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Int. J. Production Economics ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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Int. J. Production Economics
journal homepage: www.elsevier.com/locate/ijpe

The impact of hard and soft quality management on quality
and innovation performance: An empirical study$
Jing Zeng a,n, Chi Anh Phan b, Yoshiki Matsui c
a

International Graduate School of Social Sciences, Yokohama National University, 79-4 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan
University of Economics and Business – Vietnam National University, Hanoi 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam
c
Department of Business Administration, Yokohama National University, 79-4 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan
b

art ic l e i nf o

a b s t r a c t

Article history:
Received 17 June 2014
Accepted 5 July 2014

This study examines the conflicting relationship between quality management (QM) and innovation on a
global basis using a multidimensional view of QM. QM is divided into two dimensions: hard QM and soft
QM. Quality performance as an intended consequence of QM implementation is also examined as a
potential mediator between QM and innovation. A conceptual framework is developed to postulate
causal linkages between soft/hard QM, quality performance, and innovation performance. Data collected

from 283 plants in eight countries and a technique of structural equation modeling are used to test this
framework. The results indicate different paths to innovation from different dimensions of QM. Hard QM
affects innovation performance directly and indirectly through its effect on quality performance. Soft QM
has indirect effect on innovation performance through its effect on hard QM. This means that quality
performance depends directly on hard QM which can be promoted by soft QM. Quality performance
shows a partial mediating effect on the relationship between hard QM and innovation performance.
Quality and innovation are not a matter of trade-off, but they can coexist in a cumulative improvement
model with quality as a foundation. Firms have no need to abandon QM endeavor to achieve innovation.
Instead, they should devote continuous efforts to maintain a solid quality system in place integrating a
set of QM practices and corresponding performance measures. Managers are advised to emphasize on
quality control tools and techniques and use teamwork, training, employee empowerment and problemsolving approaches as an underlying support.
& 2014 Elsevier B.V. All rights reserved.

Keywords:
Soft QM
Hard QM
Quality performance
Innovation

1. Introduction
In the more and more competitive marketplace, both quality
and innovation are playing crucial roles in securing a sustainable
competitive advantage. Quality-based competition is regarded
more as an “order qualifier” criterion, while competition based
on flexibility, responsiveness and particularly innovation is viewed
as one of “order winner criteria” (Tidd et al., 1997). To survive in a
dynamic environment, organizations need to be ambidextrous –

☆
This article was selected from papers presented at the 4th World Conference on

Production and Operations Management (P&OM Amsterdam 2012), co-organized
by the European Operations Management Association (EurOMA), The Production
and Operations Management Society (POMS) and the Japanese Operations Management and Strategy Association (JOMSA). The original paper has followed the
standard review process for the International Journal of Production Economics.
The process was managed by Jose A.D. Machuca (POMS-EurOMA) and Andreas
Groessler (EurOMA) and supervised by Bart L. MacCarthy (IJPE Editor, Europe).
n
Correspondence to: 20-402, 1500 Kamisugeta-cho, Hodogaya-ku, Yokohama,
2400051, Japan. Tel.: þ 81 453393734.
E-mail addresses: (J. Zeng),
(C. Anh Phan), (Y. Matsui).

aligned and efficient in managing today's market demands, while
adaptive enough to environmental changes coming tomorrow
(Gibson and Birkinshaw, 2004). However, this does not seem to
be an easy thing, as manifested by Toyota's recall crisis.
In the early 1990s, Toyota has earned itself the reputation for an
amazing and unprecedented record of quality. Later, Toyota tried
to move toward innovation by developing core technology, pathbreaking vehicles and new routines of product development for
21st century (Nonaka and Peltokorpi, 2009). In 1997, Toyota
launched the world's first commercialized hybrid car — Prius,
which received numerous awards and orders. However, “Toyota's
reputation for quality was tarnished by massive global recalls that
started five years ago and ultimately encompassed almost every
model in its lineup and totaled more than 10 million vehicles” (The
Associated Press, 2013). Why does a firm with a strong quality
focus have so many quality issues in such a short amount of time?
Is it just because Toyota did not strongly focus on quality issues
while pursuing innovation? Or, is any attempt to achieve both
quality and innovation doomed to fail?

The recent Toyota crisis leads us to rethink about quality
management (QM)'s value and role in securing other competitive

/>0925-5273/& 2014 Elsevier B.V. All rights reserved.

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advantages, particularly innovation, in future competitive environment. A practical management issue emerged: Does QM foster
or hinder innovation? However, literature on this issue fails to
provide a clear answer to this question since there are conflicting
arguments pertaining to the relationship between QM and innovation (Prajogo and Sohal, 2001). Furthermore, there are only a
few empirical attempts to test this relationship. Some studies use
an integrated approach to consider QM as one single factor
influencing innovation and empirically found the relationship
between them to be positive (Sadikoglu and Zehir, 2010; SantosVijande and Álvarez-González, 2007; Prajogo and Sohal, 2003).
Some studies analyze this issue in more depth by considering
multidimensional aspects of QM (Prajogo and Sohal, 2004; Feng et
al., 2006), but their scope is usually restricted to a specific region
(e.g. Australia, Singapore). Martínez-Costa and Martínez-Lorente
(2008) suggest that more studies are needed to analyze which QM
dimensions have more effect on innovation and whether some of
them could be a barrier to it. Following the suggestion, this study
adopts a multidimensional view of QM to examine the impact of
QM implementation on innovation performance in a more extensive context across eight countries.
Previous literature on QM has proposed different dimensions

embodied by QM. As noted by Wilkinson (1992), the “hard” aspect
of QM involves a range of production techniques, such as statistical
process control and quality function deployment, reflecting the
production orientation of the QM gurus. The “soft” aspect of QM is
more concerned with the establishment of customer awareness
and the management of human resources. Following this classification, we view QM from two dimensions, hard QM and soft QM,
and use this view to solve the dispute over the relationship
between QM and innovation. Nevertheless, the literature on
quality has dispute over the relationships between these two
dimensions of QM and their contribution to performance. It
presents mixed results regarding whether soft QM has a direct
or indirect impact on performance, and which dimension is more
important to yield superior performance. Since our paper is
grounded on the dichotomy view of QM, clarifying the relationship
between hard QM and soft QM in linking them to quality
performance is the prerequisite for further investigation on the
QM–innovation relationship.
These opposing arguments also extend to the relationship
between quality performance and innovation performance. A
fundamental question remains about whether organizations can
excel in both types of performance or have to achieve one at the
expense of the other. Empirical studies have rarely investigated
the mediating effect of quality performance on the relationship
between QM practices and innovation performance. To further
explore the direct and indirect relationship between quality and
innovation, we examine the relationship between quality performance and innovation performance. In this paper, we particularly
focus on product innovation, whose relationship with QM is more
controversial and ambiguous, compared to process innovation,
which is closely linked to QM's concept of streamlining a process.
Above all, the purpose of this study is to empirically examine

the relationships between two dimensions of QM (hard QM and
soft QM) and quality/innovation performance on a global basis.
It aims to answer the following questions:
1.
2.
3.
4.

