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A systems research on qualitymanagement under the MBNQAframeworkHongyi Sun aa Department of Systems Engineering and EngineeringManagement, City University of Hong Kong, 83 Tat Chee Avenue,Kowloon, Hong KongPublished online: 23 Nov 2011.
To cite this article: Hongyi Sun (2011): A systems research on quality management under theMBNQA framework, Total Quality Management & Business Excellence, 22:11, 1195-1211
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A systems research on quality management under the MBNQAframework
Hongyi Sun∗
Department of Systems Engineering and Engineering Management, City University ofHong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
Most previous research in the quality management field investigates whether qualitymanagement practices contribute directly to business results. This paper reports theresults of structural analysis of the systematic relationships among qualitymanagement practices and business performance. The study’s aim is to determinewhether companies have reached the requisite level of synthesis, alignment, andintegration in quality management suggested by the Malcolm Baldrige NationalQuality Award (MBNQA) model. A conceptual model is developed from previousstudies based on the MBNQA framework guidelines. Data from 180 Norwegianmanufacturing companies are analysed using structural equation modelling. Theresults show that the sampled companies have not reached the level of synthesis,alignment, and integration suggested by the MBNQA framework. Leadership inhuman resources (HRS) is perhaps the major area for improvement identified in thesampled companies. Both previous studies and the data presented in this papersuggest that the positions of HRS development and customer focus in the MBNQAmodel should be switched, which suggests the potential for future improvements tothe model’s framework.
Keywords: quality management; structural analysis; structural equation modelling(SEM); MBNQA model; Norway
1. Introduction
Quality remains a fundamental criterion (or order qualifier) of competition (Hill, 1995)
regardless of the concepts currently in fashion. Recent product recalls worldwide are
further reminders of the central role that quality plays in maintaining competitiveness.
Quality improvement measures not only improve product quality, but can also enhance
productivity (Bailey, 1998), efficiency (Olderding, 1998), and delivery (Zurier, 1989).
Quality management will thus continue to be a significant issue for the foreseeable
future. For companies that have implemented such quality standards as Six Sigma or
ISO 9000, the basic principles and methods associated with total quality management
(TQM) remain useful in building a quality-oriented organisation (Chan & Sun, 2004).
Researchers have also sought to integrate TQM and Six Sigma (Revere & Black, 2003),
as TQM facilitates implementation of Six Sigma, and Six Sigma can take TQM to the
next level. McAdam and Henderson (2004) concluded that TQM will continue to play a
central role in organisational development and remain a challenging research area for aca-
demics and practitioners. Numerous studies have employed linear models to investigate
ISSN 1478-3363 print/ISSN 1478-3371 online
# 2011 Taylor & Francis
http://dx.doi.org/10.1080/14783363.2011.624776
http://www.tandfonline.com
∗Email: [email protected]
Total Quality Management
Vol. 22, No. 11, November 2011, 1195–1211
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TQM in terms of its contribution of quality practices. However, systematic studies remain
rare, as there is no well-accepted conceptual model that links all quality practices.
The Malcolm Baldrige National Quality Award (MBNQA) framework has evolved
from a means of recognising and promoting exemplary quality management practices to
a comprehensive framework for world-class performance that is widely used as a model
for quality improvement in the USA and in other parts of the world (Flynn & Saladin,
2001). Jacob, Madu, and Tang (2004) demonstrated that MBNQA recipients are superior
financial performers and are highly valued by investors relative to similarly sized firms and
industry benchmarks, noting that the award-winning firms stand out as performance
leaders in their industries. However, little research has investigated whether companies
follow the systematic principles proposed in the quality award framework. This paper
reports the results of a systematic evaluation of the quality management practices of
180 Norwegian manufacturing companies based on the MBNQA model and the inter-
relationships among the model’s seven categories.
2. Literature review
2.1 The MBNQA framework as a research model
Definitions of quality and TQM vary from researcher to researcher, whereas quality award
models provide a general framework for quality management practice. These models have
become popular quality benchmarking frameworks among manufacturing and service
firms (Schmenner, 1995). They not only operationalise quality in managerial practice,
but they also provide a framework for research (Evans, 1997). Previous research (Pannir-
selvam, Siferd, & Ruch, 1998) has demonstrated the logic of the MBNQA framework to be
consistent with the principles of TQM, as proposed by several quality gurus (Crosby,
1979; Ishikawa, 1985; Deming, 1986; Garvin, 1988; Juran, 1989; Feigenbaum, 1991).
The MBNQA framework, which is illustrated in Figure 1, comprises seven key dimen-
sions or categories that explain the processes, procedures, and outcomes associated with a
quality-centred organisation. These model dimensions are (1) leadership, (2) strategic
planning, (3) customer and market focus, (4) measurement, analysis, and knowledge man-
agement, (5) human resource (HRS) focus, (6) process management, and (7) business
results. Although there has been little alteration to the main contents of the MBNQA fra-
mework, several changes were made to the model between the first version issued in 1988
and the latest in 2004. These changes are particularly obvious with regard to the inter-
relationships among the seven categories.
According to the guidelines published by the National Institute of Standards and Tech-
nology (NIST, 2010, p. 55), the MBNQA framework criteria provide a systems perspec-
tive in managing organisations to achieve performance excellence:
Successful management of overall performance requires organization-specific synthesis,alignment, and integration. Synthesis means looking at your organization as a whole andbuilds upon key business requirements, including your strategic objectives and actionplans. Alignment means using the key linkages among requirements given in the BaldrigeCategories to ensure consistency of plans, processes, measures, and actions. Integrationmeans the individual components of your performance management system operate in afully interconnected manner.
