Relationship between qualitymanagement information andoperational performance

International perspective

Phan Chi AnhFaculty of Business Administration,

University of Economics and Business Vietnam National University,Hanoi, Vietnam and

Faculty of Business Administration, Yokohama National University,Yokohama, Japan, and

Yoshiki MatsuiInternational Graduate School of Social Sciences,

Faculty of Business Administration, Yokohama National University,Yokohama, Japan

Abstract

Purpose The purpose of this paper is to examine whether quality management information (QMI)can be a source of competitive advantage and should be managed strategically.

Design/methodology/approach Analysis of variance and regression techniques were applied tothe database of the high-performance manufacturing (HPM) project to analyze the differences andsimilarities existing across the countries on the degree of implementation of QMI practices and theircontribution to operational performance of manufacturing plants.

Findings The results of statistical analysis indicate significant differences in the implementation ofQMI practices across the countries. This study highlights the important role of QMI in Japanese plantswhere shop-floor and cross-functional communication and information sharing practices significantlyimpact on different dimensions of operational performance.

Practical implications This study suggests that HPM could be achieved by the implementationof a set of communication and information sharing practices in shop-floor and cross-functional levelsof manufacturing plants.

Originality/value Although scholars considered information as one dimension of qualitymanagement, existing quality management literature provides little empirical evidence on therelationship of QMI and operational performance of manufacturing plants. This paper fills the gap byintroducing a comprehensive research framework to analyze the communication and informationsharing practices in the shop-floor and cross-functional levels.

Keywords Quality management, Management information systems,Operations and production management

Paper type Research paper

IntroductionQuality management information (QMI) refers to the systematic collection and analysisof data in a problem-solving cycle to identify critical problems, find their root causes,and generate solutions to the problems. Effective implementation of QMI allows the

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

www.emeraldinsight.com/2040-8269.htm

QMI andoperational

performance

519

Management Research ReviewVol. 34 No. 5, 2011

pp. 519-540q Emerald Group Publishing Limited

2040-8269DOI 10.1108/01409171111128706

manufacturers to improve product and service quality and facilitate their supplierrelationship management (Flynn et al., 1994; Forza and Flipini, 1998; Kaynak, 2003;Morita et al., 2001; Schniederjans et al., 2006). Recently, greater attention has been paid toQMI by such international standards and awards as ISO 9000, Malcom BaldrigeNational Quality Award, and Japan Quality Award. Although scholars consideredinformation as one dimension of quality management, existing quality managementliterature provides little empirical evidence on the relationship of QMI practices andoperational performance of manufacturing plants. This study aims to fill this gap byresponding to the following questions:

. What are similarities and differences in the perception of QMI practices acrosscountries?

. Do QMI practices positively relate to various dimensions of operationalperformance of manufacturing plants such as quality, cost, delivery, flexibility, etc?

To be competitive in global market, many manufacturing companies have implemented aset of practices such as total quality management (TQM), just in time (JIT), and totalproductive maintenance (TPM) that hereafter broadly labeled as high-performancemanufacturing (HPM) initiatives. HPM literature indicates that effective implementationof such HPM practices highly depend on how the companies manage the communicationand information flow. This study examines QMI by introducing a set of communicationand information sharing practices at shop-floor and cross-functional levels ofmanufacturing plants. These practices reflect various types of communication andinteraction within shop floor and between functions/departments of manufacturing plantssuch as information feedback, suggestions, training, small group activities,cross-functional product design, coordination of decision between departments, etc.This study utilizes survey data which have been gathered from 167 manufacturing plantsin six countries during 2003-2004 in the framework of HPM project. The statistical resultsindicate the significant difference in the perception of the QMI practices across thecountries. Plants in the USA and Sweden show their stronger emphasis on QMI practicesthan other plants, particularly those in Japan and Italy. All the countries except Japan andKorea place their higher attention on cross-functional practices than shop-floor practices.The significant difference among countries in the effect of QMI practices on performance isdetected. The connection between the QMI practices and high performance in Japaneseplants appears tight, comparing with other countries. These findings are consistent withthe institutional theory when the institutions are taken to be the countries. Nationalculture, geographical specifics, and competitive environment may account for thedifferences we observe in communication and information sharing practices across thecountries. The linkage between QMI and operational performance found in this studysuggests that HPM could be obtained by implementing a set of communication andinformation sharing practices. The remaining of this paper presents the literature andresearch framework, which are followed by the descriptions of data collection,measurement test, and hypothesis testing. The last three sections discuss on the importantfindings, the limitations of this research, and the final conclusions.

Literature reviewThe impact of QMI on performance has been widely investigated by scholars (Flynn et al.,1994; Forza and Flipini, 1998; Morita et al., 2001; Kaynak, 2003; Schniederjans et al., 2006).

MRR34,5

520

Flynn et al. (1994) indicate that process management strongly depends on how processsowner collect and analyze data at the source to take immediate problem-solving action.Quality performance data such as defect rate, scrap, and rework must be collected,analyzed, shared, and used for quality improvement. Design quality also depends onQMI because QMI provides a wide range of data from purchasing, marketing,manufacturing, design, customers, and suppliers in order to design quality into products.To support suppliers for improving product quality, manufacturing plants need to createa database about the suppliers performance regarding quality, delivery, purchasingcost, etc. so that managers and employees can identify and solve problems from materialsand parts supplied and provide the suppliers timely and important feedbacks to improvetheir performance (Kaynak, 2003). In summary, empirical studies on qualitymanagement emphasize importance of QMI as follows:

. timely quality measurement;

. feedback of quality data to employees and managers for problem solving;

. evaluation of managers and employees based on quality performance; and

. availability of quality data.

Recently, researchers find that systematic management of information and dataresource is also important to the use of advanced quality management methods such asSix Sigma, which is itself a data-driven approach to eliminate defects and wastes inbusiness processes. Researchers agree that the execution of Six Sigma relies on theavailability and accuracy of QMI because quality metrics can only be used for qualityimprovement when they are calculated from reliable and valid data (Zu et al., 2008).To successfully implement QMI practices, many requirements need to be satisfied asindicated from empirical literature. Effective QMI directly depends on customer focus,workforce management, and top management support. Workforce management isconsidered as infrastructure for quality management and it facilities the collection anduse of QMI by increasing employees continuous awareness of quality-related issues andempowering employees in quality decision making. Close contact with customers,frequent visit to customers, and customer surveillance allow the firm to obtain productand service quality information and use it for further quality improvements.For manufacturing organization, QMI is a critical issue influencing its long-termviability. However, little empirical research has been conducted with the internationalperspective of QMI even in manufacturing sectors (Parast et al., 2006). Early studieson international comparison of quality management mainly focused on comparingthe quality practices between the USA and Japan (Garvin, 1986; Flynn, 1992). Recently,the scope of international comparison of quality management has been extended tostudy the quality practices in other countries and regions around the world (Madu et al.,1995; Rao et al., 1997; Flynn and Saladin, 2006; Phan and Matsui, 2009). Most of thesestudies use different frameworks, instruments, and constructs for measuring andcomparing quality management practices across the countries. As discussed in theliterature, the question regarding the universal applicability of quality managementhas not been fully answered, and more empirical studies on internationalcomparison of quality management are needed (Sila and Ebrahimpour, 2003;Rungtusanatham et al., 2005).