How does hard QM relate to soft QM?
How does hard/soft QM relate to quality performance?
How does hard/soft QM relate to innovation performance?
How does quality performance relate to innovation
performance?

A conceptual framework is developed in this study to postulate
causal linkages across hard/soft QM, quality performance, and

innovation performance. This framework is examined at the
operational level, as Flynn et al. (1994) have noted that QM is
not always implemented at the firm level, but the plant level is the
level at which QM is often implemented. Data for this study were
collected from 283 plants in eight countries across three industries
and the framework is tested using structural equation modeling
(SEM). The findings indicate that, in general, QM can provide a
fertile environment to foster innovation. The results also suggest
the different ways of different dimensions of QM to affect
innovation.
Our study contributes to a multidimensional view of QM in
exploring different paths to innovation from different dimensions
of QM. Also, by using a sample of eight industrialized countries,

this study contributes to the generalization of the positive relationship between QM and innovation. Furthermore, the results
regarding the different ways of different dimensions of QM to
affect innovation can provide guidance for the organizations to
adjust hard and soft QM to meet the quality and innovation needs.
The remainder of this paper is organized as follows. In the next
section, we provide a literature review on the relationship
between QM and innovation, which helps develop the research
hypotheses. We then describe the research methodology, followed
by presenting the results of hypotheses testing. Section five
discusses the main findings and implications stemming from this
research. Section six includes limitations of this study and future
research. Finally, the conclusions are summarized in the last
section.

2. Literature review and hypothesis development
This section includes a brief review of the literature that has
examined relationships between QM and innovation as well as the
two dimensions of QM. Following the literature review, we
formulate our hypotheses.
2.1. QM–innovation relationship
There are conflicting arguments about the relationship
between QM and innovation (Prajogo and Sohal, 2001). One group
of arguments claims that philosophy and principles of QM are not
compatible with innovation. QM advocates the philosophy of
continuous improvement which aims at simplifying or streamlining a process. Continuous improvement focuses on incremental
change and requires standardization or formalization in order to
establish control and stability (Imai, 1986; Jha et al., 1996). This
would yield rigidity and inhibit innovation by trapping people into
focusing on the details of the current quality process rather than a
new idea to change the current work system (Morgan, 1993;

Glynn, 1996). Process management practices basically aiming at
eliminating waste and improving efficiency could be detrimental
to innovation, since it reduces slack resources that are necessary
for fertilizing innovation (Sadikoglu and Zehir, 2010). Bennett and
Cooper (1981) and Slater and Narver (1998) have criticized the
customer focus itself as a source of innovation. These authors
contend that customer focus could lead organization “narrowminded” to current product and services rather than making
breakthrough improvements to explore customers' latent needs.
However, positive viewpoint contends that companies embracing QM in their system and culture can provide a fertile environment for innovation. McAdam et al. (1998) argue that “in many
ways QM can be seen as laying the foundation of a culture
environment that encourages innovation” (p. 141). Pfeifer et al.
(1998) propose three subject areas of importance for innovation:
customer orientation and service; flexible organizational structures; and creative staff, which are in agreement with the QM

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principles. QM advocates customer focus which also highlights the
importance of delighting customers. Thus focusing on customers
can stimulate companies to search for new customer needs and be
creative beyond simply conforming to standards (Prajogo and
Sohal, 2001; 2003). The implementation of QM would result in
changes in the organizational structure, making it flexible (Forza,
1996), which would yield a beneficial effect on innovation. QM
promotes employee empowerment, involvement and teamwork,
which is highly linked to workers' autonomy and knowledge
transfer. The literature has highlighted the important role of
teamwork (Humble and Jones, 1989), workers' autonomy

(Spreitzer, 1995), and knowledge transfer (Molina et al., 2007) in
nurturing innovation. Imai (1986) maintains that continuous
improvement is needed to sustain the benefits resulting from
innovation. The concepts of QM such as formalization and
empowerment can create the necessary balance between autonomy, discipline and underlying control, which provides a solid
basis for the development of gradual innovations and eventually
radical innovations (Santos-Vijande and Álvarez-González, 2007).
These opposing arguments also extend to the relationship
between quality performance and innovation performance, making the QM–innovation relationship more ambiguous. The conventional wisdom has been that fast product innovation and
quality represent a trade-off (Flynn, 1994). This model suggests
that an improvement in one measure of performance necessitates
a decrease in another and thus firms cannot achieve high levels of
performance for multiple competitive priorities simultaneously.
However, the cumulative or “sandcone” model (Ferdows and de
Meyer, 1990) argues that firms are able to improve multiple
dimensions of performance concurrently because the improvements reinforce each other in a cumulative fashion. Some
researchers such as Leong et al. (1990), Corbett and Van
Wassenhove (1993), and Noble (1995) have positioned innovation
performance as the ultimate apex of the pyramid in the sandcone
model, arguing that the achievement of innovativeness is built
upon the cumulative effect of improvement on other types of
manufacturing performance including quality performance. This
confusion needs to be clarified, since improving quality performance is the fundamental driver for firms to implement QM
practices. Also, understanding the relationship between quality
performance and innovation performance would help us explore
the possible mediating effect of quality performance on the
relationship between QM practices and innovation performance,
which is rarely considered by previous empirical studies.
Sadikoglu and Zehir (2010) point out that few empirical studies
have investigated the mediating effect of one type of performance

on the relationship between QM practices and another type of
performance. In this paper, we include the examination on the
relationship between quality performance and innovation performance to fill such a gap and provide further insight on the
ambiguous QM–innovation relationship.
Despite the ongoing arguments above, the empirical studies
which investigate the relationship between QM and innovation are
rather limited, and researchers have reported mixed results. The
seminal work by Flynn (1994) reports on the relationship between
QM and the speed of product innovation. The findings demonstrate that quality foundation and organizational infrastructure
can support fast product innovation. McAdam et al. (1998)
compare QM (presented by continuous improvement) to innovation in 15 companies in Ireland, finding a significant and strong
correlation between continuous improvement and innovation.
They argue that such a strong correlation in fact indicates a causal
relationship where the introduction of continuous improvement
over a period of time would lead to innovation. Prajogo and Sohal
(2003), based on a sample of Australian firms, found a positive
relationship between QM and innovation performance. However,