The systems perspective is emphasised in four places in the MBNQA manual (NIST,
2010, pp. 27, 49, 54–55). To reflect the systematic and synthetic nature of the model, the
MBNQA framework also proposes two triads. The leadership triad, which combines the
three categories of leadership, strategic planning, and customer and market focus,
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emphasises the importance of a leadership focus to strategy and customers. The results
triad, which incorporates HRS focus, process management, and business results, empha-
sises an organisation’s employees and the key processes that accomplish the organisation’s
work and lead to business results. Although it provides a practical framework for quality
management, the MBNQA framework does not yet constitute an academic conceptual
model, as its overall integration and linkages are not well described. In addition, as pre-
viously noted, several changes have been made to the model during the course of its devel-
opment. As a result, there are differences among previous studies based on the model, the
consequences of which are discussed in deriving a reasonable conceptual model in the next
section.
2.2 Previous research based on the MBNQA model
2.2.1 Validity and reliability testing of components of the MBNQA model
Research on the MBNQA model falls primarily into four categories. The first type of
research in this arena concerns the content validity of the model’s seven dimensions or cat-
egories. Much of this research supports the content validity and reliability of the model
components (Saraph, Benson, & Schoeder, 1989; Flynn, Schroeder, & Sakakibala,
1994; Pannirselvam et al., 1998). The results have been widely accepted, and an increasing
number of researchers are now using the framework. It has been common practice to
conduct construct validity and reliability tests with respect to the quality categories.
These pioneering studies have paved the way for more scientific and deeper research on
quality and operations management.
2.2.2 Direct practice–performance relationships based on the MBNQA components
The aim of the second body of research is to explore the relationship between quality prac-
tice and business performance based on the MBNQA components (Samson & Terziovski,
1999). Although the model’s components have passed the aforementioned validity and
reliability tests, this research does not aim to test the validity of the MBNQA paradigm,
but instead to test empirically the effectiveness of quality management practices. The
main methods employed are bivariate or multiple-element correlations. The advantage
Figure 1. The MBNQA model.
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of this line of research is that it links all possible direct contributing categories with
business performance. A disadvantage, however, is that the research framework is too sim-
plistic, as it features only a one-tier practice–performance relationship. If a category
cannot be related to business performance, then it is regarded as unimportant or too diffi-
cult to explain. As a result, the driver–system–performance relationship in the MBNQA
framework cannot be taken into account. A few recent publications treat TQM as a latent
variable and then test the relationships between it and performance (e.g. Prajogo & Sohal,
2006; Sila, 2007). Doing so, however, still fails to explain exactly which factors contribute
to performance.
As with early research on practice–performance relationships in other fields, such as
technology management, most research on quality practice–performance linkages
assumes that all practices may contribute directly to performance, whether separately or
in the form of a single latent variable. The resulting conceptual models are thus primarily
single layered and linear. However, the patterns of practice–performance linkages have
proved to be more complicated than expected, and there may be several layers connecting
practice to performance. As the latest MBNQA framework illustrates, the successful man-
agement of overall performance requires organisation-specific synthesis, alignment, and
integration. To address these issues, a structural, path analysis model has been employed
in research focusing on practice–performance relationships.
2.2.3 Validity of the MBNQA framework per se
The MBNQA offers a practical framework, not a theoretical model. Accordingly, the third
line of research relates to testing the structure of the MBNQA framework based on path
analysis and structural equation modelling (SEM). Although the content validity and
reliability of the model components have been tested (e.g. Saraph et al., 1989; Flynn
et al., 1994, cf. 2.2.1), there is surprisingly little empirical evidence of the validity of
the overall model in terms of the interrelationships among the seven categories. The
results of most model tests conducted between 1988 and 1992 have been accepted
(Wilson & Collier, 2000; Meyer & Collier, 2001), as the causal relationships in these
models are clearly defined. However, the causal relationships in the models developed
since 1997 have yet to be well defined. Evans (1997) provides a theoretical analysis of
the content validity of and relationships associated with the model, but conducts no empiri-
cal tests. Flynn and Saladin (2001) argue that it is important to understand the evolution of
the MBNQA framework and to investigate the validity of the 1997 framework, given the
major re-engineering of the criteria that took place in that year. Their study is perhaps the
only one to date that tests the relationships among the theoretical constructs that underlie
the criteria of the 1997 model. However, these researchers fail to conduct an SEM test,
thereby leaving the fitness of the overall model untested.
2.2.4 Plausible alternative empirical models based on the MBNQA framework
A few studies aim not to test the model, but instead to evaluate quality–performance
relationships according to the MBNQA framework (Winn & Cameron, 1998; Khanna,
Vrat, Shankar, & Sahay, 2002; Kaynak, 2003). Winn and Cameron (1998), for example,
adopt the 1992 model to evaluate data from universities, with their results showing that
this model fails to pass the test. Accordingly, the authors develop an alternative empirical
model, from which they draw implications. Khanna et al. (2002) and Kaynak (2003) also
develop alternative models. The one commonality in these three studies’ approach to
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data analysis is the use of SEM to test model fitness, although the methods they adopt to
specify the models differ. Winn and Cameron (1998) constructed their alternative model
based on the MBNQA framework using a method known as ‘theory trimming’ (Pedhazur,
1988), in which each dimension is linked to every other dimension, and the weakest relation-
ships are ‘trimmed’ from the model until it passes its fitness test. Khanna et al. (2002) estab-
lished their model on the basis of discussions with experts, practicing executives, and
academics, whereas Kaynak (2003) employed a traditional theoretical discussion method
to develop and test several hypotheses. Although not comparable due to different frame-
work, the same method was used in research based on the European Quality Award
Model (Dahlgaard & Nilsson, 2002; Tarı, Molina, & Castejon, 2006; Martensen, Dahlgaard,
Dahlgaard-Park, & Grønholdt, 2007;). These studies proposing alternative models do not
aim to test the validity of the original model, but rather to evaluate the current status of
quality management across companies. This type of research is similar to data mining,
and it leaves the existence of linkages unclear, as no theory or hypothesis was developed
in advance.
