capabilitiesJ. Oper. Manage., 2004, 22, 439457.

Flynn, B.B., Sakakibara, S. and Schroeder, R.G., Relationship between JIT and TQM:practices and performance Acad. Manage. J., 1995a, 38, 13251360.

Flynn, B.B., Schroeder, R.G. and Sakakibara, S., The impact of quality manage-ment practices on performance and competitive advantage Decis. Sci., 1995b, 26,659691.

Fullerton, R.R. and McWatters, C.S., The production performance benefits from JITimplementation J. Oper. Manage., 2001, 19, 8196.

Garvin, D.A., Manufacturing strategic planning Calif. Manage. Rev., 1993, 35, 85106.Grant, R.M., The resource-based theory of competitive advantage: implications for strategy

formulation Calif. Manage. Rev., 1991, 33, 114135.Griffin, A., Modeling and measuring product development cycle time across industries

J. Eng. Technol. Manage., 1997, 14, 124.Hall, R.W. and Nakane, J., Flexibility: Manufacturing battlefield of the 90s: Attaining

manufacturing flexibility in Japan and the United States, 1990 (Association forManufacturing Excellence: Wheeling, IL).

Hammer, M. and Champy, J., Reengineering the Corporation: A Manifesto for BusinessRevolution, 1993 (Harper Business: New York).

Hayes, R.H. and Pisano, G.P., Manufacturing strategy: At the intersection of two paradigmshifts Prod. Oper. Manage., 1996, 5, 2541.

Hayes, R.H. and Wheelwright, S.C., Restoring Our Competitive Edge: Competing ThroughManufacturing, 1984 (Wiley: New York).

Hendricks, K.B. and Singhal, V.R., Does implementing an effective TQM program actuallyimprove operating performance? Empirical evidence from firms that have won qualityawards Manage. Sci., 1997, 43, 12581274.

Hoedemaker, G.M., Blackburn, J.D. and van Wassenhove, L.N., Limits to concurrency Decis.Sci., 1999, 30, 118.

Hoyle, R.H., Structural Equation Modeling: Concepts, Issues, and Applications, 1995 (Sage:Thousand Oaks, CA).

Joreskog, K.G. and Sorbom, D., LISREL8: Structural Equation Modeling with the SIMPLISCommand Language, 1993 (Erlbaum: Hillsdale, NJ).

Kannan, V.R., Tan, K.C., Handfield, R.B. and Ghosh, S., Tools and techniques ofquality management: An empirical investigation of their impact on performanceQual. Manage. J., 1999, 6, 3449.

Kaplan, R.S. and Norton, D.P., The balanced scorecard: Measures that drive performanceHarv. Bus. Rev., 1992, JanuaryFebruary, 7179.

Lambert, D.M. and Harrington, T.C., Measuring non-response bias in mail surveysJ. Bus. Logist., 1990, 11, 525.

Langowitz, N., An exploration of production problems in the initial commercial manufactureof products. Res. Policy, 1988, 17, 4354.

Lawless, M.W., Bergh, D.D. and Wilsted, W.D., Performance variations among strategicgroup members: An examination of individual firm capability J. Manage., 1989, 15,649661.

Lee, S.M. and Ebrahimpour, M., Just-In-Time production system: Some requirementsfor implementation Int. J. Oper. Prod. Manage., 1984, 4, 315.

Leong, G.K., Snyder, D.L. and Ward, P.T., Research in the process and content ofmanufacturing strategy OMEGA: Int. J. Manage. Sci., 1990, 18, 109122.

Millson, M.R., Raj, S.P. and Wilemon, D.A., A survey of major approaches for acceleratingnew product development J. Product Innov. Manage., 1992, 9, 5369.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

Monden, Y., Toyota Production Systems: Practical Approach to Production Management, 1983(Institute of Industrial Engineers: Norcross, GA).

Nakamura, M., Sakakibara, S. and Schroeder, R., Adoption of just-in-time manufacturing atUS and Japanese owned plants: Some empirical evidence IEEE Trans. Eng. Manage.,1997, 45, 230240.

Nakane, J., Manufacturing Futures Survey in Japan: A Comparative Survey, 19831986(Waseda University: Tokyo).

Narasimhan, R.N. and Jayaram, J., An empirical investigation of the antecedents andconsequences of manufacturing goal achievement in North American, European,and Pan Pacific firms J. Oper. Manage., 1998, 16, 159176.

Noble, M.A., Manufacturing strategy: testing the cumulative model in a multiple countrycontext Decis. Sci., 1995, 26, 693720.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. and Podsakoff, N.P., Common methods biasesin behavioral research: a critical review of the literature and recommended remediesJ. Appl. Psychol., 2003, 88, 879903.

Ragatz, G.L., Handfield, R.B. and Scannell, T.V., Success factors for integrating suppliersinto new product development J. Prod. Innov. Manage., 1997, 14, 190202.

Raykov, T. and Marcoulides, G.A.A First Course in Structural Equation Modeling, 2000(Erlbaum: Mahwah, NJ).

Roth, A.V. and Miller, J.G., Success factors in manufacturing Bus. Horiz., 1992, JulyAugust,7381.

Roth, A.V. and van de Velde, M., Operations as marketing: a competitive service strategyJ. Oper. Manage., 1991, 10, 303328.

Safizadeh, M.H., Ritzman, L.P. and Mallick, D., Revisiting alternative theoretical paradigmsin manufacturing strategy Prod. Oper. Manage., 2000, 9, 111127.

Samson, D. and Terziovski, M., The relationship between total quality management practicesand operational performance J. Oper. Manage., 1999, 17, 393409.

Schmenner, R.W. and Swink, M.L., On theory in operations management J. Oper. Manage.,1998, 17, 97113.

Schumacker, R.E. and Lomax, R.G., A Beginners Guide to Structural Equation Modeling,1996 (Erlbaum: Mahwah, NJ).

Skinner, W., Manufacturing: missing link in corporate strategy Harv. Bus. Rev., 1969,MayJune, 156167.

Standish, R., Jones, R., Sumpter, C. and Sharp, J., Shortening the new product introductioncycle through electronic data transfer Int. J. Prod. Econ., 1994, 34, 347357.

Swink, M. and Hegarty, W.H., Core manufacturing capabilities and their links to productdifferentiation Int. J. Oper. Prod. Manage., 1998, 18, 374396.

Tan, K.C., A structural equation model of new product design and development Decis. Sci.,2001, 32, 195226.

Tan, K.C., Kannan, V.R., Jayaram, J. and Narasimhan, R., Acquisition of operationscapability: a model and test across US and European Firms Int. J. Prod. Res., 2004, 42,833851.

Ward, P.T., McCreery, J.K., Ritzman, L.P. and Sharma, D., Competitive prioritiesin operations management Decis. Sci., 1998, 29, 10351046.

Wheelwright, S.C. and Clark, K.B., Revolutionizing Product Development: Quantum Leapsin Speed, Efficiency, and Quality, 1992 (Free Press: New York).

White, G.P., A meta analysis model of manufacturing capabilities J. Oper. Manage., 1996, 14,315331.

Zirger, B.J. and Hartley, J.L., The effect of accelerated techniques on product developmenttime IEEE Trans. Eng. Manage., 1996, 43, 143152.


Dow

nloa

ded

by [I

ndian

Insti

tute o

f Tec

hnolo

gy R

oork

ee] a

t 06:0

9 04 S

eptem

ber 2

014

00207540600871269

Documents

Transcript of 00207540600871269