3

Singh and Smith (2004), with a wider sample of Australian
manufacturing organizations, could not find a strong link between
QM and innovation. Perdomo-Ortiz et al. (2006) identify three QM
practices (process management, product design, and human
resource management) standing out for the establishment of
business innovation capability. However, the empirical findings
by Kim et al. (2012) highlight the critical role of process management through which a set of interlocked QM practices positively
relates to each type of innovation (e.g. radical product innovation,
incremental product innovation). Empirical studies such as
Martínez-Costa and Martínez-Lorente (2008), Santos-Vijande and

Álvarez-González (2007), and Sadikoglu and Zehir (2010) all
analyze the overall impact of QM and innovation and found a
positive result. Abrunhosa and Sá (2008) argue that the overall
impact of QM and innovation is difficult to generalize, since QM is
a complex management philosophy encompassing both “hard”
and “soft” elements, which may lead to contrasting results in
association with innovation. We consider that a study which
analyzes the different dimensions of QM (hard versus soft) in
linking to innovation would provide more insight in explaining the
ambiguous relationship between QM and innovation. Another
drawback of the previous empirical studies on the QM–innovation
relationship reviewed above is the restricted scope to a specific
geographical region, such as US, Spain, Turkey, Australia, and
Canada. Empirical evidence based on a wider sample beyond a
specific region would add more knowledge about the QM–innovation relationship. In this paper, we will look into the hard and soft
dimensions of QM and investigate their impact on innovation
respectively with a global sample.
From a managerial perspective, firms can perceive quality
improvement, especially in product, more as a way to achieve
strategic needs of gaining knowledge than as a way to satisfy the
needs of obtaining efficiency and effectiveness. Mazzola and
Perrone (2013) provide empirical evidence that “improving product quality” is closely related to the strategic needs aiming at
gaining knowledge. This is because improvement in quality
requires firms to increase their technological knowledge and their
ability to understand and solve customers' problems. This knowledge and learning capability can be then used to build superior
new product development capability, leading to improved innovation output.

2.2. Hard QM and soft QM
Martínez-Costa and Martínez-Lorente (2008) argue that a
possible explanation of the contrary effect of QM on innovation

would be the different ways of QM implementation in a firm by
focusing more on hard aspects or more on soft aspects of QM. The
multidimensional view of QM has emerged in recent literature as a
promising approach to resolve the debate regarding the relationship between QM and innovation. Prajogo and Sohal (2004) divide
QM into two dimensions: mechanistic (customer focus and process management) and organic (leadership and people management) dimensions. Their results based on an Australian sample
indicate that the hard aspect of QM which is more mechanistic
favors quality performance, whilst the soft aspect of QM which is
more organic positively relates to innovation performance. A
replicated study conducted by Feng et al. (2006) in Singapore
confirms the conclusion. However, including customer focus into
the mechanistic dimension of QM has been questioned by
Martínez-Costa and Martínez-Lorente (2008) who argue that
customer focus has traditionally been considered to be one of
the “intangible” elements of QM which is more soft (Anderson and
Sohal, 1999; Dow et al., 1999; Samson and Terziovski, 1999).
Martínez-Costa and Martínez-Lorente (2008) suggest that more

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studies are needed to analyze the relationship between QM and
innovation in more depth using a multidimensional view of QM.
QM literature has revealed the existence of different dimensions of QM. Wilkinson (1992) maintains that QM has both “hard”
and “soft” sides. Hard QM pertains to the technical aspects of QM,
whereas soft QM relates to the social/behavioral attributes of QM.
Flynn et al. (1995) advocate that QM practices can be divided into

two interdependent groups: core quality management practices
such as process flow management, product design process, and
statistical control and feedback, and quality management infrastructure practices which are broadly defined in terms of customer
relationship, supplier relationship, work attitudes, workforce
management and top management support. Ho et al. (2001) follow
this concept and conclude that core QM practices completely
mediate the effect of supportive QM practices on quality performance. Kochan et al. (1995) argue there are two ways of implementing QM – one approach conceptualizes QM as a relatively
limited set of technical engineering changes while the second
implements these technical changes as part of broader changes to
human resource practices. Forza (1995) examines the QM system
from a dichotomy view: QM practices (e.g. quality continuous
improvement, process control) and the supporting information
system including quality information flows and information technologies for quality, and demonstrates the interdependence
between them. Sitkin et al. (1994) argue the existence of two
different orientations of QM: TQC (Total Quality Control) and TQL
(Total Quality Learning), with TQC focusing on cybernetic control
system and TQL facilitating sharing of knowledge and skills.
Rahman and Bullock (2005) distinguish hard QM, which is tool/
technique oriented, from soft QM, which is essentially dimension
of human resource management. Their results show a partial
mediating effect of hard QM on the relationship between soft
QM and performance. Though the labels and the coverage of the
two dimensions of QM emerging from these studies would vary
somewhat, the essential concept they advocate tends to be
congruent. We adopt the conceptualization of Wilkinson (1992)
where QM is classified into two dimensions: hard QM and soft QM.
We provide the definition below, and identify the main constructs
for them from major QM studies at an operational level.
Hard QM is generally defined as the QM practices which focus
on controlling processes and products through techniques and

tools in order to conform to and satisfy established requirements.
One of the most representative tool/technique-oriented QM practices (hard QM) is process management which has been covered by
most major studies on QM such as Saraph et al. (1989), Anderson
et al. (1995), and Flynn et al. (1995). Process management refers to
monitoring of manufacturing process through the techniques and
tools applied to a process to reduce process variation, so that it
operates as expected, without breakdowns, missing materials,
fixtures, tools, etc. and despite workforce variability (Flynn et al.,
1994). The process management category can further be broken
down into sub-categories. According to Flynn et al. (1994), process
management includes three major practices: process control,
preventative maintenance, and housekeeping. Process control is
used to track process performance for in-production quality
assurance (Deming, 1986; Ahire and Dreyfus, 2000). Preventative
maintenance aims to conduct safety activities and avoid equipment breakdowns through scheduled maintenance (Flynn et al.,
1995; Arauz et al., 2009). Housekeeping focuses on keeping the
cleanliness and organization of the workplace to avoid clutter that
hides defects and their causes (Flynn et al., 1994; Schonberger,
2007). Another typical tool/technique-oriented QM practice is the
usage of quality information whose importance in QM has been
underlined by so many researchers (Ho et al., 2001; Forza, 1995;
Saraph et al., 1989) that it can be identified as one of the
fundamental dimensions in hard QM. Quality information provides