2.3 Conceptual model and hypotheses
The study reported in this paper belongs to the fourth category of research. The proposed
conceptual model is based on analysis of all available previous research, with the seven
conceptual models that have been identified as related to the framework summarised in
Table 1. Limited research addressing the integration of the framework is available.
Table 1 enumerates all possible paths that have been proposed and tested. However,
the test results are quite different, ranging from a maximum of five to a minimum of
one positive result. The differences in the models and test results may derive from different
versions of the framework being used and from differences in the data. No single item of
previous research listed in Table 1 is sufficient to cover all possible relationships. We
therefore rely on summarising the results of the previous studies proposing the seven
aforementioned conceptual models. The paths in which the hypotheses have been
proposed and tested positive in at least two previous studies were accepted for our new
conceptual model. Sixteen paths/hypotheses met this requirement. We investigated
these 16 paths/hypotheses according to the general principles concerning the direction
of models within the MBNQA framework.
When developing a conceptual model, it is necessary to identify the direction of each
relationship within it because all relationships should be causal in a path model. In all of
the previous research considered here, identification of the direction was based primarily
on the guidelines of the MBNQA model with regard to quality management logic. The
MBNQA framework offers the following information about relationships. First, the direc-
tion should follow the logic of quality management, namely, starting from practice and
ending in a business result, which is consistent with the approaches used in previous
research (Wilson & Collier, 2000; Meyer & Collier, 2001; Pannirselvam & Ferguson,
2001) and clearly specified in the model guidelines, particularly ‘all actions point
toward business results’. Therefore, HRS development and process management are
linked to business results with unidirectional arrow lines. Second, the arrow indicates
the central relationship between leadership and business results, which implies that the
causal relationships begin with leadership and end in business results. Third, the horizontal
arrow in the centre of the framework links the leadership triad to the results triad. Based on
the summary in Table 1 and the guidelines associated with the MBNQA framework, we
propose the model illustrated in Figure 2. The proposed model incorporates the following
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Table 1. A summary of hypotheses tested in previous research under the MBNQA framework.
R0 R1 R2 R3 R4 R5 R6 R7 R8 No.
1 Process � Business result + 3 3 3 × 3 3 3 62 HRS � Process + 3 3 3 – 3 – 3 3 53 Information � HRS + × 3 3 3 3 3 – 3 54 Information � Leadership + 3 3 3 3 – – – 3 45 Information � Strategic plan + 3 3 3 3 3 – – 3 46 Information � Process + × 3 3 3 × 3 – 3 47 Leadership � Strategic plan + 3 3 3 3 × – – 3 48 Information � Business result + × 3 × 3 – – – 3 29 Information � Customer focus + × 3 3 × × – – 3 210 Leadership � Business result + × × 3 3 – – – 3 211 Strategic plan � Customer focus + 3 × × – 3 – – 3 212 HRS � Business result + 3 × × × – – – 3 113 Leadership � Customer focus + × × 3 × – – – 3 114 Leadership � HRS – 3 3 – 3 3 3 – 3 515 Leadership � Process – 3 3 – 3 – 3 – 3 416 Customer focus � Process – 3 3 3 – – – – 3 317 Strategic plan � HRS – 3 3 3 – – – – 3 318 Customer focus � Business result – – – – 3 3 – 3 3 319 Strategic plan � Process – 3 – – – × – – 3 120 Strategic plan � Business result – – × – 3 – – – 3 121 Customer focus � HRS – × × – – – – – – 0
Notes: 3, The hypothesis was tested and accepted; ×, the hypothesis was tested but rejected; – , the hypothesis was not tested at all; R0, MBNQA framework; R1, Winn and Cameron(1998); R2, Wilson and Collier (2000); R3, Flynn and Saladin (2001); R4, Meyer and Collier (2001); R5, Pannirselvam and Ferguson (2001); R6, Kaynak (2003); R7, Evans and Jack(2003); R8, this research; No.R: number of studies testing the relationship; No.+: number of research positive support the relationship.
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16 hypotheses, which are based on previous research (Winn & Cameron, 1998; Wilson &
Collier, 2000; Flynn & Saladin, 2001; Meyer & Collier, 2001; Pannirselvam & Ferguson,
2001; Evans & Jack, 2003; Kaynak, 2003).
H1-1: In the Norwegian sample, leadership is positively correlated with strategic planning.
H1-2: In the Norwegian sample, leadership is positively correlated with business results.
H1-3: In the Norwegian sample, leadership is positively correlated with HRS development.
H1-4: In the Norwegian sample, leadership is positively correlated with process management.
H2-1: In the Norwegian sample, strategic planning is positively correlated with HRSdevelopment.
