-
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
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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).
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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
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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
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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.
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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
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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
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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
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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.
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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).
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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: [email protected]
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.
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