workers with timely and accurate information about both quality
performance and the operation of the manufacturing process to
assist in operational controls (Flynn et al., 1994; Forza, 1995).
Soft QM can be generally defined as the QM practices which are
directed toward involvement and commitment of management
and employees, training, learning, and internal cooperation or

teamwork – in other words, promoting the human aspects of the
system. As noted by Bowen and Lawler (1992), ultimately it is
“people that make quality happen”. Previous studies have captured soft QM broadly at organizational or strategic level by
including open organization (Powell, 1995), visionary leadership
(Anderson et al., 1995), shared vision (Dow et al., 1999), strategic
planning (Samson and Terziovski, 1999), etc., at interorganizational level by considering relationship with customer
and suppliers (Flynn et al., 1995), and at employee level by
embodying employee relations (Saraph et al., 1989). As our study
is conducted at an operational level, soft QM can be better
captured by employee-related factors.
Ahire et al. (1996a) consider three employee-related factors:
employee involvement, employee empowerment, and employee
training. This content and range are similar to the suggestion of
Martinez-Lorente et al. (2000) for measuring employee relations:
the use of improvement teams, suggestion schemes and training.
Following the same line, in our study, we used small group problem
solving, employee suggestion, and task-related training for employees
to capture the concept of soft QM. Small group problem solving
uses teamwork activities to solve quality problems (Flynn et al.,
1994) for improvement. The importance of teaming for joint
problem solving and quality improvement has been included in
several research works (Linderman et al., 2004; Dow et al., 1999;
Abraham et al., 1999). Employee suggestion encourages personnel
to make suggestions on how the process can be improved, by
referring to their direct experience (Forza and Salvador, 2001).
Implementation and feedback on these suggestions can help make
improvement and unleash the knowledge previously retained by
individuals. Task-related training for employees aims to update
employees' skill and knowledge in order to maintain a workforce
with cutting-edge skills and abilities (Flynn et al., 1994). This can

not only facilitate workers to better perform their tasks, but also
transform workers into flexible problem solvers and encourage
them to be involved with their jobs (Kaynak, 2003).
There are no consistent findings on the relationship between
hard QM and soft QM and their role in determining performance.
Scholars do not agree on which dimension of QM is more
important to quality performance. Studies such as Powell (1995)
and Dow et al. (1999) conclude that only tacit, intangible and
social practices (soft QM) combine to contribute to superior
quality outcomes, rather than QM tools and techniques (hard
QM). However, studies such as Forza and Filippini (1998) suggest
hard QM to be more important than human resources factor (soft
QM) in the achievement of quality performance. Also, there is no
agreement on the direct/indirect effect of hard/soft QM on quality
performance. Many insightful studies in the QM literature, such as
Ahire and Ravichandran (2001), Anderson et al. (1995), Flynn et al.
(1995), Kaynak (2003), all tend to model QM practices–performance relationships in the sequence from soft QM practices, hard
QM practices, up to quality performance. This approach assumes
complete mediation of hard QM between soft QM and quality
performance since the direct impact of soft QM on quality
performance is not considered. This assumption lacks rigorous
validation and the partial mediation of hard QM between soft QM
and quality performance needs to be examined, as argued by Ho
et al. (2001) and Rahman and Bullock (2005). However, Ho et al.
(2001) and Rahman and Bullock (2005) arrive at different results.
Ho et al. (2001) empirically support soft QM only has an indirect
impact on performance through hard QM (complete mediation),

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while Rahman and Bullock (2005) support the direct impact of soft
QM on performance as well (partial mediation).
We build on the classification of hard and soft QM discussed
above to introduce a set of hypotheses that link hard QM, soft QM,
quality performance and innovation performance.
2.3. Hypotheses development
According to this literature, QM practices that are more
technique and tool oriented such as process management, and
quality information fall under the category of hard QM. However,
upgrading technology and promoting hard QM may not be
sufficient to increase competitive advantage. Kochan et al. (1995)
argue that quality needs to be viewed not as a limited set of
technical engineering changes, but as part of a broader strategy of
an organizational change. The adoption and utilization of these
technique and tool for quality improvement highly rely on wellmotivated employees with good problem-solving ability and
systematic encouragement promoted by managers empowering
employees to apply their ability. These can be supported by soft
QM. Previous studies such as Ahire and Ravichandran (2001),
Anderson et al. (1995), Flynn et al. (1995), Kaynak (2003) all tend
to model QM practices–performance relationships in the sequence
from soft QM practices, hard QM practices, up to quality performance and empirically found that soft QM facilitates the implementation of hard QM. As such, we contend that a sound soft QM
system can nurture a corporate culture of autonomy, cooperation
and teamwork, which provides a firm support for the successful
implementation of QM techniques and tools. The following
hypothesis can therefore be suggested:
H1. Soft QM has a positive impact on hard QM.
The relationship between QM practices and quality performance has been well documented in the extensive QM literature,

such as Flynn et al. (1995), Powell (1995), Dow et al. (1999),
Samson and Terziovski (1999), Forza and Filippini (1998), and
Kaynak (2003). TOM literature suggests that hard QM such as
striving for the reduction of process variance, making full use of
quality information, etc., in fact have a profound impact on
organizational performance. By identifying problem areas in
production and taking corrective actions to eliminate the quality
problems through process management, the amount of scrap and
rework generated will decrease, which directly leads to better
conformance quality (Ahire and Dreyfus, 2000; Flynn et al., 1995;
Kaynak, 2003). The use of quality information should also have a
direct effect on quality performance by informing the operators
and engineers about defective parts immediately so that corrective
actions can be taken timely to remedy problems before the
process drifts out of control, producing defects (Flynn et al.,
1995; Kaynak, 2003).
Direct impact of soft QM on organizational performance has
been demonstrated by empirical studies such as Powell (1995),
Ahire et al. (1996b), and Dow et al. (1999). Through a study of 39
QM companies in the US, Powell (1995) examines the relationship
of each of 12 QM factors. The results indicate that QM success is
dependent on more intangible factors (soft QM), rather than on
the more tangible factors (hard QM) such as zero defects mentality, and process improvement. Ahire et al. (1996a) draw a similar
conclusion in their study of automobile manufacturing and component companies in the US. They conclude that product quality is
highly correlated with elements of soft QM, such as employee
empowerment, employee training and employee involvement.
Empirical findings from the study of Australian manufacturing
companies conducted by Dow et al. (1999) also suggest that out of
a total of nine QM factors, only three soft aspects of QM practices

