DSpace at VNU: The impact of hard and soft quality management on quality and innovation performance: An empirical study

Empirical studies have rarely investigated the mediating effect of quality performance on the relationship between QM practices and innovation performance.. Furthermore, the results rega

The impact of hard and soft quality management on quality

Jing Zenga,n, Chi Anh Phanb, Yoshiki Matsuic

a

International Graduate School of Social Sciences, Yokohama National University, 79-4 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan

b 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

a r t i c l e i n f o

Article history:

Received 17 June 2014

Accepted 5 July 2014

Keywords:

Soft QM

Hard QM

Quality performance

Innovation

a b s t r a c t 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 problem-solving approaches as an underlying support

& 2014 Elsevier B.V All rights reserved

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

onflexibility, 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–

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, path-breaking 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 startedfive 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

Contents lists available atScienceDirect

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

http://dx.doi.org/10.1016/j.ijpe.2014.07.006

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

☆ 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

Manage-ment 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.: þ81 453393734.

E-mail addresses: zengzx1028@yahoo.co.jp (J Zeng),

anhpc@yahoo.com (C Anh Phan), ymatsui@ynu.ac.jp (Y Matsui).

advantages, particularly innovation, in future competitive

envir-onment 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

inno-vation (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;

Santos-Vijande 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

exten-sive context across eight countries

Previous literature on QM has proposed different dimensions

embodied by QM As noted byWilkinson (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 classi

fi-cation, 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

perfor-mance and innovation perforperfor-mance 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 How does hard QM relate to soft QM?

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

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

4 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 thefirm 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 rela-tionship 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 mainfindings 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 streamlin-ing 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 “narrow-minded” to current product and services rather than making breakthrough improvements to explore customers' latent needs However, positive viewpoint contends that companies embra-cing QM in their system and culture can provide a fertile environ-ment 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 struc-tures; and creative staff, which are in agreement with the QM

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

changes in the organizational structure, making itflexible (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

auton-omy, 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,

mak-ing the QM–innovation relationship more ambiguous The

con-ventional 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 thusfirms cannot achieve high levels of

performance for multiple competitive priorities simultaneously

However, the cumulative or“sandcone” model (Ferdows and de

dimensions of performance concurrently because the

improve-ments reinforce each other in a cumulative fashion Some

researchers such as Leong et al (1990), Corbett and Van

Wassenhove (1993), andNoble (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

perfor-mance 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

perfor-mance 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 byFlynn (1994)reports on the relationship between

QM and the speed of product innovation The findings

demon-strate that quality foundation and organizational infrastructure

can support fast product innovation McAdam et al (1998)

compare QM (presented by continuous improvement) to

innova-tion 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 Australianfirms, found a positive

relationship between QM and innovation performance However,

manufacturing organizations, could notfind 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

byKim et al (2012)highlight the critical role of process manage-ment 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

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–innova-tion relaQM–innova-tionship 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 pro-duct quality” is closely related to the strategic needs aiming at gaining knowledge This is because improvement in quality requiresfirms to increase their technological knowledge and their ability to understand and solve customers' problems This knowl-edge and learning capability can be then used to build superior new product development capability, leading to improved innova-tion output

2.2 Hard QM and soft QM

possible explanation of the contrary effect of QM on innovation would be the different ways of QM implementation in afirm 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 relation-ship between QM and innovation.Prajogo and Sohal (2004)divide

QM into two dimensions: mechanistic (customer focus and pro-cess management) and organic (leadership and people manage-ment) 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

customer focus has traditionally been considered to be one of the“intangible” elements of QM which is more soft (Anderson and

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

dimen-sions 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

infra-structure 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

perfor-mance.Kochan et al (1995) argue there are two ways of

imple-menting 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 informationflows and information

tech-nologies 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

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

prac-tices (hard QM) is process management which has been covered by

most major studies on QM such asSaraph et al (1989),Anderson

et al (1995), andFlynn 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 toFlynn 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

equip-ment 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;

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 cap-tured 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 inter-organizational 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 consistentfindings 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 asPowell (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 asForza 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 –perfor-mance 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 byHo

et al (2001)andRahman and Bullock (2005) However,Ho et al (2001)andRahman 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),

whileRahman 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

well-motivated 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

perfor-mance and empirically found that soft QM facilitates the

imple-mentation 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 afirm 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

perfor-mance 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

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), andDow 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

mental-ity, and process improvement.Ahire et al (1996a)draw a similar

conclusion in their study of automobile manufacturing and

com-ponent 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 byDow et al (1999)also suggest that out of

a total of nine QM factors, only three soft aspects of QM practices

have a significant positive association with quality performance Taking a resource-base perspective, the intangible and behavio-rally 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;

implementing QM tools, afirm 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

feed-back 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

control system is tightly related to the achievement of confor-mance, 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 byZairi (1994),

QM has “given organizations the impetus and commitment required for establishing climates of never-ending innovation or innovativeness” (p 28) Empirical evidence provided byPrajogo 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 commu-nication 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, cap-abilities 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;

these sequential models is that quality is viewed as the foundation for the development of cumulative capabilities Quality perfor-mance is a precondition for the development of other strategic thrusts.Flynn (1994)notes thatfirms which use product innova-tion 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