H2-2: In the Norwegian sample, strategic planning is positively correlated with a customerfocus.
H3-1: In the Norwegian sample, HRS development is positively correlated with processmanagement.
H4-1: In the Norwegian sample, information is positively correlated with process management.
H4-2: In the Norwegian sample, information is positively correlated with business results.
H4-3: In the Norwegian sample, information is positively correlated with a customer focus.
H4-4: In the Norwegian sample, information is positively correlated with strategic planning.
H4-5: In the Norwegian sample, information is positively correlated with leadership.
H4-6: In the Norwegian sample, information is positively correlated with HRS development.
H5-1: In the Norwegian sample, a customer focus is positively correlated with processmanagement.
H5-2: In the Norwegian sample, a customer focus is positively correlated with businessresults.
H6-1: In the Norwegian sample, process management is positively correlated with businessresults.
3. Empirical data and methodological issues
3.1 The questionnaire
The questionnaire, which was designed according to the MBNQA framework, covers lea-
dership, information and analysis, quality strategy, HRS development, quality assurance
Figure 2. The theoretical model developed according to literature and the positing guideline ofMBNQA framework.
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of process and product, customer satisfaction, and business results. The items within each
category are well defined. For example, business results are defined as a composite of cus-
tomer, product and service, financial, and internal operational performance results, includ-
ing HRS and social responsibility results. In each category, there are 10–17 questions/
items. All of the variables in the categories are measured on a scale ranging from 1 to
5, representing the strength or degree of the assessment, agreement, perception, or
opinion for each question. A sixth choice, ‘not relevant’, is also provided. The question-
naire was administered in the USA, India, China, Norway, and Japan.
3.2 The data
In Norway, the survey was conducted with the support of the Norwegian Quality Associ-
ation (NFK) and sponsored by the International Research Institute of Stavanger. Question-
naires were sent out to the 900 NFK member companies. The contact persons were the
companies’ quality managers. A total of 363 replies were received, giving a return rate
of about 40%. The sample includes manufacturing and service companies. The study
reported here is based on data from 180 manufacturing companies, which are involved
primarily in the batch manufacture of mechanical, electrical, and furniture products.
Basic company information is presented in Table 2.
Table 2. Company profiles.
Feature Items %
Size (employees) 0–49 2150–149 29150–499 31500–999 13.1000 5
Years of management initiatives in the company ,1 year 81–3 years 273–5 years 29.5 years 36
Life long employment Yes 95No 5
Quality awards Yes 18No 82
Causes of implementing TQM Customers demand 74Need to reduce cost 47Increased competition 55Due to joint ventures 7Survival of the company 20Concern decision 23Others 5
Education level of informants High school 1Bachelor 21Master 77Ph.D. 1
Positions of informants Top-level management 33Middle-level management 25First-level supervisor 24Quality professionals 5Others 13
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3.3 Validity and reliability tests
Each construct was tested for validity and reliability. In general, validity refers to whether
a particular indicator measures what it is intended to measure, rather than some other
phenomenon (Carmines & Zeller, 1979). Our test included both content and construct val-
idity. Content validity was determined based on the literature review, research experience,
and case studies, whereas construct validity was established through the use of confirma-
tory factor analysis (CFA) (de Vaus, 1993).
Reliability refers to the internal consistency of the items on a scale used to measure a
theoretical construct. The most frequently used estimator for scale reliability is Cronbach’s
alpha (Nunnally, 1978), which is a measure of the internal consistency of a multi-item
scale. Peterson’s (1994) summary of Cronbach’s alpha suggests that a value above 0.7
is sufficient in most situations. Table 3 presents the results of the construct and reliability
tests performed in this study. Although other validity tests can be conducted, construct
validity and reliability are the tests most commonly used in quality management research
(Saraph et al., 1989; Flynn et al., 1994). Consistent with previous research, all of the
constructs used in this study passed the validity and reliability tests.
3.4 Path analysis based on SEM
We employed CFA with a specification search (MacCallum, 1986) for scale construction.
An initial model was specified and assessed by examining the overall model fit and the
significance of individual parameters. In examining the overall model fit, we incorporated
multiple criteria, as recommended by Hu and Bentler (1999) and Byrne (2001), among
others. Because x2 is sensitive to sample size (Kaplan, 2000), the normed chi-square
(x2/df) approach was used as an alternative to assess model fit. In addition, we employed
the goodness-of-fit index (GFI) (Joreskog & Sorbom, 1984) and the comparative fit index
(CFI) (Bentler, 1990). Root-mean-square error of approximation (RMSEA) is strongly
recommended in assessing model fitness (Hu & Bentler, 1998). Generally, GFI and CFI
values above 0.9 and RMSEA values less than 0.05 indicate a good model fit, whereas
RMSEA values between 0.05 and 0.08 (Browne & Cudeck, 1993) suggest a reasonable
fit. Carmines and McIver (1981) recommend a normed chi-square value below 3, although
values up to 5 suggest a reasonable fit (Wheaton, Muthen, Alwin, & Summers, 1977;
Marsh & Hocevar, 1985). If the proposed model did not meet these fit criteria, then it
was re-specified. In this step, the items with measured coefficients that were not significant
(a ¼ 0.05) were excluded from further estimation. In the event that all of the measured
coefficients were significant (a ¼ 0.05), then items with the smallest coefficients were
eliminated. The specification search ended when the overall model fit the data well (i.e.
met the fit index criteria) and all of the individual measurement coefficients were
significant.