5

have a significant positive association with quality performance.
Taking a resource-base perspective, the intangible and behaviorally oriented elements that are embodied in soft QM might not be
readily imitable by QM adopters since they may require a
substantial change in corporate culture, and thus can directly yield
a superior performance for the companies having these intangible
factors embedded into their corporate culture. The following
hypotheses can be proposed:
H2. Hard QM has a positive impact on quality performance.
H3. Soft QM has a positive impact on quality performance.
Several empirical studies have shown that hard QM can have a
positive impact on innovation (Flynn, 1994; Kim et al., 2012;
Perdomo-Ortiz et al., 2006). Kim et al. (2012) argue that by
implementing QM tools, a firm can identify potential innovation
areas, develop innovation plans, and produce innovative products
and processes. Effective management of processes encourages
firms to develop routines that are formed by a set of best practices,
which can be used to establish a learning base and support
innovative activities (Perdomo-Ortiz et al., 2006; Peng et al.,
2008). Effective use of quality information offers the opportunities
for identifying non-value-added process, and helps employees
when modifying and improving processes (Kaynak, 2003). Flynn
(1994) demonstrates that receiving immediate and useful feedback from the manufacturing process is instrumental in speeding
new product to the market. Along the same line, Miller (1995)
found that managing quality information is the most important
QM practice that can be applicable to innovation activities.
Sitkin et al. (1994) argue hard QM focusing on cybernetic
control system is tightly related to the achievement of conformance, and soft QM which facilitates sharing of knowledge and
skills can be expected to associate with innovation. Soft QM which

promotes employee empowerment, involvement and teamwork is
highly related to TQL, and can be expected to contribute to
innovation. Soft QM enables open communication and supports
creative idea suggestion, which is essential to innovate. It can be
argued that soft QM can create a favorable and fertile atmosphere
or platform for developing innovation. As noted by Zairi (1994),
QM has “given organizations the impetus and commitment
required for establishing climates of never-ending innovation or
innovativeness” (p. 28). Empirical evidence provided by Prajogo
and Sohal (2004) confirms this favorable effect. They conclude that
leadership and people management are related to the greater
novelty of the product innovation. Flynn (1994) also highlights the
importance of soft QM which can help establish teamwork,
encourage creative ideas from employees, and promote communication environment in achieving fast product innovation. This
leads to the following hypotheses:
H4. Hard QM has a positive impact on innovation performance.
H5. Soft QM has a positive impact on innovation performance.
According to the perspective of cumulative capabilities, capabilities are layered upon each other, and are mutually reinforcing
(Boyer and Lewis, 2002). There are a number of researchers who
have made an effort to develop a sequential model for cumulative
capabilities (Ferdows and De Meyer, 1990; Swink and Way, 1995;
Schmenner and Swink, 1998). One of the commonalties between
these sequential models is that quality is viewed as the foundation
for the development of cumulative capabilities. Quality performance is a precondition for the development of other strategic
thrusts. Flynn (1994) notes that firms which use product innovation as a competitive weapon would fall short of achieving
potential market success with a poor quality. Besides, quality
performance reflects the cumulative efforts firms have strived to

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6

improve quality in the past. While firms may enjoy improved
innovation performance through implementing QM practices as an
expedient measure, enhancing innovation performance would also
need to keep on doing QM practices until remarkable results
(superior quality performance) are achieved. Prajogo and Sohal
(2003) empirically demonstrate a strong relationship between
quality performance and innovation performance in terms of both
product innovation and process innovation.
Thus, we hypothesize:
H6. Quality performance has a positive impact on innovation
performance.
The structural model presented in Fig. 1 shows the relationships proposed above.

the structural relationship between management practices and
performance to have remained stable in the last decade and that
the findings from the study will be relevant to current business
and management practice.
All plants in the sample represented different parent corporations. Three hundred and sixty-six plants were solicited for
participation by calling or personal visit. Two hundred and
thirty-eight plants agreed to participate and each plant received
a batch of questionnaires. The question items were assigned to
multiple questionnaires and distributed to the appropriate respondents. For comprehensive details on HPM survey, please refer to
Schroeder and Flynn (2001), Peng et al. (2008), etc. Table 1
summarizes the profile of the sample by industry and country.
3.2. Measures

3. Research methodology
This study uses a survey research method to examine the
hypothesized relationships between QM practices and innovation
performance. Description about the survey sample, measures and
data testing is provided below.
3.1. Sample
Data used in this study were collected through an international
joint research named High Performance Manufacturing (HPM),
round 3. This project aims to study management practices and
their impact on plant performance within global competition. The
sample consists of 238 manufacturing plants which are both
traditional and world-class plants, and was stratified by industry
and nation. There are eight countries included in the sample: the
United States, Japan, Italy, Sweden, Austria, Korea, Germany and
Finland. The three industries chosen are electrical & electronics,
machinery, and automobile, since they were industries in transition, where a great deal of variability in performance and practices
was expected to be present (Schroeder and Flynn, 2001). Even
though the data was collected during 2003–2005, we can expect

Fig. 1. Conceptual model.

To operationalize hard QM and soft QM, we identify suitable
measurement scales from the HPM database that would be consistent with the meaning of the constructs. Following the literature
review conducted in Section 2.2, hard QM is proposed as a multidimensional construct consisting of Process management and Quality
information. Three individual measurement scales, Process control,
Preventive maintenance, and Housekeeping, are used to measure
Process management which is constructed as a super-scale. Three
measurement scales are developed to examine soft QM – Small group
problem solving, Employee suggestion, and Task-related training for

employees. Thus, in total four measurement scales are identified to
measure hard QM, along with three measurement scales for soft QM.
These seven measurement scales are measured through perceptual
questions over seven points on the Likert scale (1¼Strongly disagree,
4¼Neither agree nor disagree, 7¼ Strongly agree). Each of these
measurement scales has multiple respondents from the same plant.
These respondents are from six positions: direct workers, human
resource manager, quality manager, supervisors, process engineer,
and plant superintendent.
Quality performance has been reflected and measured in various
ways in past empirical studies on QM. One well known work was
carried out by Garvin (1987), which proposes eight dimensions of
product quality. Among the eight dimensions, conformance is the
primary dimension measuring quality, having impact on performance,
durability and reliability. In this study, we measure quality performance by conformance which is the most basic among quality criteria.
Conformance is defined as the level of conformity to specifications
which indicates how well the actual product conforms to the design
once it has been manufactured. This measurement is linked to the
production point of view and to some extent is determined by defect
rates, new product yield, scrap and rework, etc.
Previous studies on organizational innovation also show variations in measuring innovation performance in organizations.
Researchers have tried to distinguish different types of innovation,
and a number of typologies of organizational innovation have been
proposed (e.g. Daft, 1978; Dewar and Dutton, 1986; Ettlie et al.,
1984). Three typologies have emerged from past research and
gained the most attention, with each centering on a pair of types
of innovation: administrative and technical, product and process,

Table 1
Profile of sample plants.