QMI andoperational

performance

521

Research frameworkQMI improves quality performance through collecting, storing, analyzing, and reportinginformation on quality to assist decision makers at all level. This concept requires inputfrom a variety of functional areas and recognized that information consists of not onlydata but also other knowledge needed for decision making ( Juran and Gryna, 1980;Forza, 1995). Schroeder and Flynn (2001) argue that successful implementation ofvariety of manufacturing management practices such as TQM, JIT, and TPM depend onhow the manufacturing plants develop their horizontal linkage structure throughout thecommunication network. The communication and action process is one of theunderlying forces that have made such practices as TQM and JIT so successful.

While most of quality management literature have emphasized on the importance ofavailability, accuracy, and timeliness of QMI, this study focuses on how themanufacturing plants develop QMI through facilitating communication and informationsharing practices to achieve HPM. The flow of communication and information sharingis distinguished into two categories: shop-floor and cross-functional levels. Shop-floorQMI concentrates on the collection, analysis, and feedback of quality information on theshop floor where products are created. It relates with two-way communications betweenmanagers/engineers and workers and between workers themselves. Conducting smallgroup activities is the means for employees to share their ideas and expertise for qualityimprovement. In addition, along with the feedback of quality performance, employeessuggestions should be formally acknowledged to encourage the employeesparticipation in quality improvement. Cross-functional QMI, on the other hand, relateswith communication and information sharing between functions/departmentsconcerning with coordination, new product development efforts, and the interactionwith customers and suppliers. Communication and information sharing betweendifferent functions are important for making quality decisions especially to solve criticalquality problems. External communication with customers and suppliers is also crucialfor quality management. Close contact with customers, frequent visit to customers, andregular customers survey are the best ways to capture customers needs andexpectations while sharing information with suppliers improves their mutual trustwithin the supply chain. The framework of this study is simply shown in Figure 1.

Prior to examining the linkage between QMI and operational performance, thisstudy empirically compares the degree of implementation of QMI practices across thecountries. This is important as we can determine whether QMI depends on thecontextual factors such as national culture or geographical specifics. Some scholars

Figure 1.Research framework

Shop-floor qualitymanagement information

practices

Cross-functional qualitymanagement information

practices

Operationalperformance

MRR34,5

522

argue that, with the evolvement and spreading of modern technologies, benchmarking,organizations may design their operational structure in similar ways in order to beefficient and effective (Form, 1979). Other scholars, however, indicate the linkagebetween information and national specifics (Wacker and Sprague, 1998; Snell and Hui,2000). More recently, Flynn and Saladin (2006) point out that such component ofquality management as QMI would be influenced by Hofstede national culture values.The power distance, individualism, masculinity, and uncertainty avoidance may affectthe use of information to support decision making. For example, high power distancecultures may restrict learning opportunities to high-status members and discourageopen access to information and information sharing between different organizationallevels. Members of collectivist national cultures are more likely to rely on informationprovided though teamwork and cross-functional collaboration. Because of a lack ofdevelopment of valid instruments on QMI, the results of previous QMI studies cannotbe generalized. The question regarding the universality of QMI and its linkage withperformance has not been answered. More empirical and cross-country research isneeded in QMI study. Then, we establish comprehensive instruments on QMI and testwhether country location influences the implementation of QMI practices. The firsthypothesis is presented as follows:

H1. There is difference in the implementation of QMI practices across the countries.

The contribution of communication and information sharing to quality performance orsupply chain performance has been identified in the existing literature (Forza, 1995; Carrand Kaynak, 2007). The use of bilateral relations, including lateral forms ofcommunication and joint decision-making processes increases information systemscapacity. This permits problems to be solved at the level where they occur, rather thanbeing referred upward in the hierarchy, increasing the capacity of the organization toprocess information and make decisions by increasing the discretion at lower levels ofthe organization (Phan and Matsui, 2009). Flynn and Flynn (1999) suggest that the use oflateral relations would moderate the adverse impact of environmental complexity,thereby improving manufacturing performance. We assume that, shop-floor QMI is acritical element for process control and improvement. The application and results ofstatistical process control need to be intensively discussed and shared on the shop floorto solve the problems. Process variation and quality problems should be detected,analyzed, controlled, and eliminated through several activities such as shop-floorinformation feedback, interaction between managers/engineers and workers, smallgroup activities, etc. As cited in the existing literature, the reduction of defectiveproducts leads to a reduction of time delay for rework, inspection, and time for machinestop. These allow the production run faster with shorter consuming time from materialreceiving to customer delivery. Thus, shop-floor QMI practices would relate with thevarious dimensions of operational performance: product cost, on-time delivery, andflexibility to change the production volume. Cross-functional QMI, in other way, wouldcontribute to design quality and new product development lead time. Fast identificationof customers expectations and translating those expectations into productspecifications requires intensive interaction with customers in various channels suchas web/fax/phone contacts, survey, or direct visits. The reduction of time-to-market andimprovement of the design quality would be achieved though the cross-functionalproducts design effort. This is an overlap design/engineering practice that includes

QMI andoperational

performance

523

all functions from the beginning of new product development project. Suppliers can beregarded as an external process of the plants. Collaboration with suppliers throughopening and sharing information concerning quality problems and design changeswould also allow the plants to improve product quality and save production cost.The hypothesis on the relationship between QMI practices and operational performance,therefore, is presented as follows:

H2. QMI practices positively relate to operational performance.

To test the hypotheses, analysis of variance (ANOVA) and regression analysis areused to compare those practices across the countries and identify whether QMIsignificantly impact 13 operational performance indicators.

Research variablesFrom literature reviewing, ten measurement scales are developed to examine QMIunder two perspectives: shop floor and cross-functional as mentioned early.

Shop-floor QMI includes six measurement scales as follows:

(1) Feedback measures whether the plant provides shop-floor personnel withinformation regarding their performance (including quality and productivity) ina timely and useful manner.

(2) Shop-floor contact measures the level of interaction between managers,engineers, and workers, on the shop floor. A high degree of interaction betweenmanagement and workers is thought to promote problem solving and generalimprovement.