4. Results
4.1 Conceptual model and hypothesis testing
The preliminary model depicted in Figure 2 was first tested with all 16 hypotheses, and the
results are shown in Figure 3 and Table 4. It can be seen that six paths did not pass the
significance test threshold, namely, p . 0.05, thus suggesting that they should be excluded
from the empirical model. They were deleted one by one until the fitness test results were
satisfactory.
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Table 3. Validity and reliability tests.
Leadership Information Strategy HRS ProcessBusiness
result Customer
1A 0.79 2A 0.82 3A 0.81 4A 0.73 5A 0.72 7A 0.79 8A 0.641B 0.65 2B 0.85 3B 0.75 4B # 5B 0.70 7B 0.77 8B #1C 0.80 2C 0.80 3C 0.73 4C # 5C 0.76 7C # 8C 0.721D 0.72 2D 0.85 3D 0.72 4D 0.64 5D # 7D 0.76 8D 0.631E 0.72 2E 0.73 3E 0.72 4E 0.77 5E 0.53 7E 0.68 8E #1F 0.79 2F 0.79 3F 0.66 4F 0.73 5F # 7F 0.79 8F #1G 0.79 2G 0.74 3G 0.77 4G 0.64 5G 0.76 7G 0.83 8G 0.651H 0.77 2H 0.87 3H 0.76 4H # 5H 0.63 7H 0.77 8H 0.721I 0.78 3I 0.74 4I 0.70 5I 0.69 7I # 8I 0.71
4J 0.60 5J 0.71 7J 0.81 8J 0.654K 0.81 5K 0.70 7L 0.80 8Q 0.794L 0.74 8R 0.66
Initial eigenvalues 5.16 5.16 5.20 5.13 4.52 4.82 3.90 4.55Variance explained 57.30 57.30 65.00 51.38 50.25 48.24 34.35 45.53KMO test 0.91 0.91 0.90 0.86 0.88 0.89 0.87 0.90Cronbach’s a 0.91 0.91 0.92 0.89 0.88 0.88 0.93 0.86
Validity and reliability tests and basic statistics of the seven constructs1 2 3 4 5 6 7Leadership Strategic plan Customer focused Information HRS Process Business results
Initial eigenvalues 4.65 6.60 5.94 4.78 5.41 7.00 7.03Variance explained 52% 66% 54% 60% 54% 58% 54%Cronbach’s a 0.88 0.94 0.91 0.90 0.90 0.93 0.93
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4.2 Empirical model of sampled Norwegian companies
After deleting the non-significant paths, the fitness test showed the model to be satisfactory
(x2 ¼ 16.466, x2/df ¼ 2.058 , 3, GFI ¼ 0.975 . 0.9, CFI ¼ 0.989 . 0.9 and RMSEA
Figure 3. The first-round test of the conceptual model.
Table 4. Regression weights of all the paths in the conceptual model.
Estimate SE CR P Label
Leadersh�Informat 0.661 0.056 11.771 ∗∗∗
Strategy�Leadersh 0.646 0.064 10.064 ∗∗∗
Strategy�Informat 0.165 0.064 2.575 0.010HRS�Strategy 0.553 0.075 7.342 ∗∗∗
Customer�Strategy 0.478 0.088 5.449 ∗∗∗
HRS�Informat 0.194 0.066 2.952 0.003Customer�Informat 0.273 0.077 3.563 ∗∗∗
Customer�Leadersh 20.026 0.094 20.277 0.781 RejectHRS�Leadersh 0.092 0.081 1.142 0.253 RejectProcess�Customer 0.160 0.067 2.369 0.018Process�HRS 0.414 0.074 5.568 ∗∗∗
Process�Informat 0.279 0.078 3.597 ∗∗∗
Process�Leadersh 20.059 0.080 20.732 0.464 RejectPerforma�Process 0.218 0.071 3.076 0.002Performa�HRS 0.106 0.084 1.251 0.211 RejectPerforma�Strategy 0.108 0.096 1.121 0.262 RejectPerforma�Customer 0.259 0.069 3.765 ∗∗∗
Performa�Leadersh 0.190 0.086 2.209 0.027Performa�Informat 0.016 0.076 0.211 0.833 Reject
Note: SE, standard error; CR, critical ratio.∗∗∗p , 0.001.
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¼ 0.077 , 0.05). This model is the alternative empirical model of the sampled Norwegian
companies (Figure 4). The tested model shown in Figure 5 represents the synthesis, align-
ment, and integration of the sampled Norwegian companies. The regression weights of the
paths are shown in Table 5. Three linkages are missing, that from leadership to HRS in the
strategic triad and those from information management to performance and from HRS to
performance in the results triad. The implications of these results are discussed in the next
section.
Figure 4. The final results of the model.
Figure 5. The alternative empirical model for Norwegian sampled companies.
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5. Discussion and implications
The core theme of the MBNQA model is that ‘the successful management of overall
performance requires organisation-specific synthesis, alignment, and integration’. Com-
pared with the conceptual model in Figure 1, we can see that three linkages are
missing in the Norwegian companies, as shown by the dotted lines in Figure 5.
This result indicates that the sampled Norwegian companies have not reached a full
level of synthesis, alignment, and integration, the implications of which are discussed
in the following.
5.1 Information management and business results
Research shows that information management is strongly linked with leadership, strategic
planning, HRS development, a customer focus, and process management. However, the
linkage from information management to business results is often missing. Four studies
have investigated the information–business results relationship, with two supporting
(Winn & Cameron, 1998; Flynn & Saladin, 2001) and two failing to support (Wilson &
Collier, 2000; Meyer & Collier, 2001) this relationship. Prybutok, Richards, and Cutshall
(2001) found information and analysis to be critical in ensuring that executive leadership
engages in quality management, particularly that based on the quality award model. In
fact, information is a component of leadership, and accordingly, information management
may not have a direct influence on business results.
5.2 Leadership and HRS development
McCarthy and Greatbanks (2006) discovered that companies that adopt the quality award
model for assessment can better identify good leadership practices, which in turn can lead
to enhanced quality leadership. Bell and Elkins (2004) proposed a balanced score card
model of leadership for the quality award model. They suggested that leadership should
look at the strategic and operational issues related to HRS development. Previous research
based on the MBNQA framework also supports the path from leadership to HRS (Winn &
Cameron, 1998; Wilson & Collier, 2000; Flynn & Saladin, 2001; Meyer & Collier, 2001;
Pannirselvam & Ferguson, 2001). However, in the strategic triad of our Norwegian
Table 5. Regression weights of significant paths in the alternative empirical model.
Estimate SE CR P Label
H1-1 Leadership�Strategic plan 0.415 0.133 3.126 0.002∗ PassedH1-4 Leadership�Process management 20.366 0.133 22.755 0.006∗ PassedH2-2 Strategic plan�Customer focus 0.864 0.145 5.973 ∗∗∗ PassedH3-1 HRS development�Process management 0.409 0.107 3.834 ∗∗∗ PassedH4-4 Information�Strategic plan 0.575 0.154 3.723 ∗∗∗ PassedH4-5 Information�Leadership 0.633 0.169 3.735 ∗∗∗ PassedH4-6 Information�HRS development 0.805 0.185 4.363 ∗∗∗ PassedH5-1 Customer focus�Process management 0.636 0.107 5.935 ∗∗∗ PassedH5-2 Customer focus�Business result 0.666 0.080 8.355 ∗∗∗ Passed
Note: SE, standard error; CR, critical ratio.∗p , 0.05.∗∗∗p , 0.001.
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sample, the path linkage from leadership to HRS is missing, which implies weak HRS lea-
dership in this sample.
5.3 HRS, customer focus, and business results: implications for modifying theMBNQA framework
The test did not support the linkage between HRS and business results. Only one of four
previous studies (Winn & Cameron, 1998) supported the path from HRS to business
results, with the other three failing to do so (Wilson & Collier, 2000; Flynn & Saladin,
2001; Meyer & Collier, 2001). However, one path that is omitted from the conceptual fra-
mework of the MBNQA model turned out to be positive in this study, that is, the path from
a customer focus to business results, a path that is also supported by previous research
(Meyer & Collier, 2001; Pannirselvam & Ferguson, 2001; Evans & Jack, 2003). Recent
research (Cai, 2009) also supports the relationship between a customer orientation and per-
formance. Accordingly, the MBNQA framework may require the modification shown in
Figure 5, in which the positions of HRS development and customer focus are switched.
The managerial implications of such modification would be huge. For example, the leader-
ship triad would include leadership, strategy, and HRS or workforce development,
meaning that HRS issues would play an important role in quality strategies. The scoring
system of the award assessment (NIST, 2010, p. 60) would also need to be adjusted
accordingly. However, these suggestions are preliminary in nature and require additional
research prior to implementation.
6. Limitations and future research
The study reported herein employed the MBNQA framework to evaluate quality manage-
ment practices in Norwegian manufacturing companies. Our results show that the sampled
companies have not reached the full level of synthesis, alignment, and integration required
by the core theme of the MBNQA framework. The three areas that require improvement
are leadership and HRS development, HRS and performance, and information manage-
ment. Furthermore, it was also found that these factors cannot be addressed separately.
Instead, they must be considered in conjunction with related factors to achieve the requi-
site level of synthesis, alignment, and integration.
It must be emphasised that the results of this study are limited to the sampled manu-
facturing companies. Future research should be conducted with larger samples and com-
paring manufacturing and other types of companies, for example, service companies,
using the same conceptual model.
Numerous quality management studies have been carried out on the basis of simple or
multiple correlations. The present study demonstrates that research based on structural
models leads to quite different results and provides an understanding of the interrelation-
ships among quality management components. Based on their review of more than 85
articles, Rahman and Sohal (2002) suggested that future research should identify the
impact of each element or component of TQM on organisational performance, particularly
in the context of analytical models. In other words, the important issue is whether the set of
practices associated with quality management is valid as a whole (Sousa & Voss, 2002).
Such research based on the US Quality Award Model has somehow stopped since 2003.
There has been empirical research testing the relationship among categories among Euro-
pean Quality Award Model as well (Dahlgaard & Nilsson, 2002; Tarı et al., 2006; Marten-
sen et al., 2007). However, since the structure of the two quality award models are
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different and the results cannot be comparable yet. It was noted that more empirical
research based on the European Quality Model has been reported recently (Dahlgaard-
Park & Dahlgaard, 2010; Dahlgaard, Pettersen, & Dahlgaard-Park, 2011). Hopefully,
more systems-oriented research will be conducted based on the two quality award
models in the future.
Our sample included no award-winning companies. Consequently, our methodology is
not appropriate for validating the quality award model. This issue could be addressed in
future with a larger and more representative sample. For example, as it is recommended
here that the positions of HRS development and customer focus be switched, their
scoring weights should also be reconsidered in future. It is hoped that similar research
will be conducted in other countries with more data to allow the validity issues of the
model underlying the quality award framework to be tested in a more comprehensive
manner.