Country

Electrical & electronic
Machinery
Automobile
Total

Total

Austria

Finland

Germany

Italy

Japan

Korea

Sweden

USA

10
7
4
21

14
6
10
30

9
13
19
41

10
10
7
27

10
12
13
35

10
10
11
31

7
10
7
24

9
11
9
29

79
79
80
238

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and radical and incremental. In this study, we refer to the typology
of product and process innovation, which is the most traditional
one, and we particularly focus on product innovation instead of
lumping all kinds of innovation into one single indicator. According to Ettlie (1990), product innovations are new products or
services introduced to meet an external user or market need. We
measure product innovation by two criteria: speed of new product
introduction, and product innovativeness.
Both quality and innovation performance measures are evaluated
based on a five-point scale, where a high score indicates that plant
manager perceives that the plant has been relatively successful
pursuing these performance indicators compared to its competitors.

3.3. Testing measurement scales
Three steps are executed in the validation process for the measurement scales: reliability, content validity and construct validity. The
reliability and validity tests for the four measurement scales for hard

QM from Process control to Quality information, three measurement
scales for soft QM from Small group problem solving to Task-related
training for employees, as well as Innovation performance in Table 2 are
conducted on a dataset at an individual level consisting of response
from each respondent. Reliability is broadly defined as the degree to
which scales are free from error and therefore consistent (Nunnally
and Bernstein, 1994). Reliability is operationalized through the internal
consistency method. Cronbach's alpha is used as the reliability
indicator and a value of 0.6 or above is considered acceptable. We
eliminate the items that do not strongly contribute to Cronbach's
alpha and whose content is not critical. Table 2 shows the Cronbach's
alpha value for all scales. As can be seen, most of the scales exceed the
lower limit by a substantial margin, indicating a good reliability of the
measurement scales.
Content validity is ensured through an extensive review of
literature and empirical studies. Construct validity measures the
extent to which the items in a scale all measure the same
multivariate construct. Factor analysis is used to establish construct validity, and the results demonstrate that all scales are onedimensional. The eigenvalues for each measurement scale are
presented in Table 2 and the factor loadings by item are shown
in the Appendix. The eigenvalue of the first factor for each scale is
above the minimum eigenvalue of 1.00, and all factor loadings
meet the criterion of larger than 0.4. Thus, all items contribute to
their respective scales, indicating a good construct validity.
After establishing satisfactory measurement performance, a
dataset at the plant level is aggregated by averaging the item
scores for each measurement scale. All scale responses are averaged into a single plant response per scale. Aggregating respondents across respondent category and collecting the same data
from different respondents can help address the issue of common
method bias. Based on this plant-level data, the super-scale Process
Management consisting of Process control, Preventive maintenance,
Table 2

Summary of measurement analysis.
Measure name

Process control
Preventive maintenance
Housekeeping
Quality information
Small group problem solving
Employee suggestion
Task-related training for employees
Innovation performance
Process management

Mean S.D.

4.811
4.858
5.516
4.878
5.046
5.171
5.187
3.448
4.987

0.827
0.666
0.687
0.843
0.640

0.624
0.625
0.877
0.577

Cronbach
alpha

Eigenvalue
(% variance)

0.824
0.675
0.817
0.791
0.824
0.834
0.792
0.681
0.696

2.964(59)
2.202(44)
2.847(57)
2.759(55)
3.211(54)
3.025(60)
2.477(62)
1.517(75)
1.878(63)

7

and Housekeeping is subject to the same process of testing
reliability and validity as above. This super-scale is found to be
reliable and valid as shown at the bottom of Table 2, and then it is
computed by averaging the scores of its three measurement scales.

4. Hypothesis testing
Hypotheses are tested using AMOS program. A number of
indices are used to determine the fit of the data to the model
(e.g. χ2/df, CFI, RMSEA and PNFI). The overall fit statistics for the
hypothesized model are χ2 ¼18.102, df ¼10, χ2/df ¼1.810, p ¼0.053,
CFI¼0.988, PNFI¼ 0.417, and RMSEA¼ 0.047. The index χ2/df ratio
which is below the threshold level of 3 with a p value more than
0.05 indicates a good model fit. Our CFI, which has the value of
0.988, is optimal, since it has to be greater than 0.9 for the model
to be considered very good (Bentler, 1990). PNFI should be higher
than 0.5 for the model to be considered very good; our results
(PNFI¼0.417) are close to this criterion. RMSEA is another fit
statistics which adjust the sample discrepancy function by degree
of freedom. The RMSEA has been recognized as one of the most
informative criteria in SEM (Byrne, 2001) and values of 0.05 or less
indicate good fit; on this criterion, our model (RMSEA ¼0.047) fits
well. From these fit statistics, it is concluded that the overall model
demonstrates a good model fit.
In addition to a good fit of the structural model, a good
structural equation model needs to have a good measurement
model. Table 3 presents the estimated values of the standardized
path coefficients of all measurement constructs to their related

latent constructs, and the relative p-value. Some constructs do not
present p-values in that the relative path coefficient is fixed at 1 as
suggested in the SEM theory. The three constructs of hard QM, and
those of soft QM all have significant estimates of the standardized
coefficients between 0.691 and 0.888, demonstrating good measurement models of hard QM and of soft QM.
Table 4 presents the analysis results of the structural model.
Two paths, from soft QM to Quality Performance (standardized
Table 3
Results for the measurement model.
Construct
name

Measure variable

Standardized
coefficient

p-Value

Hard QM

Process management
Quality information

0.888
0.783

–
0.000

Soft QM

Small group problem solving
Employee suggestion
Task-related training for
employees