(3) Employee suggestions measures employees perception regardingmanagements implementation and feedback on employee suggestions.

(4) Small group problem solving evaluates how the plant uses teamworkactivities to solve quality problems.

(5) Supervisory interaction facilitation measures whether supervisorssuccessfully encourage workers works as team, including expressing theiropinions and cooperating with each other to improve production.

(6) Multi-functional employees determines if employees are trained in multipletask/areas; that is, received cross-training so that they can perform multipletasks or jobs.

Cross-functional QMI includes four measurement scales as follows:

(1) Coordination of decision making determines cross-functional cooperation andcommunication in the plants.

(2) Cross-functional product design measures the level about amount of inputthat the manufacturing function has in the new product introduction process.This includes cooperation and input into process across functional boundaries.

(3) Communication with customers assesses the level of customer contact,customer orientation, and customer responsiveness.

(4) Communication with suppliers assesses whether plants develop trust-basedrelationship with suppliers by exchanging communication and sharinginformation.

MRR34,5

524

A total of 13 measurement items are used to evaluate different dimensions of operationalperformance of the plants: unit cost of manufacturing, conformance to productspecifications, on-time delivery performance, fast delivery, flexibility to change productmix, flexibility to change volume, inventory turnover, cycle time (from raw materials todelivery), new product development lead time, product capability and performance,on-time new product launch, product innovativeness, and customer support and service.Those items are summed up to form overall operational performance.

Because the objective of this study is to identify impacts of QMI practices onoperational performance that can be generalized across countries and industries, theeffects of country and industry need to be removed prior to evaluating the relationshipbetween QMI practices and operational performance. We, therefore, include thefollowing control variables in the regression analyses. Five country control variables:USA (the USA compared to Japan), ITA (Italy compared to Japan), SWE (Swedencompared to Japan), KOR (Korea compared to Japan), and AUT (Austria compared toJapan) are used to represent the five countries. Similarly, two industry control variables,MAC (machinery industry compared to automobile industry) and EE (electric andelectronics industry compared to automobile industry), are used to represent the threeindustries from which the data were collected.

Data collectionThis study explores data gathered through the international joint research initiativecalled High-Performance Manufacturing (HPM) Project started in 1980s by researchersat the University of Minnesota and Iowa State University. The overall target of thisproject is to study best practices in manufacturing plants and their impact on plantperformance in the global competition. The first round of the survey was conducted in1989 gathering information from 46 US manufacturing plants. In 1992, the project wasexpanded to include researchers from Germany, Italy, Japan, and the UK. The secondround of the survey gathered data from 146 manufacturing plants from the abovecountries. In 2003, the project was expanded to include other researchers from Korea,Sweden, Finland, Austria, and Spain. The total number of manufacturing plantsparticipated in the third round of the survey is 210 except Spanish plants. Within eachcountry, surveyed are plants with more than 100 employees belonging to one of threeindustrial fields electrical and electronics, machinery, and transportation.

The researchers, based on business and trade journals and financial information,identified manufacturers as having either a world-class manufacturer (WCM) or anon-WCM reputation. Each manufacturer selected one typical plant for participatingin the project. This selection criterion allowed for the construction of a sample withsufficient variance to examine variables of interest for the research agenda.

In this research, the authors can acquire data from 167 manufacturing plants insix countries: the USA, Japan, Italia, Sweden, Austria, and Korea during 2003-2004.The key characteristics of these plants are summarized in Table I.

In each plant, the degree of implementation of QMI practices and continuousimprovement and learning is evaluated by nine positions such as direct workers,supervisors, process engineer, quality manager, production control manager, inventorymanager, human resource manager, plant superintendent, and a member of new productdevelopment team as summarized in Table II. Ten QMI measurement scales are constructedby four to six question items evaluated on a seven-point Likert scale (1 strongly disagree,

QMI andoperational

performance

525

4 neither agree nor disagree, and 7 strongly agree). The individual question items areshown in the Appendix. Finally, 13 operational measures of manufacturing plants arejudged by the plant manager. Each plant manager is asked to indicate his/her opinion abouthow the plant compares to its competitors in the same industry on a global basis on afive-point Likert scale (1 poor or low end of the industry, 3 average, and 5 superior ortop of the industry).

Measurement analysisThe first step of analytical process is the analysis of reliability and validity of tenmeasurement scales and two super-scales. In this study, Cronbachs alpha coefficient iscalculated to evaluate the reliability of each measurement scale. Table III shows thealpha values for all of ten scales exceeded the minimum acceptable alpha value of 0.60for pooled sample and country-wise. Most of the scales have the alpha value above 0.75indicating that the scales were internally consistent:

. Content validity. An extensive review of literature and empirical studies isundertaken about quality management and organization performance to ensurecontent validity.

USA Japan Italy Sweden Austria Korea Total

Electrical and electronic 9 10 10 7 10 10 56Machinery 11 12 10 10 7 10 60Automobile 9 13 7 7 4 11 51Total 29 35 27 24 21 31 167Plant characteristicsAverage market share (%) 25.50 33.05 23.38 34.80 20.00 31.54Average sale ($000) 284,181 1,118,492 71,209 584,371 64,470 2,266,962Average of number ofemployee (salaried person) 153 474 296 348 122 2,556

Table I.Demographic ofsurvey respondent

Positions to answer questionnaireMeasurement scales PD HR DL IM PE QM SP PS PM

Feedback 6 1 1Shop-floor contact 1 4 1Supervisory interaction facilitation 6 4 1Employee suggestions 6 4 1Multi-functional employees 1 4 1Small group problem solving 6 1 4Coordination of decision making 6 4 1Cross-functional product design 1 1 4Communication with suppliers 6 1 1 4 1Communication with customers 6 1 1 4Operational performance 1

Notes: DL, Direct labor; PM, plant manager; PD, member of new product development team; HR,human resource manager; QM, quality manager; PS, plant superintendent; IM, inventory manager; SP,supervisor; PE, process engineer

Table II.Survey respondents

MRR34,5

526

Des

crip

tiv

eC

ron

bac

hs

alp

ha

Mea

sure

men

tit

ems

Mea

nS

DU

SA

Jap

anS

wed

enK

orea

Ital

yA

ust

ria

Poo

led

sam

ple

Sca

les

Fee

db

ack

(FD

B)

4.97

20.

812

0.76

0.78

0.80

0.74

0.88

0.80

0.79

Sh

op-fl

oor

con

tact

(SF

C)

5.24

50.

632

0.60

0.64

0.69

0.70

0.60

0.72

0.64

Su

per

vis

ory

inte

ract

ion

faci

lita

tion

(SIF

)5.