At the conceptual level, this study has implications for future versions of the quality
award model. The conceptual models developed in previous research suggest that a cus-
tomer focus is directly related to business results. However, this is not obvious in the
MBNQA framework. At the same time, HRS management obviously contributes directly
to business results, although previous research does not support such a linkage. Therefore,
the positions of HRS and customer focus were switched in the conceptual model used in
this study. McAdam and Henderson (2004) pointed out that the rapid rate of change in
global and niche markets has increased the pressure on organisations to become more
competitive. Recent research also reviewed the European Quality Award Model using
management control theories (Dahlgaard-Park, 2008). TQM itself is not immune from
such pressure, which suggests that the quality award model may also need to change
accordingly. Furthermore, the model may even require variation from culture to culture,
as Flynn and Saladin (2006) suggested.
Acknowledgement
The research reported in this paper was fully supported by a Strategic Research Grant
(SRG 7002298) from the City University of Hong Kong, Hong Kong, People’s Republic
of China.
References
Bailey, D.E. (1998). Comparison of manufacturing performance of three team structures in semicon-ductor plants. IEEE Transactions on Engineering Management, 45(1), 20–32.
Bell, R.R., & Elkins, S.A. (2004). A balanced scorecard for leaders: Implications of the MalcolmBaldrige National Quality Award Criteria. Advanced Management Journal, 69(1), 12–18.
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107,238–246.
Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K.A. Bollen & J.S.Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: SagePublications.
Byrne, B.M. (2001). Structural equation modeling with AMOS: Basic concepts, applications andprogramming. Mahwah, NJ: Lawrence Erlbaum Associates.
Cai, S. (2009). The importance of customer focus for organizational performance: A study ofChinese companies. The International Journal of Quality & Reliability Management, 26(4),369–379.
Carmines, E.G., & McIver, J.P. (1981). Analyzing models with unobservable variables. In G.W.Bohrnstedt & E.F. Borgatta (Eds.), Social measurement: Current issues (pp. 65–115).Beverly Hills: Sage.
Total Quality Management 1209
Dow
nloa
ded
by [
Aca
dia
Uni
vers
ity]
at 2
1:06
15
May
201
3
Carmines, E.G., & Zeller, R.A. (1979). Reliability and validity assessment. Atlanta, GA: Beverly.Chan, C.O., & Sun, H.Y. (2004). A model for implementing Six Sigma in China SMEs. In: S. Ho
(Ed.), Proceedings of the 9th International Conference on ISO9000 & TQM, Bangkok,Thailand, April 5–7, 2004, pp. 238–247 [CD-ROM].
Crosby, P.B. (1979). Quality is free – the art of making quality certain. New York, NY: McGraw-Hill.
Dahlgaard, J.J., & Nilsson, L. (2002). Understanding excellence: Benchmarking performance andidentifying improvement areas. European Quality, 9(4), 88–100.
Dahlgaard, J.J., Pettersen, J., & Dahlgaard-Park, S.M. (2011). Quality and lean healthcare – a systemfor assessing and improving the health of healthcare organizations. Total QualityManagement & Business Excellence, 22(6), 673–689.
Dahlgaard-Park, S.M. (2008). Reviewing the European excellence model from a managementcontrol view. The TQM Journal, 20(2), 98–119.
Dahlgaard-Park, S.M., & Dahlgaard, J.J. (2010). Organizational learnability and innovability – asystem for assessing, diagnosing and improving innovation excellence. InternationalJournal of Quality and Service Sciences, 2(2), 153–174.
Deming, W.E. (1986). Out of crisis. Cambridge, MA: MIT Press.Evans, J.R. (1997). Critical linkages in the Baldrige Award Criteria: Research models and edu-
cational challenges. Quality Management Journal, 5(1), 13–25.Evans, J.R., & Jack, E.P. (2003). Validating key results linkages in the Baldrige Performance
Excellence Model. Quality Management Journal, 10(2), 7–25.Feigenbaum, A.V. (1991). Total quality control, the 40th anniversary edition. London: MacGraw-
Hill International.Flynn, B.B., & Saladin, B. (2001). Further evidence on the validity of the theoretical models
underlying the Baldrige criteria. Journal of Operations Management, 19(6), 617–652.Flynn, B.B., & Saladin, B. (2006). Relevance of Baldrige constructs in an international context: A
study of national culture. Journal of Operations Management, 24(5), 583–603.Flynn, B.B., Schroeder, R.G., & Sakakibala, S. (1994). A framework for quality management
research and an associated measurement instrument. Journal of Operations Management,11, 1339–1366.
Garvin, D.A. (1988). Managing quality. New York, NY: Free Press.Hill, T. (1995). Manufacturing strategy: Text and cases. London: Macmillan Press.Hu, L., & Bentler, P.M. (1998). Fit indices in covariance structure modeling: Sensitivity to under-
parameterized model misspecification. Psychological Methods, 3, 424–453.Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.Ishikawa, K. (1985). What is total quality control? The Japanese Way. Englewood Cliffs, NJ:
Prentice Hall.Jacob, R., Madu, C.N., & Tang, C. (2004). An empirical assessment of the financial performance of
Malcolm Baldrige Award winners. International Journal of Quality & ReliabilityManagement, 21(8), 897–914.
Joreskog, K.G., & Sorbom, D. (1984). LISREL-VI user’s guide (3rd ed.). Mooresville, IN: ScientificSoftware.
Juran, J.M. (1989). Juran on leadership for quality: An executive handbook. New York, NY: TheFree Press.
Kaplan, D. (2000). Structural equation modelling: Foundations and extensions. Newbury Park, CA:Sage Publications.
Kaynak, H. (2003). The relationship between total quality management practices and their effects onfirm performance. Journal of Operations Management, 21, 405–435.
Khanna, V.K., Vrat, P., Shankar, R., & Sahay, B.S. (2002). Developing causal relationships for aTQM index for the Indian automobile sector. Work Study, 51(6/7), 364–373.
MacCallum, R. (1986). Specification searches in covariance structure modeling. PsychologicalBulletin, 100(1), 107–120.
Marsh, H.W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study ofself-concept: First- and higher-order factor models and their invariance across groups.Psychological Bulletin, 97, 562–582.
1210 H. Sun
Dow
nloa
ded
by [
Aca
dia
Uni
vers
ity]
at 2
1:06
15
May
201
3
Martensen, A., Dahlgaard, J.J., Dahlgaard-Park, S.M., & Grønholdt, L. (2007). Measuring and diag-nosing innovation excellence – simple contra advanced approaches – a Danish study.Measuring Business Excellence, 11(4), 51–65.
McAdam, R., & Henderson, J. (2004). Influencing the future of TQM: Internal and external drivingfactors. The International Journal of Quality and Reliability Management, 21(1), 51–71.
McCarthy, G., & Greatbanks, R. (2006). Impact of EFQM excellence model on leadership inGerman and UK organizations. The International Journal of Quality & ReliabilityManagement, 23(9), 1068–1091.
Meyer, S.M., & Collier, A. (2001). An empirical test of the causal relationships in the BaldrigeHealth Care Pilot Criteria. Journal of Operations Management, 19(4), 403–425.
National Institute of Standards and Technology. (2010). 2011–2012 Criteria for business excel-lence. Retrieved from http://www.nist.gov/baldrige/
Nunnally, J.C. (1978). Psychometric theory. New York, NY: McGraw-Hill Publishing.Olderding, S.R. (1998). Mitsubishi and Honda on competition and quality circles. The Journal for
Quality and Participation, 21(3), 55–59.Pannirselvam, G.P., & Ferguson, L.A. (2001). A study of the relationships between the Baldrige cat-
egories. International Journal of Quality and Reliability Management, 18(1), 14–34.Pannirselvam, G.P., Siferd, S.P., & Ruch, W.A. (1998). Validation of the Arizona governors’ quality
award criteria: A test of the Baldrige criteria. Journal of Operations Management, 16(5),529–550.
Pedhazur, E.J. (1988). Multiple regression in behavioral research. New York, NY: Holt, Rinehart,and Winston.
Peterson, R.A. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of ConsumerResearch, 21(2), 381–391.
Prajogo, D.I., & Sohal, A.S. (2006). The integration of TQM and technology/R&D management indetermining quality and innovation performance. Omega, 34(3), 296–312.
Prybutok, V.R., Richards, R.M., & Cutshall, R. (2001). The significance of information and analysisas a component of a leadership model based on Malcolm Baldrige National Quality Awardcriteria. The Journal of Computer Information Systems, 41(4), 52–57.
Rahman, S., & Sohal, A.S. (2002). A review and classification of total quality management researchin Australia and an agenda for future research. The International Journal of Quality &Reliability Management, 19(1), 46–67.
Revere, L., & Black, K. (2003). Integrating Six Sigma with total quality management: A caseexample for measuring medication errors. Journal of Healthcare Management, 48(6),377–392.
Samson, D., & Terziovski, M. (1999). The relationship between total quality management practicesand operational performance. Journal of Operations Management, 17(4), 393–409.
Saraph, J.V., Benson, P.G., & Schoeder, R.G. (1989). An instrument for measuring the criticalfactors of quality management. Decision Sciences, 20, 810–829.
Schmenner, R.W. (1995). Service operations management. Englewood, NJ: Prentice Hall.Sila, I. (2007). Examining the effects of contextual factors on TQM and performance through the
lens of organizational theories: An empirical study. Journal of Operations Management,25(1), 83–109.
Sousa, R., & Voss, C.A. (2002). Quality management re-visited: A reflective review and agenda forfuture research. Journal of Operations Management, 20, 91–109.
Tarı, J.J., Molina, J.F., & Castejon, J.L. (2006). The relationship between quality managementpractices and their effects on quality outcomes. European Journal of OperationalResearch, 183(2), 483–501.
de Vaus, D.A. (1993). Survey in social research (3rd ed.). San Francisco, CA: UCL Press.Wheaton, B., Muthen, B., Alwin, D.F., & Summers, G.F. (1977). Assessing reliability and stability in
panel model. In D.R. Heise (Ed.), Sociological methodology (pp. 84–136). San Francisco,CA: Jossey-Bass.
Wilson, D.D., & Collier, D.A. (2000). An empirical investigation of the Malcolm Baldrige NationalQuality Award causal model. Decision Sciences, 31(2), 361–390.
Winn, B.A., & Cameron, K.S. (1998). Organizational quality: An examination of the MalcolmBaldrige quality framework. Research in Higher Education, 39(5), 491–512.
Zurier, S. (1989). Delivering quality customer service. Industrial Distribution, 78(3), 30–34.
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