0.861
0.772
0.691

–
0.000
0.000

Table 4
Results for the structural model.
Causing
construct

Caused construct

Hypothesis Standardized
coefficient

p-Value

Soft QM
Hard QM

Hard QM
Quality
performance
Quality
performance
Innovation
performance
Innovation
performance
Innovation
performance

H1
H2

0.900
0.294

0.000
0.000

H3

Not supported

H4

0.141

H5

Not supported

H6

0.308

Soft QM
Hard QM
Soft QM
Quality
performance

0.047

0.000

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8

coefficient¼0.064; p-value¼ 0.799) and from soft QM to Innovation
Performance (standardized coefficient¼ 0.025; p-value¼0.918), are
insignificant. Among six hypotheses, four are supported and two are
rejected. The results show that soft QM has a positive impact on hard
QM, suggesting support for H1. The results also indicate that hard QM
has a significant impact on both quality performance and innovation

performance, supporting H2 and H4. However, soft QM has no direct
impact on either quality performance or innovation performance,
suggesting rejecting H3 and H5, which is surprising. This result might
be due to our study scope particularly focusing on plant operations, as
further discussed in the next section. Quality Performance is also
found to directly influence innovation performance, which provides
support for H6. Fig. 2 presents the summary of the findings above.

5. Discussion and implications
In this section, we discuss the main findings and implications
for management. First, the results of this study reveal that hard
QM completely mediates the relationship between soft QM and
quality performance (support for H1 and H2, and rejection against
H3). Although some researchers found that soft QM had a direct
effect on performance (Rahman and Bullock, 2005), our findings
are consistent with the results suggested by Ho et al. (2001).
Indeed, many insightful empirical studies on the impact of QM
practices on performance have modeled the relationship between
QM practices and performance in the sequence from soft QM to
hard QM then to quality performance, such as Anderson et al.
(1995), Flynn et al. (1995), Forza and Filippini (1998), and Kaynak
(2003). Our findings provide a strong support for the assumption
of complete mediation underlying these studies, though Ho et al.
(2001) argue the possibility that hard QM practices partially
mediate the relationship between soft QM practices and performance. Firm-level studies, such as Rahman and Bullock (2005),
tend to suggest the direct impact of soft QM on performance.
However, at the plant level, hard QM could exhibit a dominant
influence on quality performance in terms of conformance. Therefore, hard QM becomes a complete mediator between soft QM and
quality performance. Successful implementation of hard QM, in
turn, is achieved through well-established soft QM.

Another interesting insight gained from this study considers
the different ways of each dimension of QM in influencing
innovation performance. This has been suggested by a few of
previous studies, but there exists disagreement regarding which
dimension is more effective in determining innovation performance. While some studies (e.g. Prajogo and Sohal, 2003; Feng
et al., 2006) contend that only soft dimension (leadership and
people management) can foster innovation, Perdomo-Ortiz et al.
(2006) assert that both hard and soft dimensions (e.g. process

Fig. 2. SEM result.

management and human resource management) play a significant
role in building innovation capability. However, Kim et al. (2012)
demonstrate a dominant role of process management (hard QM)
when supported by other interrelated quality practices in determining innovation.
Our study is significantly different from these studies in that it
distinguishes hard QM from soft QM. It also verifies the QM–
innovation relationship on a global basis rather than a single
region. Our findings align with Kim et al.'s (2012), highlighting the
importance of hard QM to innovation and the supporting role of
soft QM. Results reveal that hard QM plays an essential role in
determining innovation performance. Supported by soft QM, hard
QM can affect innovation performance not only directly but also
indirectly through the accumulative effect of improved quality.
This finding can be supported by several arguments in the
literature. Since hard QM emphasizes the use of quality techniques
and tools, it helps organization to reestablish order – getting the
system in control through the reduction of variance. Spencer
(1994) notes that once the system is stable and in control, it is
possible to learn how to improve, leading to fostering a learning

base. A set of routines established through the implementation of
hard QM can support innovation activities because routine-based
organizations pay more attention to vital processes and avoid
activities that do not add value (Hoang et al., 2006). However, the
direct effect of soft QM on innovation performance lacks support
by our empirical evidence. This might be due to our operationfocused scope, under which soft QM is particularly measured at
the operational level rather than at the firm level. Firm-level soft
QM practices, such as top management leadership for quality
initiatives and organizational-wide training and learning, can
instantly disseminate knowledge across functions and inspire
creative ideas, which could be expected to directly yield improved
innovation performance. However, at the operational level, the
strengthened human power through implementation of soft QM is
first converted into productivity in terms of improved quality
performance, and then would gradually become a solid foundation
fostering innovation. The choice of operational level allows us to
have higher scrutiny of QM practices, since QM is often implemented on the plant level (Flynn et al., 1994). However, this also
results in a limitation of scope. Future research with a wider scope
could complement our results.
Third, this study also provides support for the notion that quality
must be attained first as a sequential precedent to other strategic
capabilities (Ferdows and De Meyer, 1990). Quality performance has
a mediating effect on the relationship between hard QM and
innovation performance. The mediating effect is partial because hard
QM has a direct impact on innovation performance. The continuously
improved quality performance would lead to the achievement of
other strategic competitive priorities in a cumulative fashion.
Although QM practices are originally intended to enhance quality
performance, the achieved quality performance can result in the
improvement of innovation performance. This result can be considered as a secondary but indispensible effect of the implementation of

QM practices. Therefore, quality and innovation are not a matter of
trade-off, but they can coexist in a cumulative improvement model
with quality as a foundation.
Managers can find useful reference from this study. Our
findings respond to the concerns that managers have on whether
QM should be continued as a future management paradigm in the
increasingly competitive and fast changing environment. The
empirical results suggest that QM implementation can affect
innovation which allows firms to adapt to the market changes.
This is an encouraging finding for practicing mangers as it
demonstrates the simultaneous pursuit of multiple competitive
advantages in both quality and innovation. QM implementation is
able to transform firms to be ambidextrous in both efficiently

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managing current market demands and adaptively responding to
market changes coming in the future. Firms should not abandon
QM practices, even though some quality aspects such as conformance to specifications are no longer considered as a winning
criterion in some industries. To obtain innovation performance
through QM, managers are encouraged to leverage the different
roles played by different dimensions of QM in determining
innovation performance. Firms would foster innovation through
QM by emphasizing on the establishment of a routine base
through QM tools and techniques, which can be facilitated by
the concurrent use of teamwork, training, employee empowerment and problem-solving approaches. Additionally, the role of
quality performance as a partial mediator between QM practices

and innovation performance has a valuable managerial implication. Quality performance reflects the cumulative efforts firms
have strived for quality improvement in the past. The cumulative
effect of quality performance on innovation performance would
suggest that managers devote continuous efforts involving
employees into quality improvement initiatives in order to foster
innovation eventually. Firms should not just rush to implement
QM practices for short periods expecting instant benefits on
innovation performance, but need to stay grounded to keep
implementing QM until remarkable quality performance is
achieved.
All together, achievement of innovation through QM requires a
sound quality system in place integrating a set of QM practices and
corresponding performance measures. At the end, innovation is
not a fancy achievement occasionally coming from a whim of some
talent, but it stems from a solid foundation where employees have
thorough understanding of process, go deep into the root cause of
quality problems, and persistently look for solutions to improve.
As a sound quality system is the very mechanism of laying such a
foundation, firms that have their feet on the ground with much
attention to their process could more easily enjoy the benefit of
innovation in addition to quality foundation.