131

0.62

40.

760.

750.

800.

710.

820.

830.

79E

mp

loy

ees

sug

ges

tion

s(E

SG

)5.

149

0.60

40.

850.

810.

800.

690.

860.

860.

82S

mal

lg

rou

pp

rob

lem

solv

ing

(SP

S)

5.09

70.

601

0.85

0.81

0.80

0.69

0.86

0.86

0.83

Cro

ss-f

un

ctio

nal

trai

nin

g(C

FT

)5.

297

0.62

30.

840.

820.

780.

770.

760.

760.

79C

oord

inat

ion

ofd

ecis

ion

mak

ing

(CD

M)

5.22

60.

648

0.73

0.78

0.75

0.69

0.77

0.80

0.74

Cro

ss-f

un

ctio

nal

pro

du

ctd

esig

n(C

PD

)4.

817

0.72

40.

790.

700.

700.

710.

750.

810.

74C

omm

un

icat

ion

wit

hsu

pp

lier

(CS

P)

5.46

30.

478

0.65

0.68

0.69

0.79

0.82

0.86

0.71

Com

mu

nic

atio

nw

ith

cust

omer

(CC

S)

5.26

00.

535

0.66

0.68

0.78

0.77

0.75

0.67

0.69

Supe

rsc

ale

sS

hop

-floo

rq

ual

ity

info

rmat

ion

(SQ

MI)

5.15

10.

521

0.88

0.88

0.84

0.91

0.83

0.90

0.88

Cro

ss-f

un

ctio

nal

qu

alit

yin

form

atio

n(C

QM

I)5.

192

0.45

10.

640.

840.

620.

890.

690.

810.

74

Table III.Measurement test

QMI andoperational

performance

527

. Construct validity. Construct validity is conducted to ensure that all questionitems in a scale all measure the same construct. Within-scale factor analysis istested with the three criteria: uni-dimensionality, a minimum eigenvalue of 1, anditem factor loadings in excess of 0.40. The results of measurement testing for thepooled sample and country-wise show that all scales had well construct validity.The eigenvalue of the first factor for each scale is more than two. Factor loadingfor each items are more than 0.40, mostly range between 0.70 and 0.90 for thepooled sample as shown in the Appendix.

Hypothesis testingThis section starts with the analysis of country effect existed in QMI practices. One-wayANOVA is used to identify the similarities and differences in QMI practices across thecountries. The last two columns of Table IV show the values of the F-statistic and theirsignificant levels. If we set the set significant level at 5 percent, the ANOVA test resultssuggest that all of QMI practices are significantly different across the countries exceptemployee suggestions. Next, Tukey pairwise comparison tests of mean differences areconducted to identify how QMI practice differed between each pair of countries.We observe that the largest differences exist in such practices as supervisory interactionfacilitation, cross-functional product design, coordination of decision making,communication with suppliers, and communication with customers. The Japanese andUS plants are quite similar in almost of the practices except multi-functional employeesand communication with customers. In addition, QMI practices are evaluated in similarway in two Asian countries. In general, shop-floor QMI practices are lowest in Italy andhighest in Austria and Korea, while cross-functional QMI practices are lowest in Japan andhighest in Austria and the USA. In the USA, an Italian plants, the focus of cross-functionalQMI practices are appeared higher than shop-floor QMI while both of them are similar inJapanese and Korean plants. It is found that the most focused practices (top practices) ofQMI practices are different between the countries: communication with customer (in theUSA), multi-functional employees (in Sweden), coordination of decision making(in Austria), shop-floor contact (in Korea), and employee suggestions (in Japan). Insummary, the results of ANOVA test suggest that QMI practices vary widely by country.Each country evaluated the degree of implementation of QMI practices in different ways.National culture, geographical specifics, and competition environment and other factorsmay account for the differences we observed among QMI practices adopted in differentcountries. As the result, we would like to accept H1 and state that there is significantdifference in QMI practices across the countries.

Primary relationship between ten QMI practices and 13 operational performancemeasures is identified by the binary correlation analysis that conducted in pooled andcountries-wise samples as show in Table V. It has 130 cells, each corresponding to apair of one QMI practices and one operational indicator. The cells include initials ofthe countries for which significant correlations are found between the practices and theperformance indicators. We observe that linkage between QMI practices andperformance in Japanese plants exhibits closer than the one in other countries if weset the significant level at 0.5 percent as suggested in literature. Out of 130, the number ofpair of significant correlation in Japanese case is 43. This number is 14, 13, 10, 8, 7, and 82in Korea, Austria, Italy, Korea, US, Sweden, and pooled samples, respectively.It is observed that QMI practices are highly associated with on-time delivery,

MRR34,5

528

QM

Ip

ract

ices

US

AJa

pan

Sw

eden

Kor

eaIt

aly

Au

stri

aP

air-

wis

ed

iffe

ren

ces

FS

ig.

Fee

db

ack

5.04

74.

843

4.98

05.

139

4.60

45.

262

(Iv

sA

)2.

568

0.02

0S

hop

-floo

rco

nta

ct5.

144

5.08

75.

372

5.47

14.

982

5.50

7(K

vs

I)an

d(I

vs

A)

4.97

60.

000

Su

per

vis

ory

inte

ract

ion

faci

lita

tion

5.29

25.

140

5.50

35.

163

4.47

65.

275

(Uv

sI)

,(J

vs

I),

(Sv

sI)

,(K

vs

I),

and

(Av

sI)

7.94

60.

000

Em

plo

yee

sug

ges

tion

s5.

091

5.20

45.

242

5.28

64.

808

5.26

11.

874

0.08

7S

mal

lg

rou

pp

rob

lem

solv

ing

5.32

74.

920

5.21

55.

016

4.86

75.

352

(Uv

sI)

3.00

80.

008

Mu

lti-

fun

ctio

nal

emp

loy

ees

5.45

74.

957

5.63

95.

188

5.16

85.

578

(Jv

sI)

,(J

vs

S),

(Jv

sA

),an

d(S

vs

K)

5.53

10.

000

Coo

rdin

atio

nof

dec

isio

nm

akin

g5.

103

5.17

75.

547

5.08

94.

912

5.71

0(U

vs

A),

(Jv

sA

),(S

vs

K),

(Sv

sI)

,an

d(I

vs

A)

5.72

10.

000

Cro

ss-f

un

ctio

nal

pro

du

ctd

esig

n4.

366

5.01

55.

146

4.87

14.

533

5.01

6(U

vs

J),

(Uv

sS

),(U

vs

K),

(Uv

sA

),an

d(S

vs

I)5.