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

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 inFig 1 shows the

relation-ships proposed above

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

transi-tion, 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

the structural relationship between management practices and performance to have remained stable in the last decade and that thefindings from the study will be relevant to current business and management practice

All plants in the sample represented different parent corpora-tions 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 respon-dents 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

To operationalize hard QM and soft QM, we identify suitable measurement scales from the HPM database that would be consis-tent with the meaning of the constructs Following the literature review conducted inSection 2.2, hard QM is proposed as a multi-dimensional 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, 7¼Strongly 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 byGarvin (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 perfor-mance by conforperfor-mance 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 varia-tions in measuring innovation performance in organizavaria-tions 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, Fig 1 Conceptual model.

Table 1

Profile of sample plants.

Austria Finland Germany Italy Japan Korea Sweden USA

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

Accord-ing 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 afive-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

measure-ment 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 inTable 2are

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 2shows 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

con-struct validity, and the results demonstrate that all scales are

one-dimensional The eigenvalues for each measurement scale are

presented inTable 2and the factor loadings by item are shown

in the Appendix The eigenvalue of thefirst 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

aver-aged into a single plant response per scale Aggregating

respon-dents 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,

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 ofTable 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 overallfit statistics for the hypothesized model areχ2

¼18.102, df¼10,χ2

/df¼1.810, p¼0.053, CFI¼0.988, PNFI¼0.417, and RMSEA¼0.047 The indexχ2/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 goodfit; on this criterion, our model (RMSEA¼0.047) fits well From thesefit statistics, it is concluded that the overall model demonstrates a good modelfit

In addition to a good fit of the structural model, a good structural equation model needs to have a good measurement model.Table 3presents 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 mea-surement 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 2

Summary of measurement analysis.

Measure name Mean S.D Cronbach

alpha

Eigenvalue (% variance) Process control 4.811 0.827 0.824 2.964(59)

Preventive maintenance 4.858 0.666 0.675 2.202(44)

Housekeeping 5.516 0.687 0.817 2.847(57)

Quality information 4.878 0.843 0.791 2.759(55)

Small group problem solving 5.046 0.640 0.824 3.211(54)

Employee suggestion 5.171 0.624 0.834 3.025(60)

Task-related training for employees 5.187 0.625 0.792 2.477(62)

Innovation performance 3.448 0.877 0.681 1.517(75)

Process management 4.987 0.577 0.696 1.878(63)

Table 3 Results for the measurement model.

Construct name

Measure variable Standardized

coefficient

p-Value

Hard QM Process management 0.888 –

Quality information 0.783 0.000 Soft QM Small group problem solving 0.861 –

Employee suggestion 0.772 0.000 Task-related training for

employees

0.691 0.000

Table 4 Results for the structural model.

Causing construct

Caused construct Hypothesis Standardized

coefficient

p-Value

Soft QM Hard QM H1 0.900 0.000 Hard QM Quality

performance

Soft QM Quality

performance

H3 Not supported Hard QM Innovation

performance

Soft QM Innovation

performance

H5 Not supported Quality

performance

Innovation performance

coefficient¼0.064; p-value¼0.799) and from soft QM to Innovation

Performance (standardized coefficient¼0.025; 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 2presents the summary of thefindings above

5 Discussion and implications

In this section, we discuss the mainfindings 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), ourfindings

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), andKaynak

(2003) Ourfindings provide a strong support for the assumption

of complete mediation underlying these studies, thoughHo et al

mediate the relationship between soft QM practices and

perfor-mance 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

There-fore, 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

perfor-mance 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

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 deter-mining 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 Ourfindings align withKim 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 operation-focused scope, under which soft QM is particularly measured at the operational level rather than at thefirm 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 imple-mented 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 attainedfirst 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 consid-ered 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 allowsfirms 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 Fig 2 SEM result.

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

con-formance 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

empower-ment and problem-solving approaches Additionally, the role of

quality performance as a partial mediator between QM practices

and innovation performance has a valuable managerial

implica-tion 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

consid-eration 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

per-formance are likely to have remained fairly constant We do note

below the desirability of further longitudinal studies to

under-stand 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,

many parts of the discussion in this research tend to be biased towards manufacturing operations A large portion of the litera-ture 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 thefirm 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 fruitfulfield 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 innova-tion, and suggest the importance of continued efforts with QM practices Innovation can be achieved through quality in a cumu-lative 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 signi fi-cance 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-in-Aids 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 shopfloor 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)

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 andfixtures 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 troublefinding 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 shopfloor (0.758)

2 Charts showing schedule compliance are posted on the shopfloor (0.754)

3 Charts plotting the frequency of machine breakdowns are posted on the shopfloor (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 (5¼Superior, 4¼Better than average, 3¼Average or equal to the competition, 2¼Below average, 1¼Poor, low end of industry)

1 Speed of new product introduction (0.871)

2 Product innovativeness (0.871)

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