6. Limitations and future research
Several limitations to this study should be taken into consideration. First, the data we used to conduct analysis was collected
from 2003–2005. The implementation level of QM has become
more widespread and pervasive across business organizations
since these data were collected. However, we argue that the
structural relationships between management practices and performance are likely to have remained fairly constant. We do note
below the desirability of further longitudinal studies to understand these issues further. Another limitation is that this study
utilizes survey-based subjective and qualitative data. Although we

address the issue of common method bias through the use of
multiple respondents, this study relies on the perceptions of the
respondents to operationalize the survey instrument. This may
have introduced bias in to the data, which could cause potential
concerns regarding generalizability, reliability, and validity. Third,

9

many parts of the discussion in this research tend to be biased
towards manufacturing operations. A large portion of the literature addressing the theoretical and empirical aspects of the
research topic has been derived from the manufacturing point of
view, and the data used in this study is coming from only
manufacturing plants. The findings and conclusion could not be
generalized to the firm level or the service industry at the current
stage. Future research can expand to a service setting.
While this study has contributed to the body of knowledge
about the relationship between QM and innovation, we suggest
that the following areas could further enhance the understanding
about this relationship. First, an examination of the potential
effects of contingency factors on the proposed framework could
provide a fruitful field of research endeavor. Contingency factors
such as environmental uncertainty, organizational culture, and
organization's strategy can be investigated. Second, it would be
valuable to conduct a longitudinal study within organizations to
observe the achievement of innovation performance through the
cumulative effect of QM implementation. Third, future studies can
also examine the QM–innovation relationship with an expanded
scope such as strategic level, firm level, or even inter-firm level,
which would generate more interesting results complementing
with ours.

7. Conclusions
Based on a multi-dimensional view of QM, this study has
provided empirical evidence to resolve some of the controversies
that appear in the literature concerning the relationship between
QM and innovation. The findings support the notion that QM
provides a foundation to achieve a competitive position in innovation, and suggest the importance of continued efforts with QM
practices. Innovation can be achieved through quality in a cumulative fashion, which is consistent with the proposition by the
well-known sand cone model. By looking at QM from two
dimensions, hard and soft QM, this study further contributes to
the understanding of the different roles played by different QM
dimensions in determining innovation. It highlights the significance of the routine-based approach through emphasis on the
implementation of hard QM to foster a learning base leading to
innovation, with soft QM playing a supporting role behind to
enable this effect to work.

Acknowledgments
The authors appreciate the financial support for this research
from the Japan Society for the Promotion of Science by Grant-inAids for Scientific Research, Nos. 22330112 and 25245050.

Appendix. Question items of measurement scales
Factor loadings are given in parentheses following each item.
Process control
1. Processes in our plant are designed to be “foolproof” (0.581)
2. A large percent of the processes on the shop floor are currently under statistical quality control (0.815)
3. We make extensive use of statistical techniques to reduce variance in processes (0.825)
4. We use charts to determine whether our manufacturing processes are in control (0.734)
5. We monitor our processes using statistical process control (0.862)

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Preventive maintenance
1. We upgrade inferior equipment, in order to prevent equipment problems (0.689)
2. In order to improve equipment performance, we sometimes redesign equipment (0.542)
3. We estimate the lifespan of our equipment, so that repair or replacement can be planned (0.748)
4. We use equipment diagnostic techniques to predict equipment lifespan (0.734)
5. We do not conduct technical analysis of major breakdowns (0.578)
Housekeeping
1. Our plant emphasizes putting all tools and fixtures in their place (0.698)
2. We take pride in keeping our plant neat and clean (0.811)
3. Our plant is kept clean at all times (0.856)
4. Employees often have trouble finding the tools they need (0.586)
5. Our plant is disorganized and dirty (0.791)
Quality information
1. Charts showing defect rates are posted on the shop floor (0.758)
2. Charts showing schedule compliance are posted on the shop floor (0.754)
3. Charts plotting the frequency of machine breakdowns are posted on the shop floor (0.692)
4. Information on quality performance is readily available to employees (0.781)
5. Information on productivity is readily available to employees (0.726)
Small Group Problem Solving
1. During problem solving sessions, we make an effort to get all team members' opinions and ideas before making a decision (0.643)
2. Our plant forms teams to solve problems (0.805)
3. In the past three years, many problems have been solved through small group sessions (0.786)
4. Problem solving teams have helped improve manufacturing processes at this plant (0.775)
5. Employee teams are encouraged to try to solve their own problems, as much as possible (0.652)

6. We don't use problem solving teams much, in this plant (0.710)
Employee suggestion
1. Management takes all product and process improvement suggestions seriously (0.809)
2. We are encouraged to make suggestions for improving performance at this plant (0.780)
3. Management tells us why our suggestions are implemented or not used (0.764)
4. Many useful suggestions are implemented at this plant (0.819)
5. My suggestions are never taken seriously around here (0.711)
Task-related training for employees
1. Our plant employees receive training and development in workplace skills, on a regular basis (0.854)
2. Management at this plant believes that continual training and upgrading of employee skills is important (0.779)
3. Employees at this plant have skills that are above average, in this industry (removed)
4. Our employees regularly receive training to improve their skills (0.879)
5. Our employees are highly skilled, in this plant (0.608)
Innovation performance
Please circle the number which indicates your opinion about how your plant compares to its competition in your industry, on a
global basis. (5 ¼Superior, 4 ¼Better than average, 3 ¼Average or equal to the competition, 2 ¼Below average, 1 ¼Poor, low end of
industry)
1. Speed of new product introduction (0.871)
2. Product innovativeness (0.871)

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