630

0.00

0C

omm

un

icat

ion

wit

hsu

pp

lier

s5.

190

4.69

85.

394

4.82

75.

125

5.17

0(U

and

J),(

Uan

dK

),(J

and

S),

(Jan

dI)

,an

d(J

and

A)

7.87

00.

000

Com

mu

nic

atio

nw

ith

cust

omer

s5.

561

4.89

15.

323

5.19

15.

157

5.62

4(U

and

J),(

Uan

dK

),(U

and

I),(

Jan

dS

),(I

and

A),

and

(Kan

dA

)8.

304

0.00

0

Table IV.Quality information

practices across countries

QMI andoperational

performance

529

QM

Ip

ract

ices

UC

MC

PS

OT

DF

DL

FP

MF

CV

ITO

CT

MN

DT

PC

PO

PL

PIN

CS

S

Fee

db

ack

P,

JP

P,

A,

IP

P,

JP

,J

P,

J,K

Sh

op-fl

oor

con

tact

PP

P,

UP

P,

JP

KP

A,

K,

UP

,U

P,

JS

up

erv

isor

yin

tera

ctio

nfa

cili

tati

onJ

PP

,A

P,

JP

,J

JP

P,

JP

,J

Em

plo

yee

sug

ges

tion

sP

,J

PP

,A

,I

P,

JP

,J

PP

P,

JP

PP

,S

P,

J,S

Sm

all

gro

up

pro

ble

mso

lvin

gP

,J

PP

,A

,I

PP

PP

P,

KP

,K

PP

Mu

lti-

fun

ctio

nal

emp

loy

ees

PP

P,

A,

KP

PP

,J,

KP

P,

KP

P,

JC

oord

inat

ion

ofd

ecis

ion

mak

ing

P,

UP

,A

,J,

UP

P,

J,K

P,

JP

P,

A,

K,

UP

P,

JC

ross

-fu

nct

ion

alp

rod

uct

des

ign

P,

SP

,I,

SP

,I

KP

,J,

K,

SI,

JJ,

SP

,I,

J,K

IP

,I,

J,K

P,

JJ

Com

mu

nic

atio

nw

ith

sup

pli

ers

SP

,U

P,

A,

I,J

JJ

A,

J,K

JA

,J

A,

JP

,J

Com

mu

nic

atio

nw

ith

cust

omer

sP

,J,

UP

PP

,J

JP

,A

,J

P,

JA

,J

P,

J

Notes:

U,

US

A;

J,Ja

pan

;S

,S

wed

en;

K,

Kor

ea;

I,It

aly

;A

,A

ust

ria;

P,

poo

led

sam

ple

Table V.Correlation betweenquality informationpractices and operationalperformance indicators

MRR34,5

530

flexibility to change volume, new product develop lead time, and on-time new productlaunch. In case of Japanese and pooled samples, QMI practices significantly correlatewith every performance indicators. The number of performance indicators significantlycorrelate with QMI practices is 11, 6, 5, 5, 4, and 4, in Japanese, Sweden, Austrian, Korean,US, and Italian samples, respectively.

To formally test the impact of shop-floor and cross-functional QMI practices onoperational performance, further regression analysis was conducted. Regression model isformulated using SQMI and CQMI as two dependent variables along with seven dummyvariables. Table VI presents the regression on overall operational performance (deliveredby summarizing 13 individual operational performances) using pooled sample of 167 cases.If we consider the value of adjusted R 2 as the indicator for explanation power of the model,regression result indicates that both SQMI and CQMI can explain 13.5 percent variabilityof operation performance. Between two independent variables, CQMI is found assignificant predictor for operational performance. Although correlation analysis suggeststhe country-difference on impact of individual QMI practices on individual operationalperformances, regression analysis rather indicates the non-significant difference in thedeterminants of operational performance between Japan and other countries.

To confirm this finding with more formal statistical evidence, additional regressionanalysis is required to check whether the coefficients in a particular regression model arethe same for the samples of different countries, after dividing the pooled sample into sixsub-samples representing each country. What is required is to compare an estimatedregression model including two measurement scales as independent variables for the

Coefficients and significant level

(Constant) 21.306 (0.128)USA 2.431 (0.026)SWE 2.195 (0.039)KOR 1.493 (0.112)ITA 2.029 (0.044)AUT 1.847 (0.050)MAC 0.104 (0.147)EE 20.006 (0.477)SQMI 0.165 (0.356)CQMI 0.694 (0.036)US*CQMI 22.453 (0.130)US*SQMI 0.009 (0.498)SWE*SQMI 21.221 (0.264)SWE*CQMI 21.357 (0.253)KOR*CQMI 22.300 (0.174)KOR*SQMI 0.709 (0.394)ITA*SQMI 0.366 (0.420)ITA*CQMI 22.497 (0.106)AUT*CQMI 22.332 (0.137)AUT*SQMI 0.291 (0.441)R 2 0.244Adjusted R 2 0.135F and p 2.242 (0.002)

Note: Model using dummy country and industry variables

Table VI.Regression analysis

on relationship betweenquality information

practices and operationalperformance

QMI andoperational

performance

531

pooled sample with the corresponding model applied for six sub-samples. In estimatingthe regression models for the sub-samples, no restrictions are imposed on the values ofregression coefficients so that every coefficient can take different values for differentcountries. We can evaluate the improvement in explanatory power by dividing thepooled sample into six sub-samples and enabling regression coefficients to take differentvalues by an F-test (Chow, 1960):

F2 statistic RSSR2 SSSRi=kSSSRi=n2 i*k ;

where:

RSSRis the sum of squared residuals from a linear regression of the pooled sample.

SSRi is the sum of squared residuals from a linear regression of sub-sample i.

i is the number of subgroup.

k is number of independent variable.

n is number of total observations.

Table VII shows regression analysis on relationship between QMI practices andoperational performance taking on pooled and country-wise sample using SQMI andCQMI as two independent variables. We obtain value of F-statistic is 3.654 with p-valueis 0.008. If we setting significant level at 5 percent, the results of Chow test indicate thedifference on determinant of operational performance across the countries. In summary,we can accept H2 and state that QMI practices significantly impact operationalperformance. In addition, statistical results reveal that this impact widely varies acrossthe countries and the linkage between QMI and performance in Japanese plants appearscloser than others.

DiscussionsResults of the present study show that the differences on QMI practices existed inmanufacturing plants operating in different countries. The degree of implementation ofeach QMI practices and their linkage with specific operational performance indicatorsappear differently across the countries. Although this has been previously implied in thequality management literature, comparison of a comprehensive list of QMI practicesamong countries was lacking. We obtained the mixed results when the QMI practiceswere compared across six countries.

First, we find that all the QMI practices are significantly different across thecountries (except employee suggestion). The USA and three other European countriesplace their higher focus on cross-functional QMI practices rather than on shop-floorQMI practices. Plants in the USA, Austria, and Sweden show their stronger emphasison every QMI practice than other plants in Italy and Japan.

Second, we find the linkage between QMI practices with different dimensions ofoperational performance rather than with quality performance only. For example,statistical analysis reveals that QMI practices closely linked with time-basedperformance indicators of manufacturing plants, for example: on-time delivery(in Austria, the USA, and Italy), on-time new product launch (in Korea), and new productdevelopment lead time (in Japan). Cross-functional product design is found as the most

MRR34,5

532

Poo

led

sam

ple

US

AS

outh

Kor

eaJa

pan

Sw

eden

Ital

yA

ust

ria

(Con

stan

t)1.

386

(0.0

02)

2.37

6(0

.050

)0.

945

(0.2

40)

21.

067

(0.1

88)

1.98

5(0

.074

)1.

485

(0.8

8)1.

528

(0.1

35)

SQ

MI

0.13

9(0

.120

)0.

137

(0.3

25)

0.31

8(0

.235

)0.

053

(0.4

30)

20.

194

(0.2

37)

0.22

7(0

.195

)0.

322

(0.2

17)

CQ

MI

0.25

1(0

.018

)0.

109

(0.3

62)

0.10

7(0

.403

)0.

551

(0.0

39)

0.40

9(0

.070

)0.

179

(0.2

49)

0.13

1(0

.374

)R

20.

136

0.05

30.

173

0.35

80.

105

0.13

90.

189

Ad

just

edR

20.

124

20.

030

0.09

40.

315

0.02

00.

067

0.08

1F

and

p11

.688

(0.0

00)

0.64

1(0

.267

)2.

192

(0.1

37)

8.34

8(0

.001

)1.

231

(0.1

56)

1.93

4(0

.084

)1.

750

(0.1

04)

Chow

test

Fan

dp:

3.65

4(0

.008

)

Table VII.Regression analysis

on relationship betweenQMI practices and

operational performancetaking on pooled andcountry-wise sample

QMI andoperational

performance

533

critical factor for these performances. In general, employee suggestions, coordination ofdecision making, and cross-functional product are found highly associated withoperational performance of plants in the six countries.

Third, the significant difference between countries in the linkage of individual QMIpractices on specific performance indicators is detected. We observed that theconnection between the QMI practices and high performance in Japanese plantsappears tight, comparing with other countries. Japanese plants with high performancehighly focus on shop-floor contact, small group problem solving, and feedback.

The findings on significant differences across the countries are consistent with theinstitutional theory when the institutions are taken to be the countries. National culture,geographical specifics, and competitive environment may account for the differencesthat we observed in communication and information sharing practices across thecountries. In addition, the finding of our study highlights the Japanese qualitymanagement. The prosperity and survival of Japanese manufacturers are archived bytheir Japanese way of management such as TQM, JIT production, TPM, concurrentengineering, and their ability to create horizontal linkage structure throughout thecommunication network. Those are the real strengths of Japanese manufacturers,besides of their technological advantages. The communication and action process isone of underlying forces that have made such practices as TQM and JIT so successful(Morita et al., 2001).

For the researchers and practitioners, this study provides the evidence on howperformance is associated with communication and information sharing in the plants.Managers who want to improve selected operational performance indicators can findsome valuable suggestions from the statistical analysis results. For example, in Japaneseplants, high performance in term of manufacturing cost and volume flexibility relates withthe implementation of such shop-floor QMI practices as feedback, shop-floor contact,supervisory interaction facilitation, and employee suggestions. Improvement of inventoryturnover and reduction of new product development lead time would be achieved byimplementation of such cross-functional QMI practices as cross-functional product design,and communication to suppliers and communication to customers. Because the quality,cost, delivery, and flexibility performances are closely correlated, benefits of QMIpractices sometimes have multiple effects on operational performance. Regressionanalysis on the pooled sample shows that cross-functional QMI is significant predictor foroperational performance. This suggests that the emphasis on communication andinformation crossing the borders of functions would explain the difference on competitiveposition of manufacturing plants

This study contributes to quality management literature as it refines ourunderstanding of the nature of relationship between QMI practices and plantperformance. Continuing to use HPM perspective to study QMI, we provide furtherinsight on the achievement of high performance through communication andinformation sharing. This study introduces a comprehensive research framework tostudy QMI and uses the latest database to test the hypotheses. Our findings are in linewith previous studies on QMI using HPM perspectives such as Forza and Salvador(2001), Schroeder and Flynn (2001) and Flynn and Saladin (2006). In addition, we findthat operational performance would be influenced by such QMI components asshop-floor contact or supervisory interaction facilitation, which have not been fullystudied in previous works.

MRR34,5

534

Limitation and future researchIt is important to view this research in the context of its limitations. Methodologically,this study is based on cross-sectional survey research data. It utilizes database gatheredfrom self-reported questionnaires, and individual bias in reporting may exist. Althoughwe address the issue of common method bias through the use of multiple respondents,the study heavily relies on the use of perceptual data. The other issue is sample size.Because time and resources constrains, the sample consist of only 167 plants belongingto three industries. These restrict the scope of the studies and utilization of some dataanalysis techniques. For example, the relative small sample size not allows the authorsto use path analysis technique to examine interrelations among specific QMI practicesand operational performance with industry and country effects.

Next is the issue relates with evaluation of operational performance. The HPM collectedboth objective and subjective data on operational performance of manufacturing plants inall of member countries. The objective measures of operational performance on quality,cost, and delivery have been collected such as percentage of scrap and rework,manufacturing cost, percentage of on-time delivery, etc. However, because ofindustrial difference; these objective data on performance cannot be fully used in thisstudy. Therefore, the subjective measures are used to evaluate operational performance inthis study. Other studies in framework of HPM projects also encountered this issue(Flynn et al., 1995; Ahmad et al., 2003; Matsui and Sato, 2002; Phan and Matsui, 2009).

To overcome above-mentioned limitations, a future research should be conducted withlarger and comprehensive sample size. This will allow the researchers to usecomprehensive techniques for investigating relationship between management practicesand performance for specific countries or specific industries, such as path analysis orstructural equation modeling. Researchers should explore both objective measures andsubjective measures in their studies, particularly when focusing on a specific industry.

ConclusionsIn the previous sections, we presented the results of the empirical study on therelationship between QMI and operational performance in manufacturing plants.A simple analytical framework and two hypotheses were proposed. Then, based on theliterature, we introduced ten measurement scales and two super scales and all of thosemeasurement scales are satisfactory in terms of reliability and validity for the dataset of167 manufacturing plants in six countries. Using these scales, we examined the countryeffect on QMI to explore the critical success factors of operational performance. Theresults indicate the similarities and differences of the implementation of QMI and itsimpact on operational performance across countries. This study suggests thatmanufacturing plants should develop the process and network of shop-floor andcross-functional communication and information sharing which is an underlying forcethat have made manufacturing management practices became successful and contributeto their competitive performance. It also highlights the unique and distinguishedposition of Japanese manufacturers in the impact of QMI on operational performance.

References

Ahmad, S., Schroeder, R.G. and Sinha, K.K. (2003), The role of infrastructure practices in theeffectiveness of JIT practices: implications for plant competitiveness, Journal ofEngineering and Technology Management, Vol. 20 No. 3, pp. 161-91.

QMI andoperational

performance

535

Carr, A. and Kaynak, H. (2007), Communication methods, information sharing, supplierdevelopment and performance: an empirical study of their relationships, InternationalJournal of Operations & Production Management, Vol. 27 No. 4, pp. 346-70.

Chow, G.C. (1960), Tests of equality between sets of coefficients in two linear regressions,Econometric, Vol. 28 No. 3, pp. 591-605.

Flynn, B.B. (1992), Managing for quality in the US and in Japan, Interface, Vol. 22 No. 5,pp. 69-80.

Flynn, B.B. and Flynn, E.J. (1999), Information-processing alternatives for coping withmanufacturing environment complexity, Decision Sciences, Vol. 30 No. 2, pp. 1021-52.

Flynn, B.B. and Saladin, B. (2006), Relevance of Baldrige constructs in an international context:a study of national culture, Journal of Operations Management, Vol. 24 No. 2, pp. 583-603.

Flynn, B.B., Schroeder, R.G. and Sakakibara, S. (1994), A framework for quality managementresearch and an associated instrument, Journal of Operation Management, Vol. 11 No. 4,pp. 336-9.

Flynn, B.B., Schroeder, R.G. and Sakakibara, S. (1995), The impact of quality managementpractices on performance and competitive advantage, Decision Sciences, Vol. 26 No. 5,pp. 659-91.

Form, W. (1979), Comparative industrial sociology and the convergence hypotheses, AnnualReview of Sociology, Vol. 5, pp. 1-25.

Forza, C. (1995), The impact of information systems on quality performance: an empirical study,International Journal of Operations & Production Management, Vol. 15 No. 6, pp. 69-83.

Forza, C. and Flippini, R. (1998), TQM impact on quality conformance and customersatisfaction: a causal model, International Journal of Production Economics, Vol. 55 No. 1,pp. 1-20.

Forza, C. and Salvador, F. (2001), Information flows for high-performance manufacturing,International Journal of Production Economics, Vol. 70 No. 1, pp. 21-36.

Garvin, D.A. (1986), Quality problem, policies, and attitudes in the United States and Japan:an explore study, The Academy of Management Journal, Vol. 29 No. 4, pp. 653-73.

Juran, J.M. and Gryna, F.M. (1980), Quality Planning and Analysis, McGraw-Hill, New York, NY.

Kaynak, H. (2003), The relationship between total quality management practices and theireffects on firm performance, Journal of Operations Management, Vol. 21 No. 4, pp. 405-35.

Madu, C.N., Kuel, C. and Lin, C. (1995), A comparative analysis of quality practices inmanufacturing firms in the US and Taiwan, Decision Sciences, Vol. 26 No. 5, pp. 621-36.

Matsui, Y. and Sato, O. (2002), An international comparison study on benefits of productioninformation systems, International Journal of Operation and Quantitative Management,Vol. 8 No. 3, pp. 191-214.

Morita, M., Sakikabara, S., Matsui, Y. and Sato, O. (2001), Japanese manufacturing organization:are they still competitive, in Schroeder, R.G. and Flynn, B.B. (Eds), High PerformanceManufacturing: Global Perspectives, Wiley, New York, NY, pp. 199-232.

Parast, M.M., Adam, S.G., Jones, E.C., Rao, S.S. and Raghu-Nathan, T.S. (2006), Comparingquality management practices between the United States and Mexico, QualityManagement Journal, Vol. 13 No. 4, pp. 36-49.

Phan, C.A. and Matsui, Y. (2009), Effect of quality management on competitive performance inmanufacturing companies: international perspective, Journal of Productivity and QualityManagement, Vol. 4 No. 2, pp. 153-77.

MRR34,5

536

Rao, S.S., Ragu-Nathan, T.S. and Solis, L.E. (1997), Does ISO have an effect on qualitymanagement practice? An international empirical study, Total Quality Management,Vol. 8 No. 6, pp. 335-46.

Rungtusanatham, M., Forza, C., Koka, B.R., Salvador, F. and Nie, W. (2005), TQM acrossmultiple countries: convergence hypothesis versus national specificity arguments,Journal of Operations Management, Vol. 23 No. 1, pp. 43-63.

Schniederjans, M.J., Parast, M.M., Nabavi, M., Rao, S.S. and Raghu-Nathan, T.S. (2006),Comparative analysis of Malcolm Baldrige National Quality Award criteria: an empiricalstudy of India, Mexico and the United States, Quality Management Journal, Vol. 13 No. 4,pp. 7-21.

Schroeder, R.G. and Flynn, B.B. (2001), High Performance Manufacturing: Global Perspectives,Wiley, New York, NY.

Sila, I. and Ebrahimpour, M. (2003), Examination and comparison of critical factors of totalquality management (TQM) across countries, International Journal of ProductionResearch, Vol. 41 No. 2, pp. 235-68.

Snell, R.S. and Hui, S.S.K. (2000), Towards the Hong Kong learning organization: an exploratorycase study, Journal of Applied Management Studies, Vol. 9 No. 2, pp. 150-75.

Wacker, J.F. and Sprague, L.G. (1998), Forecasting accuracy: comparing the relativeeffectiveness of practices between seven developing countries, Journal of OperationsManagement, Vol. 16 Nos 2/3, pp. 271-90.

Zu, X., Fredendall, L. and Douglas, T.J. (2008), The evolving theory of quality management:the role of Six Sigma, Journal of Operations Management, Vol. 26 No. 5, pp. 630-50.

Further reading

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

Benson, P.G., Saraph, J.V. and Schroeder, R.G. (1991), The effects of organizational context on qualitymanagement: an empirical investigation, Management Science, Vol. 37 No. 9, pp. 1107-24.

Das, A., Handfield, R.B., Calantone, R.J. and Ghosh, S. (2000), A contingent view of qualitymanagement the impact of international competition on quality, Decision Sciences,Vol. 31 No. 3, pp. 649-90.

Flynn, B.B., Schroeder, R.G., Flynn, E.J., Sakakibara, S. and Bates, K.A. (1997), World-classmanufacturing project: overview and selected results, International Journal of Operations& Production Management, Vol. 17 No. 7, pp. 671-85.

Matsui, Y. (2002), An empirical analysis of quality management in Japanese manufacturingcompanies, Proceedings of the Seventh Asia-Pacific Decision Sciences InstituteConference, National Institute of Development Administration, Bangkok, Thailand.

Molina, L.M., Lorens-Montes, J. and Ruiz-Moreno, J. (2007), Relationship between qualitymanagement practices and knowledge transfer, Journal of Operations Management,Vol. 25 No. 3, pp. 682-701.

Nair, A. (2005), Meta-analysis of the relationship between quality management practices andfirm performance implications for quality management theory development, Journal ofOperations Management, Vol. 24 No. 6, pp. 948-75.

Saraph, J.V., Benson, G.P. and Schroeder, R.G. (1989), An instrument for measuring the criticalfactors of quality management, Decision Sciences, Vol. 20 No. 4, pp. 810-29.

Schonberger, R.J. (1986), World Class Manufacturing: The Lessons of Simplicity Applied,The Free Press, New York, NY.

QMI andoperational

performance

537

AppendixThe values that follow the names of scales and super-scales report the results of factor analysis(the eigenvalue and percentage of variance of the first factor) taking on the pooled sample toevaluate the validity of these scales.

The values follow each question item show factor loading for this question item:

I. Shop-floor quality information practices (3.83 and 64 percent)

I.1 Feedback (2.792 and 56 percent):

1. Charts showing defect rates are posted on the shop floor (0.76).

2. Charts showing schedule compliance are posted on the shop floor (0.78).

3. Charts plotting the frequency of machine breakdowns are posted on the shop floor(0.68).

4. Information on quality performance is readily available to employees (0.78).

5. Information on productivity is readily available to employees (0.74).

I.2 Shop-floor contact (2.20 and 44 percent):

1. Managers in this plant believe in using a lot of face-to-face contact with shop-flooremployees (0.70).

2. Engineers are located near the shop floor, to provide quick assistance whenproduction stops (0.73).

3. Our plant manager is seen on the shop floor almost every day (0.75).

4. Managers are readily available on the shop floor when they are needed (0.79).

5. Manufacturing engineers are often on the shop floor to assist with productionproblems (0.70).

I.3 Supervisory interaction facilitation (2.57 and 64 percent):

1. Our supervisors encourage the people who work for them to work as a team (0.77).

2. Our supervisors encourage the people who work for them to exchange opinions andideas (0.78).

3. Our supervisors frequently hold group meetings where the people who work for themcan really discuss things together (0.79).

4. Our supervisors rarely encourage us to get together to solve problems (0.76).

I.4 Employee suggestions (3.03 and 61 percent):

1. Management takes all product and process improvement suggestions seriously(0.80).

2. We are encouraged to make suggestions for improving performance at this plant(0.78).

3. Management tells us why our suggestions are implemented or not used (0.81).

4. Many useful suggestions are implemented at this plant (0.70).

5. My suggestions are never taken seriously around here (removed).

I.5 Small group problem solving (2.64 and 53 percent):

1. During problem solving sessions, we make an effort to get all team membersopinions and ideas before making a decision (0.80).

2. Our plant forms teams to solve problems (0.78).

3. In the past three years, many problems have been solved through small groupsessions (0.87).

MRR34,5

538

4. Problem solving teams have helped improve manufacturing processes at this plant(0.76).

5. Employee teams are encouraged to try to solve their own problems, as much aspossible (0.78).

6. We do not use problem-solving teams much, in this plant (removed).

I.6 Multi-functional employees (3.026 and 61 percent):

1. Our employees receive training to perform multiple tasks (0.77).

2. Employees at this plant learn how to perform a variety of tasks (0.76).

3. The longer an employee has been at this plant, the more tasks they learn to perform(0.77).

4. Employees are cross-trained at this plant, so that they can fill in for others, if necessary(0.78).

5. At this plant, each employee only learns how to do one job (0.75).

II. Cross-functional quality information practices (2.320 and 58 percent)

II.1 Coordination of decision making (2.28 and 57 percent)

1. Generally speaking, everyone in the plant works well together (0.79)

2. Departments in the plant communicate frequently with each other (0.79)

3. Departments within the plant seem to be in constant conflict (0.70)

4. Management works together well on all important decisions (0.75)

II.2 Cross-functional product design (2.28 and 56 percent)

1. Direct labor employees are involved to a great extent before introducing new productsor making product changes (0.76)

2. Manufacturing engineers are involved to a great extent before the introduction ofnew products (0.77)

3. There is little involvement of manufacturing and quality people in the early designor products, before they reach the plant (0.78)

4. We work in teams, with members from a variety of areas (marketing, manufacturing,etc.) to introduce new products (0.77)

II.3 Communication with customer (2.11 and 53 percent)

1. We frequently are in close contact with our customers (0.70)

2. Our customers give us feedback on our quality and delivery performance (0.65)

3. We strive to be highly responsive to our customers needs (0.76)

4. We regularly survey our customers needs (0.80)

II.4 Communication with supplier (2.18 and 54 percent)

1. We are comfortable sharing problems with our suppliers (0.76)

2. In dealing with our suppliers, we are willing to change assumptions, in order to findmore effective solutions (0.77)

3. We believe that cooperating with our suppliers is beneficial (0.76)

4. We emphasize openness of communications in collaborating with our suppliers(0.72)

5. We maintain close communications with suppliers about quality considerations anddesign changes (removed)

QMI andoperational

performance

539

About the authorsPhan Chi Anh is a Lecturer in the Faculty of Business Administration, University of Economicsand Business Vietnam National University, Hanoi. His research topics relate to qualitymanagement, lean production, and high-performance manufacturing. His articles can be found inInternational Journal of Productivity and Quality, Operation Research Review, and InternationalJournal of Production Economics. Phan Chi Anh is the corresponding author and can becontacted at: anhpc@yahoo.com

Yoshiki Matsui is a Professor of Operations Management at the International GraduateSchool of Social Sciences and Faculty of Business Administration, Yokohama NationalUniversity in Japan. His research and teaching topics cover issues of manufacturingmanagement, supply chain management, quality management, JIT production, and new productdevelopment. He has published papers in International Journal of Production Economics,International Journal of Operations and Quantitative Management, International Journal ofGlobal Logistics and Supply Chain Management, and so on.

MRR34,5

540

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints