the relationship between continuous improvement and rapid improvement sustainability 2014

8/19/2019 The Relationship Between Continuous Improvement and Rapid Improvement Sustainability 2014

1/20

This article was downloaded by: [West Virginia University]On: 04 May 2015, At: 08:34Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House37-41 Mortimer Street, London W1T 3JH, UK

Click for updates

International Journal of Production ResearchPublication details, including instructions for authors and subscription information:

http://www.tandfonline.com/loi/tprs20

The relationship between continuous improvement

and rapid improvement sustainabilityWiljeana J. Glover

a, Jennifer A. Farris

b & Eileen M. Van Aken

c

a Technology, Operations, and Information Management Division, Babson College, Babson

Park, MA, USAb Department of Industrial Engineering, Texas Tech University, Lubbock, TX, USA

c Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute

and State University, Blacksburg, VA, USA

Published online: 18 Dec 2014.

To cite this article: Wiljeana J. Glover, Jennifer A. Farris & Eileen M. Van Aken (2014): The relationship between

continuous improvement and rapid improvement sustainability, International Journal of Production Research, DOI:

10.1080/00207543.2014.991841

To link to this article: http://dx.doi.org/10.1080/00207543.2014.991841

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of tContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon ashould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveor howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any

form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

http://www.tandfonline.com/page/terms-and-conditionshttp://crossmark.crossref.org/dialog/?doi=10.1080/00207543.2014.991841&domain=pdf&date_stamp=2014-12-18http://www.tandfonline.com/page/terms-and-conditionshttp://www.tandfonline.com/page/terms-and-conditionshttp://dx.doi.org/10.1080/00207543.2014.991841http://www.tandfonline.com/action/showCitFormats?doi=10.1080/00207543.2014.991841http://www.tandfonline.com/loi/tprs20http://crossmark.crossref.org/dialog/?doi=10.1080/00207543.2014.991841&domain=pdf&date_stamp=2014-12-18


2/20

The relationship between continuous improvement and rapid improvement sustainability

Wiljeana J. Glover a

*, Jennifer A. Farris b

and Eileen M. Van Akenc

aTechnology, Operations, and Information Management Division, Babson College, Babson Park, MA, USA; b Department of Industrial Engineering, Texas Tech University, Lubbock, TX, USA; cGrado Department of Industrial and Systems Engineering, Virginia

Polytechnic Institute and State University, Blacksburg, VA, USA

( Received 30 August 2013; accepted 19 November 2014)

While rapid improvement efforts, e.g. Kaizen events, and continuous improvement efforts, i.e. kaizen, remain popular approaches to operational excellence, it is rare that organisations fully sustain change from these initiatives. The impact of both Kaizen events and kaizen may be substantially lower, if not entirely eliminated, after signicant time has elapsedfrom initial implementation of changes. In this paper, we examine how having a continuous improvement culture cansupport rapid improvement sustainability via an examination of the impact of Kaizen events several months after imple-mentation. Employing a dynamic capabilities perspective and using the institutionalisation of planned change framework,we empirically examine this relationship via a eld study of 65 Kaizen events in eight manufacturing organisations. In

short, we

nd that the extent to which work area employees exhibit peer learning, as well as awareness and responsibil-ity both inside and outside of their work area, and the extent to which changes are accepted are signicantly related tothe perceived impact of Kaizen events several months after implementation. This research adds to current understandingof Kaizen events and kaizen, providing evidence to guide the use of Kaizen events and to inform areas for futureresearch.

Keywords: lean production; teams; performance improvement sustainability; quality management; manufacturingcompanies; dynamic capabilities; institutionalising change

1. Introduction

Achieving and sustaining effective improvement efforts continues to be a cornerstone for successful organisations and

a focus of academic inquiry in the production research community (e.g. Hung, Ro, and Liker 2009; van Iwaarden

et al. 2008). In particular, lean production has received signicant attention to date (Chen, Li, and Shady 2010;

Hung, Ro, and Liker 2009; Modarress, Ansari, and Lockwood 2005; Shah and Ward 2007; Sugimori et al. 1977;Wan and Chen 2008). One improvement mechanism associated with lean production is the Kaizen event. A Kaizen

event is a ‘focused and structured improvement project, using a dedicated cross-functional team to improve a targeted

work area, with specic goals, in an accelerated timeframe’ (Farris et al. 2008, 10). Kaizen events are also known as

‘kaizen bursts’ (e.g. Anand et al. 2009), ‘kaizen blitzes’ (e.g. Cuscela 1998; Done, Voss, and Rytter 2011), ‘kaikaku’

(Browning and Heath 2009) and ‘rapid improvement workshops’ (e.g. Done, Voss, and Rytter 2011; Martin et al.

2009).

Kaizen events are related to, but distinct from, the lean principle of kaizen (Shah and Ward 2007). Literally

translated as ‘change for the better ’ (Emiliani 2006), the western translation of kaizen is continuous improvement (thus,

we use the two terms interchangeably in this paper), and refers to ‘a systematic effort to seek out and apply new ways

of doing work, i.e. actively and repeatedly making process improvements’ (Anand et al. 2009, 444). In practice, a

Kaizen event can be viewed as a technique or tool to implement the philosophical principle of kaizen (Shah and

Ward 2007).

The consideration of Kaizen events and kaizen in tandem draws an apparent paradox, i.e. implied discontinuity vs.continuous improvement (Schonberger 2007). The principle of kaizen typically focuses on a smooth, uninterrupted ow

of incremental improvements – truly continuous improvement – while Kaizen events (at least seemingly) focus on peri-

ods of rapid change followed by periods of relative stasis. Despite this potential contradiction, scholars suggest that the

use of Kaizen events for ‘ jolts’ of immediate improvement combined with other factors that support the principle of

kaizen may be an ideal approach to achieve sustained change in an organisation (Anand et al. 2009; Brunet and New

*Corresponding author. Email: [email protected]

© 2014 Taylor & Francis

International Journal of Production Research, 2014

http://dx.doi.org/10.1080/00207543.2014.991841

mailto:[email protected]://dx.doi.org/10.1080/00207543.2014.991841http://dx.doi.org/10.1080/00207543.2014.991841mailto:[email protected]


3/20

2003; Chen, Li, and Shady 2010; Glover et al. 2013; Hung, Ro, and Liker 2009; Schonberger 2007). However, as has

been found with other improvement approaches, e.g. (Schonberger 2007), the growing popularity of Kaizen events and

kaizen appears to have outpaced the empirical research and theory testing to fully understand their use and impact on

work areas and organisations. In particular, it can be dif cult for many organisations to sustain results after Kaizen

events (Bateman 2005; Done, Voss, and Rytter 2011; Friedli 2000), such that the impact of both Kaizen events and

kaizen may be lower after signicant time, e.g. one year has elapsed from the initial implementation (Burch 2008;

Laraia, Moody, and Hall 1999).While there have been previous studies that explore Kaizen event sustainability, most studies of Kaizen event sus-

tainability use single case studies (Doolen et al. 2008; Magdum and Whitman 2007; Patil 2003). Those that include

multiple Kaizen events primarily use qualitative data and call for further quantitative study and theory testing (Bateman

2005; Done, Voss, and Rytter 2011; Patil 2003). The few quantitative studies have smaller samples (Burch 2008) or

focus on human-oriented outcomes as opposed to operationally oriented outcomes (Glover et al. 2011). Furthermore,

most of the continuous improvement literature tends to focus on an improvement programme as a whole (Glover et al.

2013), rather than the inuence of individual change interventions, e.g. Kaizen events.

We take a dynamic capabilities perspective to explain how continuous improvement culture supports the sustainabil-

ity of Kaizen events after implementation. Evolving from the resource-based view of the rm, dynamic capabilities are

the rm’s strategically responsive, identiable processes that integrate, build, recongure, gain, and release internal and

external resources and competences to address rapidly changing environments or ecosystems (Eisenhardt and Martin

2000; Helfat and Winter 2011; Szulanski 1996; Teece, Pisano, and Shuen 1997). Kaizen culture serves as a dynamic

capability when it provides a comprehensive infrastructure that enables an organisation to coordinate its resourcestowards systematically improving processes and sustaining improvement outcomes (Anand et al. 2009; Bessant, Caffyn,

and Gallagher 2001; Bessant and Francis 1999; Oxtoby, McGuiness, and Morgan 2002; Teece and Pisano 1994).

Scholars suggest that Kaizen events may serve as a supportive mechanism in conjunction with a kaizen culture to

further drive an organisation’s sustained improvement efforts (Anand et al. 2009; Brunet and New 2003); however, there

has been limited empirical study of this concept.

The objective of this research, therefore, is to identify the critical success factors for sustaining impact on a work

area after a Kaizen event, taking into consideration how the kaizen or continuous improvement orientation of the work

area inuences the sustained impact over time. In short, we expect that conducting Kaizen events within the context of

a work area culture that employs the dynamic capability, kaizen, will increase the likelihood that the Kaizen event will

have sustained impact. Specically, we examine how kaizen culture characteristics of the target work area and the post-

Kaizen event sustaining mechanisms after implementation (i.e. 9 – 18 months after the Kaizen event) inuence the per-

ceived impact of Kaizen events after implementation, i.e. impact on area post -implementation.

The remainder of this paper is organised as follows. Section 2 presents background on the combined use of Kaizenevents and kaizen and our theoretical framework, which applies the dynamic capabilities perspective, and in particular,

the institutionalisation of organisational change framework. Meanwhile, Section 3 describes the research methodology,

Section 4 presents the analyses and Section 5 discusses the ndings and conclusions. Using data from a eld study of

65 Kaizen events across eight manufacturing organisations, we test our hypothesised relationships to identify the factors

that are the most signicant predictors of impact on area post -implementation. Implications to inform our theoretical

understanding of how continuous improvement culture can support rapid improvement and recommendations for

organisations using rapid improvement projects are presented. Finally, limitations of our study and areas for future

research are presented.

2. Theoretical framework

Organisational change in general, and continuous improvement in particular, is a key dynamic capability (Oxtoby,

McGuiness, and Morgan 2002). Continuous improvement creates novel problem-solving patterns and routines, which

are expected to produce incremental or radical changes in a systematic and predictable fashion (Nelson and Winter

1982; Schreyoegg and Kliesch-Eberl 2007). Teece and Pisano (1994) note that continuous improvement as a part of lean

production serves as a dynamic capability because it requires distinctive shop oor practices and processes, as well as

distinctive higher order managerial processes, making the required coherence of organisational processes very high

for success and making replication of the model very dif cult because it requires systemic changes throughout the

organisation.

In light of this, we adapt the organisational change model known as the institutionalisation of planned change

framework to provide theoretical support for our inquiry. The implementation of new programmes or behaviour, e.g. via

Kaizen events, often achieves some initial success, but high degrees of change institutionalisation are generally dif cult

2 W.J. Glover et al.


4/20

to achieve (Goodman and Dean 1982; Szulanski 1996); i.e. ‘lasting change is usually the exception rather than the rule’

(Doolen et al. 2008, 36 – 37). Thus, Goodman, Bazerman, and Conlon (1980) and later works (e.g. Cummings and

Worley 1997) suggest that institutionalisation, including the extent to which the new change is performed across the

workforce, is dependent upon (1) structure of the change, e.g. goal specicity and internal support for the change, (2)

the organisational characteristics, i.e. existing values, norms, character, and skills of the workforce, and (3) institutionali-

sation processes, including socialisation of commitment to, reward allocation for, diffusion of, and sensing and recalibra-

tion of the change.Regarding the structure of the change, previous studies of Kaizen event sustainability failed to nd the structural

aspects of the Kaizen event, e.g. goal clarity and management support, inuenced the sustainability of Kaizen events,

e.g. (Bateman 2005; Bateman and Rich 2003; Glover et al. 2011). As described by Bateman and Rich (2003), Kaizen

events that meet the highest or lowest levels of sustainable performance tend to achieve some improvement during the

Kaizen event. This suggests that these structural characteristics may play a greater role in the achievement of immediate

outcomes than on sustaining those outcomes. Thus, we exclude this category in our theoretical development and focus

our hypotheses on two sets of characteristics that may be critically associated with a Kaizen event ’s impact on area

post -implementation, or the extent to which the implemented change has a lasting impact on the work area (Buller and

McEvoy 1989). These characteristics are (1) kaizen characteristics of the target work area and (2) post-event characteris-

tics. Figure 1 illustrates the adapted framework and the following describes the theoretical support for each tested

hypothesis indicated in the framework.

2.1 Kaizen characteristics and impact on area post-implementation

To capture existing characteristics that exhibit the culture, behaviours, values and norms of the work area and organisa-

tion per the institutionalisation of planned change framework (Goodman and Dean 1982), we identied two kaizen char-

acteristics: experimentation and continuous improvement, and learning and stewardship.

Experimentation and continuous improvement relate to the extent to which the individuals have knowledge of con-

tinuous improvement and apply new ideas to help themselves learn. This variable relates to recent ndings of the impor-

tance of balancing innovation and improvement (Anand et al. 2009). Research has suggested that an awareness and

understanding of continuous improvement knowledge may be important to the sustainability of improvement, e.g. (Kaye

and Anderson 1999). Also, active experimentation with new ideas (Upton 1996) has been found to be a key component

of knowledge development, which may inuence impact on area post -implementation. Learning and stewardship

Figure 1. Theoretical Framework and Hypotheses.Source: Adapted from Cummings and Worley (1997).

International Journal of Production Research 3


5/20

includes the collective responsibility of a group of work area employees, which may relate to their commitment to

improvement, which may in turn inuence improvement outcome sustainability, e.g. (Mann 2005). Learning and stew-

ardship also describes the extent to which work area employees understand how their work relates to that of other work

areas, which may support awareness and communication across work areas, which in turn may support continued

improvement after a Kaizen event (Tennessen and Tonkin 2008).

Thus, we hypothesise the following:

H1. Kaizen characteristics are signicantly related to impact on area post -implementation.

H1a. Experimentation and continuous improvement is positively related to impact on area post -implementation.

H1b. Learning and stewardship is positively related to impact on area post -implementation.

2.2 Post-event characteristics and impact on area post-implementation

Post-event characteristics, institutionalising change, improvement culture, performance review, avoiding blame and

accepting changes, describe the socialisation of and commitment to the improvement from the Kaizen event, as well as

the allocation of rewards based on the pursuit of behaviours that support the change and the processes used to measure

the degree of institutionalisation, feedback information and corrective actions, i.e. sensing and recalibration (Cummings

and Worley 1997; Goodman and Dean 1982).

Institutionalising change is dened as a bundle of activities conducted to complete the implementation of changesand actions identied in the Kaizen event and to incorporate changes into the ongoing, everyday activities of the organi-

sation (Jacobs 2002). Done, Voss, and Rytter (2011) propose that post-event planning for further ongoing follow-up

activities leads to long-term improvement and sustained change. Improvement culture is dened as the encouragement

of organisational change through management ’s support of the use of both Kaizen events and continuous improvement

activities among work area employees and Kaizen event team members. Anand et al. (2009) suggest that development

of a constant change culture supports the continuous improvement capability, and Oxtoby, McGuiness, and Morgan

(2002) suggest that such a culture also assists in sustaining change. Avoiding blame is the extent to which blame and

negativity are avoided when goals are not achieved or results are different than the established goals. This construct

relates to the extent to which rewards are allocated to support the institutionalisation of change (Cummings and Worley

1997), albeit the concepts have a reverse conceptual relationship. Accepting changes describes the extent to which work

area management and employees accept changes made as a result of the Kaizen event, employees follow the new work

methods as a result of the Kaizen event, and employees are held accountable for following the new work methods as a

result of the Kaizen event. Teece (2007) suggests that organisational cultures should be shaped to accept changes, or

otherwise changes will be met with anxiety and desired continuity may not be sustained. Performance review is dened

as the extent to which the organisation measures and evaluates the results of the Kaizen event. Kaye and Anderson

(1999) identied the establishment of performance measurement and feedback systems as a key criterion for continuous

improvement. Thus, we hypothesise the following:

H2. Post-event characteristics are positively related impact on area post -implementation.

H2a. Institutionalising change is positively related to impact on area post -implementation.

H2b. Improvement culture is positively related to impact on area post -implementation.

H2c. Performance review is positively related to impact on area post -implementation.

H2d. Avoiding blame is positively related to impact on area post -implementation.

H2e. Accepting changes is positively related to impact on area post -implementation.

2.3 The mediating role of post-event characteristics

The institutionalisation of planned change framework is designed to be a mediating model such that the institutionalisa-

tion processes are proposed to at least partially mediate the relationship of the organisational characteristics (Cummings

and Worley 1997; Goodman, Bazerman, and Conlon 1980; Goodman and Dean 1982; Jacobs 2002). This is in part due



6/20

to the temporal nature of the phenomenon, i.e. institutionalisation processes occur after the focal organisation is selected.

This also suggests that a lack of institutionalisation is due to the combined effects of organisation characteristics and the

institutionalisation processes; thus, the model suggests that to ensure long-term success, the institutionalisation processes

require as much or more attention as the other parts of the framework (Jacobs 2002). We adapt and test this framework

such that post-event characteristics mediate the relationship between kaizen characteristics and impact on area post -

implementation as expressed in the following hypothesis:

H3. Post-Event characteristics at least partially mediate the relationship of Kaizen and Work Area characteristics and impact onarea post -implementation.

3. Methods

3.1 Sample selection

We used a multi-site, cross-sectional eld study design, with randomisation at the event level, but not the organisation

level. We used non-random selection at the organisation level due to the need for access to data from multiple events

within each organisation, as well as other organisation-level data, which would require top management buy-in and

longer term commitment to the study. As incentive to participate, organisations were provided with a description of the

study benets to the Kaizen event body of knowledge, as well as the promise of research reports describing ndings

within and across participating organisations. The characteristics of the eight participating organisations are summarised

in Table 1.Despite the non-random nature of the organisational selection, we applied several boundary conditions and event

sampling selection criteria were applied to increase the reliability and validity of study results (Yin 1994). The boundary

conditions used to select organisations were: the organisation manufactures products of some type, had been conducting

Kaizen events for at least one year prior to the start of the study, had been using Kaizen events in a systematic way (vs.

in an ad hoc way) and had been conducting Kaizen events relatively frequently (i.e. at least one per month on average).

That is, all of the eight manufacturing organisations included in this study conducted Kaizen events regularly for at least

one year as a part of their larger improvement programmes, i.e. as part of a lean transformation programme, using a kai-

zen approach.

Kaizen events were randomly sampled within each organisation. The Kaizen events typically included activities such

as documenting current processes, identifying opportunities for improvement, implementing and evaluating changes, pre-

senting results to management and developing an action plan for future improvements (Melnyk et al. 1998). Four organ-

isations agreed to provide data for all events conducted during the study period; therefore, a census sampling approach

was used in those organisations. The other organisations requested a lower data collection frequency. In these organisa-tions, a systematic sampling procedure was used (Scheaffer, Mendenhall, and Ott 1996). For instance, if the average

number of events per month in the organisation was n, a number k was selected between one and n, such that every k th

event was targeted for study.

The data collection occurred approximately 9 – 18 months after each Kaizen event. This time frame was selected

based on previous improvement sustainability studies, e.g. (Doolen et al. 2008; Patil 2003) as shorter time periods were

not believed to be suf cient for assessing long-term outcomes (e.g. implementation efforts were more likely to be still

ongoing) and longer time periods were more likely to encounter cases where work area changes made the sustainability

study no longer relevant.

The researchers successfully collected data from 68 Kaizen events across eight organisations (October 2006 – April

2009). Two of the 68 cases were removed from the analysis due to incomplete data, and one of the 68 cases was consid-

ered inappropriate for inclusion because it was still in implementation phase when the data collection was planned. Thus,

the total sample size for this research is 65 Kaizen events across eight organisations. Table 1 also describes the number of

events studied per organisation, the average number of team members and the average number of days per event.

3.2 Data collection

All data were collected using the 67-item post-event questionnaire that used the Kaizen event or targeted work area as

the referent, and the characteristics measured were hypothesised to represent shared Kaizen event and its associated

work area-level properties. For example, an item for institutionalising change was ‘Individual team members working

on follow-up action items from the Kaizen event ’. An item for learning and stewardship was ‘Work area employees

understand how their work ts into the “ bigger picture” of the organisation’. The items for the variables used in this

study are listed in Appendix 1.



7/20

T a b l e 1 .

C h a r a c t e r i s t i c s o f t h e o r g a n i z a t i o n s s t u d i e d .

O r g .

A

O r g . B

O r g . C

O r g . E

O r g . F

O r g . G

O r g . Q

O r g . R

O r g . d e s c r i p t i o n

S e c o n d a r y w o o d

p r o d u

c t

m a n u f a c t u r e r

E l e c t r o n i c

m o t o r


S e c o n d a r y w

o o d

p r o d u c t

m a n u f a c t u

r e r

S p e c i a l t y

e q u i p m e n t


S t e e l

c o m p o n e n t


A e r o s p a c e

e n g i n e e r i n g a n d


I T

c o m p o n e n t


A e r o s p a c e

e n g i n e e r i n g a n d


Y e a r f o u n d e d

1 9 4 6

1 9 8 5

1 9 4 6

1 9 6 4

1 9 1 3

1 9 1 6

1 9 3 9

1 9 1 6

N o . e m p l o y e e s

5 6 0

7 0 0

5 0 0

9 5 0

3 5 0 0

1 1 , 0 0 0

3 2 1 , 0 0 0

3 0 , 0 0 0

F i r s t K a i z e n e v e n t

1 9 9 8

2 0 0 0

1 9 9 2

2 0 0 0

1 9 9 5

1 9 9 3

2 0 0 4

1 9 9 8

E v e n t r a t e d u r i n g

r e s e a r c h

2 – 3 p e r m o n t h

1 p e r m o n t h

2 p e r m o n

t h

6 – 8 p e r m o n t h

1 p e r m o n t h

4 p e r w e e k

2 p e r m o n t h

4 p e r w e e k

%

o f o r g .

e x p e r i e n c i n g

e v e n t s

1 0 0

9 0

D a t a n o t a v a i l a b l e

1 0 0

2 0

7 0

1 0

1 0 0

%

o f e v e n t s i n

m a n u f a c t u r i n g

a r e a s

A l m o s t 1 0 0 %


7 5 %


A l m o s t 1 0 0 %

m a n u f a c t u r

i n g

D a t a n o t

a v a i l a b l e

8 0 – 8 5 %


7 0 %


9 5 %


6 0 %


N o . K a i z e n e v e n t s

s a m p l e d

( r e t a i n e d )

1 9 ( 1

9 )

5 ( 4 )

4 ( 4 )

1 5 ( 1 3 )

7 ( 7 )

7 ( 7 )

5 ( 5 )

6 ( 6 )

A v g . # d a y s p e r

e v e n t

5 . 1 6

4 . 0 0

5 . 0 0

3 . 3 1

2 . 4 3

4 . 7 1

4 . 8 0

5 . 0 0

A v g . t e a m

s i z e

7

1 3

7

6

6

1 7

1 1

1 3



8/20

The post-event information Qqestionnaire was administered either to the facilitator of the Kaizen event or to the

work area manager, based on the availability of the respondent. Because the post-event information questionnaire was

collected 9 – 18 months after the Kaizen event, in some cases, the Kaizen event facilitator was no longer available for

various reasons (e.g. too busy, left the organisation). Because both facilitators and work area managers represent man-

agement positions that have signicant interactions with the targeted work areas before, during and after the Kaizen

event, we would expect similarities in their views, particularly as most of the variables’ measures through the post-event

information questionnaire are objectively measurable or related to objectively observable behaviours. Still, it is possiblethat opinions of facilitators and work area managers differ systematically across organisation, e.g. facilitators may

always feel that post-event characteristics were conducted to a greater extent than work area managers. Thus, while we

believed that losing an event from the analysis introduced more potential for bias than using a different respondent, the

potential for systematic differences in opinion between facilitator and work area managers is a limitation of the research

as well as an area that should be investigated in future research.

The majority of the post-event information questionnaires were self-administered. If the respondent preferred, one of

the researchers gathered the data via a telephone interview. The collection method was based on the preference and

availability of the respondent. Again, using this mixed collection method could introduce some bias in the data. How-

ever, because of the relatively objective nature of the variables, we believe that the benets of being able to collect more

data were preferred over this potential bias.

The questionnaire was developed in accordance with commonly accepted principles for questionnaire and interview

script design (Dillman 2000). The variables were operationalised as multi-item constructs, which, where possible, were

based on previously existing instruments. All close-ended perceptual measures used the same six-point response scale:1 = ‘not at all’, 2 = ‘to a small extent ’, 3 = ‘to some extent ’, 4 = ‘to a moderate extent,’ 5 = ‘to a large extent ’, and

6 = ‘to a great extent ’. Table 2 presents the means, standard deviation and correlation matrix for all independent and

dependent variables.

3.3 Validation of measures

Following data collection, we used exploratory factor analysis (EFA) and Cronbach’s alpha (Cronbach 1951) to analyse

the validity and reliability of our multi-item scales. We used EFA, rather than conrmatory factor analysis (CFA)

because of our evaluation of new and signicantly adapted constructs (Shah and Goldstein 2006), as well as our rela-

tively small sample size and the nested nature of our data, which could result in biased statistical test results, although

not biased factor loadings. Future research should use CFA with a larger sample size to further validate the measures

developed in this research.

After eliminating post-event information questionnaires with excessive missing data as discussed above, our data-set consisted of 65 post-event information questionnaires. Three separate EFA were conducted for kaizen characteristics,

post-event characteristics and the dependent variable, impact on area post -implementation, due to the hypothesised role

of post-event characteristics as a mediator of kaizen characteristics, as well as the small sample size. Using this division,

the n = 65 sample size meets the minimum observation to item ratio of 2 data points per one variable (Kline 2005) for

both EFA.

Table 2. Mean, standard deviation, and correlations for dependent and independent variables of interest.

1 2 3 4 5 6 7 8

1. Impact on area post-implementation 12. Institutionalising change .322(**) 1

3. Improvement culture .335(**) .511(**) 14. Performance review .237 .514(**) .357(**) 15. Avoiding blame .436(**) .192 .298(*) .198 16. Accepting changes .241 .261(*) .264(*) −.014 .592(***) 17. Learning and stewardship .356(**) .414(**) .388(**) .486(**) .367(**) .055 18. Experimentation and continuous

improvement .273(*) .397(**) .419(**) .369(**) .477(**) .289(*) .763(**) 1

Mean 4.55 3.53 4.32 3.32 4.00 4.78 4.57 4.33Standard deviation 1.038 1.178 0.874 1.233 1.321 0.437 0.709 0.745

**Indicates statistical signicance at α


9/20

Prior to the EFA, we screened the data for basic distributional assumptions of standard parametric methods (Neter

et al. 1996); overall, the data were non-normal, but this deviation was not severe enough to exclude the use of paramet-

ric analysis methods, i.e. no skewness values were greater than 2.0 (DeCarlo 1997).

All the EFA models used principal components extraction with oblique (direct quartimin) rotation, to allow correla-

tion between factors which may be interrelated (Jennrich 2002; Johnson and Wichern 2007). The established heuristic

of extracting all factors with eigenvalues greater than 1.0 (Johnson 1998) was used to determine the number of factors.

Individual items were considered to have loaded onto a given factor when the primary loading was 0.500 or greater andall cross-loadings were less than 0.300 (Kline 1994).

Cronbach’s alpha values were calculated on the nal revised scales and were evaluated against the commonly

applied thresholds of 0.700 for established scales (Nunnally 1978) and 0.600 for newly developed scales (DeVellis

1991). All scales had alpha values greater than 0.700 and most scales (nine out of 11) had alpha values of 0.800 or

greater. Appendix 1 includes the mean value, skewness value, smallest primary loading, largest cross-loading, initial

eigenvalue, percentage of variance explained and the Cronbach’s alpha values for each construct.

Following the reliability analysis, scale averages were calculated using the revised scales to arrive at the values of the

associated study variables for the Kaizen event. The resultant variables were assessed to determine the statistical moments,

distributional properties and the collinearity of the independent variables in our study. In general, the variables appeared to

be relatively normally distributed. While formal tests of normality were rejected for several variables, they appeared to only

demonstrate mild departures from normality. Finally, the collinearity of the resultant independent variables was assessed

using the variance ination factor (VIF). An individual VIF greater than 10.0 (Neter et al. 1996) or an average VIF greater

than 3.0 generally indicates a problem with multicollinearity. In this research, the maximum observed VIF was 3.09 and theaverage VIF was 2.24. Thus, multicollinearity did not appear to be problematic in the data-set. A summary of the results of

the EFA, reliability analysis and multicollinearity analysis is presented in Glover (2009, 2010).

4. Analysis

To test the relationships between variables, we used multiple linear regression to test direct relationships (H1 – H2) and

mediation analysis to test indirect relationships (H3). Due to the nested structure of our data, we could not assume that

the responses for Kaizen events within a given organisation were uncorrelated (Kenny and Judd 1986). Therefore, we

used generalised estimating equations (GEE) (Liang and Zeger 1986), executed in SAS 9.1.3 using PROC GENMOD,

to account for correlation between Kaizen events within the same organisation, which may bias the estimates of parame-

ter standard errors and associated F-tests (Lawal 2003). The study sample size is 65 Kaizen events as opposed to eight

organisations, because GEE accounts for the interclass correlation and the potentially different averages (intercepts)

across organisations. Ordinary least squares (OLS) estimates were also calculated for comparison purposes, and auto-mated OLS variable selection procedures were used to analyse the robustness of the model generated using GEE. Other

common approaches for analysing nested data include hierarchical linear modelling (HLM) (e.g. Raudenbush and Byrk

2002). However, sample size considerations precluded the use of this technique. In our GEE models, we assumed an

exchangeable correlation matrix, which hypothesises equal correlation of residuals between all Kaizen events within a

given organisation. There was no established hierarchy of variable importance. Therefore, for the model-building pro-

cess, an exploratory manual backwards selection procedure was used.

Mediation analysis was used to determine whether any input factors, i.e. the kaizen characteristics, had indirect

effects on impact on area post -implementation through the mediating post-event characteristics. A mediator is a variable

that is in a causal sequence between two variables (MacKinnon, Fairchild, and Fritz 2007), and mediation occurs when

an input variable acts indirectly upon an outcome variable through a mediating process variable (Baron and Kenny

1986). GEE was also used to analyse the mediation relationships. A ve-step process was used to perform the mediation

analysis (Kenny 2009); the rst steps are the identication of the potentially mediating variables and the primary media-

tion analysis testing, while the last two steps were post hoc analyses used to test the robustness of the solution found inthe primary mediation analysis testing. The rst three steps tested three paths to evaluate each mediation hypothesis (the

paths from the potential mediators to the outcome – i.e., Step 1 – had already been tested in the direct regression).

Therefore, an α level of 0.05/3 = 0.0167 was adopted as the signicance level for each path to preserve an overall 0.05

condence level for the test (Kenny 2009).

4.1 Identi cation of direct and indirect predictors of impact on area post-implementation

All of the selection procedures (OLS and GEE) converged upon a single predictor model (Table 3), where accepting

change GEE β = 0.658, p < 0.0001) signicantly predicted approximately 50% of the variance for the impact on area

post -implementation model (GEE Ra 2

= 0.504).



10/20

The GEE and OLS model parameters are similar. Also, the observed intraclass correlation reported by the GEE pro-

cedure was −0.043. Because the observed intraclass correlation was negative, more variation occurs between clusters

(organisations) than within clusters (organisations). However, it should also be noted that the intraclass correlation may

not be signicantly different from zero.

Finally, the residual plots and partial regression plots did not indicate departures from linearity or any other evidence

of model specication errors. All standardised residual values were less than 2.0 (the largest standardised residual had

an absolute value of 1.987), thus presenting no strong evidence of inuential cases. The Wald – Wolfowitz run test

(Chang 2000) was not signicant ( p = 0.276), indicating a random pattern in the residuals. In summary, the null hypoth-

esis for H1 failed to be rejected in that no kaizen characteristics were found to be direct predictors of impact on area post -implementation. On the other hand, there was partial support for H2e; i.e. impact on area post -implementation was

signicantly predicted by one post-event characteristic, accepting changes.

Again, because accepting changes is a post-event characteristic, the potential role of accepting changes as a mediat-

ing variable in the model was explored. Specically, the following hypotheses were tested:

H3. Post-event characteristics partially mediate the relationship of kaizen and work area characteristics and impact on area post -implementation.

H3a. Accepting changes at least partially mediates the relationship between experimentation and continuous improvement andimpact on area post -implementation.

H3b. Accepting changes at least partially mediates the relationship between learning and stewardship and impact on area post -implementation.

Table 4 presents the results of the mediation analysis.

In the rst step of the mediation analysis, accepting changes was found to have a signicant relationship with learn-

ing and stewardship and experimentation and continuous improvement . For step 2 (Path b), the impact of accepting

changes on impact on area post -implementation while controlling for the predictor ( X ) was signicant for learning and

stewardship and experimentation and continuous at the α = 0.05/3 = 0.0167 level. Thus, mediation analysis results for

learning and stewardship and experimentation and continuous improvement were consistent with the mediation hypothe-

sis that learning and stewardship and experimentation and continuous improvement impacts impact on area post -imple-

mentation indirectly through accepting changes.

Path c’ was not signicant for learning and stewardship or experimentation and continuous improvement at the

adjusted alpha value, which is consistent with a full mediation effect that learning and stewardship and experimentation

and continuous improvement signicantly affects impact on area post -implementation, but only indirectly through

accepting changes.

For step 3, accepting changes was regressed simultaneously on learning and stewardship and experimentation and continuous improvement . Learning and stewardship was signicant in this regression ( p < 0.05), thus, providing further

support for its inclusion in the mediation hypothesis. However, experimentation and continuous improvement was not

signicant, suggesting that it should not be included as a mediating variable in the nal model, as it is no longer signi-

cant when considered simultaneously with learning and stewardship. Finally, impact on area post -implementation was

regressed on learning and stewardship. In considering the direct effects of the input variables on the outcome, learning

and stewardship had a signicant direct effect at the 0.05 level, further supporting its inclusion in the model

( β = 0.5294, p = 0.009). In summary, only learning and stewardship is presented as a fully mediated variable in the nal

model of impact on area post -implementation. This specically refers to H3b in the detailed hypothesis list above, thus,

H3b was supported.

Table 3. Regression model for impact on area post-implementation.

GEE β SE GEE α GEE OLS β SE OLS α OLS

Intercept 1.373 0.378 0.000 1.275 0.421 0.004Accepting changes 0.658 0.073


11/20

T a b l e 4 .

P o s t - h o c m e d i a t i o n a n

a l y s i s r e s u l t s f o r i m p a c t o n a r e a p o s t - i m p l e m e n t a t i o n .

S t e p 1 : y ′ = A c c e p t i n g C h a n g e s ,

s e p a r a t e r e g r e s s i o n

C o e f . ( a )

S . E .

p - v a l u e

L e a r n i n g a n d S t e w a r d s h i p

0 . 8 8 4

0 . 1 5 5

< . 0 0 0 1 *

E x p e r i m e n t a t i o n a n d C o n t i n u o u s I m p r o v e m e n t

0 . 5 5 3

0 . 1 7 1

0 . 0 0 1 2 *

S t e p 2 : y ′ = I m p a c t o n A r e a P o s t - I m p l e m e n t a t i o n , s e p a r a t e r e g r e s s i o n

C o e f . ( b )

S E

p - v a l u e

C o e f . ( c ′ )

S E

p - v a l u e

A c c e p t i n g C h a n g e s

0 . 7 1 2

0 . 0 9 1

<

0 . 0 0 0 1 *


− 0 . 1 3 8

0 . 1 3

8

0 . 3 1 8 2

A c c e p t i n g C h a n g e s

0 . 7 4 5

0 . 1 0 3

<

0 . 0 0 0 1 *


− 0 . 2 0 7

0 . 1 7

2

0 . 2 2 6 9

S t e p 3 : y ′ = A c c e p t i n g C h a n g e s ,

s i m u l t a n e o u s r e g r e s s i o n

C o e f . ( a ’ )

S E

p - v a l u e


0 . 7 9 4

0 . 2 2 4

0 . 0 0 0 4 *


0 . 0 8 0

0 . 2 1 9

0 . 7 1 4 4

S t e p 4 : y ′ = I m p a c t o n A r e a P o s t - I m p l e m e n t a t i o n , s e p a r a t e r e g r e s s i o n

C o e f .

S E

p - v a l u e


0 . 5 2 9

0 . 1 7 1

0 . 0 0 9 *

M e d i a t i o n A n a l y s i s R e s u l t s f o r A c c e p t i n g C h a n g e s a n d I m p a c t o n A r e a P

o s t -

I m p l e m e n t a t i o n

T o t a l m e d i a t e d e f f e c t

( a × b )

P a r t i a l o r F u l l


0 . 6 5 8 9

F u l l

* I n d i c a t e s s t a t i s t i c a l s i g n i c a n c e

a t α

< = 0 . 0 5 .



12/20

5. Discussion and conclusions

5.1 Implications for theory

Changes as a result of Kaizen events have been reported to immediately improve performance, including increased pro-

ductivity, reduced cycle time and decreased WIP (Laraia, Moody, and Hall 1999). The extent to which such changes

have a lasting impact on the work area is a criterion of change institutionalisation (Buller and McEvoy 1989). Knowl-

edge transfer theorists also emphasise that persistence and sustained change are hallmarks of achieving a ‘retentive’

capability within an organisation (Szulanski 1996). These theoretical underpinnings are evident in our measurement of the dependent variable, impact on area post -implementation, nine to 18 months after the Kaizen event.

To the authors’ knowledge, this research uses the largest sample size at the Kaizen event-level to date (n = 65) to

study the relationship between kaizen characteristics, post-event characteristics, and perceived impact on area post-

implementation. Our respondents reported moderate levels of impact on area post -implementation (average = 4.55/6.00).

This nding supports the multi-case study research of previous scholars, e.g. Done, Voss, and Rytter ( 2011) that found

that at least half of the Kaizen events studied had at least ‘satisfactory’ level of improvement, indicating that there was

at least some basis for sustaining and continuing long-term improvement.

Accepting changes was a direct, positive predictor of impact on area post -implementation. Research suggests that

perceptions and activities related to accepting changes, including having an ‘open-minded’ workforce (Bateman and

Rich 2003; García, Rivera, and Iniesta 2013) and the reinforcement of change from management (Kaye and Anderson

1999), may support sustainable improvements, which provides general support for this nding. The nding suggests that

sustained impact of a Kaizen event is related to a key routine of successful continuous improvement implementation,

‘leading the way,’ or the ability of management to lead direct and support the sustaining of continuous improvement

behaviours via their acceptance of Kaizen event changes (Bessant, Caffyn, and Gallagher 2001). Also, the rst follow-

up task in the Bateman and Rich (2003) model of improvement sustainability is ‘maintaining the new procedure’. This

task is similar to the component of accepting changes that refers to the extent to which work area employees follow the

new work methods as a result of the Kaizen event.

Theoretically, the signicance of accepting changes to predict impact on area post -implementation further supports

the critical role of institutionalisation processes, particularly the commitment to change, on sustaining change

(Cummings and Worley 1997; Goodman and Dean 1982). Teece (2007) argues that building loyalty and commitment

via leadership demonstration and recognition of non-economic factors, value and culture is a micro-foundation of the

dynamic capabilities necessary to sustain superior enterprise performance. Similarly, our nding suggests that both man-

agement and the workforce play a role in adopting improvements into their work area, thus building their dynamic capa-

bility of continuous improvement and modifying their operational capabilities. Thus, in answer to the central line of

inquiry for this paper, the ability to impact a work area from a Kaizen event after signi

cant time has lapsed, since ini-tial implementation is at least in part explained by the extent to which management and the workforce are accepting of

change.

Through mediation analysis, we found that learning and stewardship was positively indirectly related to impact on

area post -implementation through accepting changes, suggesting that higher perceptions of accepting changes appear to

be evident in work areas that encourage learning and stewardship among their employees. This nding supports the in-

stitutionalisation mediation model (Cummings and Worley 1997; Goodman and Dean 1982), such that a post-event char-

acteristic mediated the relationship between a kaizen characteristic and impact on area post -implementation. This also

provides support for explaining the relationship between Kaizen events and kaizen. Organisational learning is considered

one of the underlying theories of the dynamic capabilities perspective (Zollo 2002), and thus, a critical component of

building continuous improvement capabilities (Anand et al. 2009). Furthermore, researchers note that a learning-oriented

workforce may be more open-minded about the way work is performed (Baker and Sinkula 1999), and may have

increased feelings of ownership over the changes that are implemented in their work area (Oxtoby, McGuiness, and

Morgan 2002). Thus, Kaizen events also appear to enable kaizen, at least in part, by the extent to which work areaemployees learn and collaborate with one another within their work area and have a shared sense of responsibility out-

side of their work area.

5.2 Implications for management

Through this study, our intent is that managers may better understand the extent to which Kaizen events are able to make a

lasting measurable improvement on performance and what factors may inuence impact on area post -implementation. The

signicant relationship between accepting changes and impact on area post -implementation implies that managers

may increase the impact of a Kaizen event on the targeted work area through such practices as having employee training on



13/20

new work methods; explaining changes to work area management so that they are more likely to accept them (particularly

if the managers were not directly involved in the Kaizen event); and implementing incentives or constructive feedback

mechanisms to encourage employees to be accountable for following those new work methods.

Also, it appears that work area employees who possess increased learning and stewardship are more accepting of

change because they are more aware of the role that their acceptance plays in the larger organisation and wish to be

‘good stewards’ by accepting and adhering to changes. Another practical recommendation for managers, based on medi-

ation of learning and stewardship through accepting changes, is to promote internal collaborative knowledge exchangevia assigning time for peers to discuss lessons learned and encouraging collective responsibility among work area

employees via explaining how employee roles and responsibilities inuence operations inside and outside of the work

area.

5.3 Limitations and future research

It is interesting to review the variables that were not found to be signicant in the research model. For instance, the kai-

zen characteristic experimentation and continuous improvement did not directly predict impact on area post -implementa-

tion. This suggests that having this characteristic in a work area may not contribute substantially towards promoting

behaviours related to accepting changes and sustaining the results of a Kaizen event. Other post-event characteristics, in-

stitutionalising change, improvement culture, performance review and avoiding blame were also not found to have an

effect on impact on area post -implementation. In other words, activities such as planning and executing follow-up activ-

ities after the Kaizen event, regular continuous improvement, measuring performance for continuous improvement and

avoiding negativity do not necessarily explain why some work areas would achieve high impact on area post -implemen-

tation, while others would not.

There were also several limitations to this study. First, while this study does account for some organisation-level

effects through the use of GEE, it should be noted that GEE does not remove all organisation-level effects. In particular,

GEE does not account for different regression slopes across organisations, e.g. if there is a different relationship between

a certain X and Y in a certain organisation. HLM is a technique which can account for this type of effect. In future

research, a larger sample size could be collected to allow for HLM. It should also be noted our all of our items were

expected to vary at the Kaizen event or work area level, rather than the organisation level, and worded accordingly. For

example, an item for ‘institutionalising change’ was ‘Training work area employees in new work methods and processes

from the Kaizen event ’. One can see how, within the same organisation, more training could be provided for one Kaizen

event vs. another. Items for ‘learning and stewardship’ or ‘improvement culture’ asked specically about work area

management or work area employees.The performance variable in question, impact on area post -implementation, is a perceptual measure as opposed to

an objective measure, presenting another limitation of the research. While objective results for each Kaizen event were

collected immediately after the Kaizen event and 9 – 18 months after the event, respondents’ reporting of these objective

results were so varied (e.g. ‘56% decrease in lead time’ and ‘yes, the 5S results have been sustained’) that comparison

between the objective measures yielded a statistical model with limited predictive power (Glover et al. 2013); future

research should consider other approaches to objectively measuring improvement performance over time. The mixed

respondent positions and mixed-methods data collection approach are also potential limitations that were discussed in

the methodology section.

Finally, the study sample was limited in terms of the number and type of participating organisations, which may

impact generalisability. All participating organisations were manufacturing organisations, so it is possible that the nd-

ings may be affected by industry type and cannot be generalised to other industries. Further research could consider a

larger sample size of events from a broader range of industries.

Acknowledgement

We also gratefully acknowledge the Kaizen event team members, facilitators, and coordinators for their participation.

Disclosure statement

The authors have no nancial interest or benet from the direct applications of this research.



14/20

Funding

This work was supported by the National Science Foundation [grant number DMI-0451512].

References

Adamson, Bonnie, and Susan Kwolek. 2008. “Strategy, Leadership and Change: The North York General Hospital Transformation

Journey.” Healthcare Quarterly 11 (3): 50 – 53.Anand, Gopesh, Peter T. Ward, Mohan V. Tatikonda, and David A. Schilling. 2009. “Dynamic Capabilities through Continuous

Improvement Infrastructure.” Journal of Operations Management 27 (6): 444 – 461. doi:10.1016/j.jom.2009.02.002 . http://www.

sciencedirect.com/science/article/pii/S0272696309000199.

Baker, W., and J. Sinkula. 1999. “The Synergistic Effect of Market Orientation and Learning Orientation on Organizational Perfor-

mance.” Journal of the Academy of Marketing Science 27 (4): 411 – 427.

Baron, R. M., and D. A. Kenny. 1986. “The Moderator – Mediator Variable Distinction in Social Psychological Research: Conceptual,

Strategic, and Statistical Considerations.” Journal of Personality and Social Psychology 51: 1173 – 1182.

Bateman, Nicola. 2005. “Sustainability: The Elusive Element of Process Improvement.” International Journal of Operations and Pro-

duction Management 25 (3): 261 – 276. http://www.emeraldinsight.com/10.1108/01443570510581862.

Bateman, Nicola, and Nick Rich. 2003. “Companies’ Perceptions of Inhibitors and Enablers for Process Improvement Activities.”

International Journal of Operations and Production Management 23 (2): 185 – 199. http://dx.doi.org/10.1108/014435703104

58447.

Bessant, John, and David Francis. 1999. “Developing Strategic Continuous Improvement Capability.” International Journal of Opera-

tions and Production Management 19 (11): 1106 – 1119.

Bessant, John, Sarah Caffyn, and Maeve Gallagher. 2001. “An Evolutionary Model of Continuous Improvement Behaviour.” Techno-

vation 21 (2): 67 – 77.

Browning, Tyson R., and Ralph D. Heath. 2009. “Reconceptualizing the Effects of Lean on Production Costs with Evidence from the

F-22 Program.” Journal of Operations Management 27 (1): 23 – 44. doi:10.1016/j.jom.2008.03.009 . http://www.sciencedirect.

com/science/article/pii/S0272696308000211.

Brunet, A. P., and S. New. 2003. “Kaizen in Japan: An Empirical Study.” International Journal of Operations and Production

Management 23 (12): 1426 – 1446.

Buller, Paul F., and Glenn M. McEvoy. 1989. “Determinants of the Institutionalization of Planned Organizational Change.” Group &

Organization Management 14 (1): 33 – 50. http://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=

480495&Fmt=7&clientId=8956&RQT=309&VName=PQD .

Burch, M. K. 2008. Lean Longevity Kaizen Events and Determinants of Sustainable Improvement . Amherst, MA: University of Mas-

sachusetts.

Chang, Y. 2000. “Residual Analysis of the Generalized Linear Models for Longitudinal Data.” Statistics in Medicine 19 (10): 1277 –

1293.

Chen, Joseph C., Ye Li, and Brett D. Shady. 2010. “From Value Stream Mapping Toward a Lean/Sigma Continuous Improvement

Process: an Industrial Case Study.” International Journal of Production Research 48 (4): 1069 – 1086.

Cronbach, L. 1951. “Coef cient Alpha and the Internal Structure of Tests.” Psychiatrika 16: 297 – 334.

Cummings, T., and C. Worley. 1997. Organizational Development and Change. 6th ed. Cincinnati, OH: South-Western College Pub-

lishing.

Cuscela, Kristin N. 1998. “Kaizen Blitz: Attacks Work Processes at Dana Corp.” IIE Solutions 30 (4): 29 – 31. http://ezproxy.lib.vt.

edu:8080/login?url=http://proquest.umi.com/pqdweb?did=28487282&Fmt=7&clientId=8956&RQT=309&VName=PQD .

DeCarlo, L. T. 1997. “On the Meaning and Use of Kurtosis.” Psychological Methods 2 (3): 292 – 307.

Destefani, Jim. 2005. “Lean Propels Turbine Engine Production.” Manufacturing Engineering 134 (5): 157 – 165.

DeVellis, R. F. 1991. Scale Development: Theory and Application. Newbury Park, CA: Sage.

Dillman, D. A. 2000. Mail and Internet Surveys: The Tailored Design Method . 2nd ed. New York: Wiley.

Done, Adrian, Chris Voss, and Niels Gorm Rytter. 2011. “Best Practice Interventions: Short-term Impact and Long-term Outcomes.”

Journal of Operations Management 29 (5): 500 –

513. doi:10.1016/j.jom.2010.11.007. http://www.sciencedirect.com/science/article/pii/S0272696310000926 .

Doolen, Toni L., Marla E. Hacker, and Eileen M. Van Aken. 2008. “The Impact of Organizational Context on Work Team Effective-

ness: A Study of Production Team.” IEEE Transactions on Engineering Management 50 (3): 285 – 296.

Doolen, Toni L., Eileen M. Van Aken, Jennifer A. Farris, June M. Worley, and Jeremy Huwe. 2008. “Kaizen Events and Organiza-

tional Performance: A Field Study.” International Journal of Productivity and Performance Management 57 (8): 637 – 658.

http://www.emeraldinsight.com/10.1108/17410400810916062 .

Eisenhardt, Kathleen M., and Jeffrey A. Martin. 2000. “Dynamic Capabilities: What Are They?” Strategic Management Journal 21

(10 – 11): 1105 – 1121. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4017346&site=ehost-live.

Emiliani, M. L. 2006. “Origins of Lean Management In America.” Journal of Management History 12 (2): 167 – 184.


http://dx.doi.org/10.1016/j.jom.2009.02.002http://www.sciencedirect.com/science/article/pii/S0272696309000199http://www.sciencedirect.com/science/article/pii/S0272696309000199http://www.emeraldinsight.com/10.1108/01443570510581862http://dx.doi.org/10.1108/01443570310458447http://dx.doi.org/10.1108/01443570310458447http://dx.doi.org/10.1016/j.jom.2008.03.009http://www.sciencedirect.com/science/article/pii/S0272696308000211http://www.sciencedirect.com/science/article/pii/S0272696308000211http://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=480495&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=480495&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=28487282&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=28487282&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://dx.doi.org/10.1016/j.jom.2010.11.007http://www.sciencedirect.com/science/article/pii/S0272696310000926http://www.sciencedirect.com/science/article/pii/S0272696310000926http://www.emeraldinsight.com/10.1108/17410400810916062http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4017346&site=ehost-livehttp://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=4017346&site=ehost-livehttp://www.emeraldinsight.com/10.1108/17410400810916062http://www.sciencedirect.com/science/article/pii/S0272696310000926http://www.sciencedirect.com/science/article/pii/S0272696310000926http://dx.doi.org/10.1016/j.jom.2010.11.007http://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=28487282&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=28487282&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=480495&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://ezproxy.lib.vt.edu:8080/login?url=http://proquest.umi.com/pqdweb?did=480495&Fmt=7&clientId=8956&RQT=309&VName=PQDhttp://www.sciencedirect.com/science/article/pii/S0272696308000211http://www.sciencedirect.com/science/article/pii/S0272696308000211http://dx.doi.org/10.1016/j.jom.2008.03.009http://dx.doi.org/10.1108/01443570310458447http://dx.doi.org/10.1108/01443570310458447http://www.emeraldinsight.com/10.1108/01443570510581862http://www.sciencedirect.com/science/article/pii/S0272696309000199http://www.sciencedirect.com/science/article/pii/S0272696309000199http://dx.doi.org/10.1016/j.jom.2009.02.002


15/20

Farris, Jennifer A. 2006. “An Empirical Investigation of Kaizen Event Effectiveness Outcomes and Critical Success Factors.” Ph.D.

diss., Virginia Polytechnic Institute and State University.

Farris, Jennifer A., Eileen M. Van Aken, Toni L. Doolen, and June M. Worley. 2008. “Learning from Less Successful Kaizen Events:

A Case Study.” Engineering Management Journal 20 (3): 10 – 20.

Friedli, D. 2000. “UK Firms May Suffer from ‘kamikaze’ Kaizen Strategy.” The Engineer , January.

García, J. L., D. G. Rivera, and A. A. Iniesta. 2013. “Critical Success Factors for Kaizen Implementation in Manufacturing industries

in Mexico.” The International Journal of Advanced Manufacturing Technology 68 (1-4): 537 – 545.

Glover, Wiljeana J. 2009. “Evaluating the Psychometric Properties of Survey Measures for Kaizen Event Post-event Factors.”Proceedings of the 2009 American Society for Engineering Management Conference, Springeld, MO.

Glover, Wiljeana J. 2010. “Critical Success Factors for Sustaining Kaizen Event Outcomes.” Industrial and Systems Engineering .

Blacksburg, VA: Virginia Polytechnic Institute and State University.

Glover, Wiljeana J., Jennifer A. Farris, Eileen M. Van Aken, and Toni Doolen. 2011. “Critical Success Factors for the Sustainability

of Kaizen Event Human Resource Outcomes: An Empirical Study.” International Journal of Production Economics 132:

197 – 213.

Glover, Wiljeana J., Jennifer A. Farris, Eileen M. Van Aken, and Toni Doolen. 2013. “Kaizen Event Result Sustainability for Lean

Enterprise Transformation.” Journal of Enterprise Transformation 3 (3): 136 – 160.

Glover, Wiljeana J., Wen-Hsing Liu, Jennifer A. Farris, and Eileen M. Van Aken. 2013. “Characteristics of Established Kaizen Event

Programs: An Empirical Study.” International Journal of Operations & Production Management 33 (9): 1166 – 1201.

Goldacker, D. K. 2005. “Of ce Accelerated Improvement Workshop Methodology (AIW).” Paper presented at the annual meeting of

the IIE Annual Conference and Expo, Orlando, FL, May 20 – 24.

Goodman, Paul S., and James W. Dean. 1982. “Creating Long-term Organizational Change.” In Change in Organizations: New

Perspectives on Theory, Research, and Practice, edited by Paul S. Goodman, 1st ed., 226 – 279. San Francisco, CA: Jossey-Bass.

Goodman, Paul S., Max Bazerman, and Edward Conlon. 1980. “Institutionalization of Planned Organizational Change.” In Research

in Organizational Behavior , edited by Barry M. Staw and Larry L. Cummings, 2: 215 – 246. Greenwich, CT: Jay Press.

Groesbeck, Richard L. 2001. “An Empirical Study of Group Stewardship and Learning: Implications for Work Group Effectiveness.”

Ph.D. diss., Virginia Polytechnic Institute and State University.

Heard, E. 1997. “Rapid-Fire Improvement with Short-Cycle Kaizen.” Paper presented at the annual meeting of the American Produc-

tion and Inventory Control Society, Washington, DC, October 26 – 29.

Helfat, C. E., and S. G. Winter. 2011. “Untangling Dynamic and Operational Capabilities: Strategy for the (N)ever-changing World.”

Strategic Management Journal 32 (11): 1243 – 1250.

Hung, K., Y. K. Ro, and J. K. Liker. 2009. “Further Motivation for Continuous Improvement in Just-In-Time Logistics.” IEEE

Transactions on Engineering Management 56 (4): 571 – 583.

van Iwaarden, J., T. van der Wiele, B. Dale, R. Williams, and B. Bertsch. 2008. “The Six Sigma Improvement Approach: A Transna-

tional Comparison.” International Journal of Production Research 46 (23): 6739 – 6758. doi:10.1080/00207540802234050.

http://www.tandfonline.com/doi/abs/10.1080/00207540802234050 .Jacobs, Ronald L. 2002. “Institutionalizing Organizational Change through Cascade Training.” Journal of European Industrial Training

26 (2/3/4): 177 – 182. http://www.emeraldinsight.com/10.1108/03090590210422058

Jennrich, Robert. 2002. “A Simple General Method for Oblique Rotation.” Psychometrika 67 (1): 7 – 19. http://dx.doi.org/10.1007/

BF02294706.

Johnson, Dallas E. 1998. Applied Multivariate Methods for Data Analysts. Pacic Grove, CA: Duxbury Press.

Johnson, Richard Arnold, and Dean W. Wichern. 2007. Applied Multivariate Statistical Analysis. 6th ed. Upper Saddle River, NJ:

Pearson Prentice Hall.

Kaye, Mike, and Rosalyn Anderson. 1999. “Continuous Improvement: the Ten Essential Criteria.” International Journal of Quality

and Reliability Management 16 (5): 485 – 509.

Kenny, D. A. 2009. “Mediation.” Accessed March 13, 2010. http://davidakenny.net/cm/mediate.htm

Kenny, D. A., and C. M. Judd. 1986. “Consequences of Violating the Independence Assumption in Analysis of Variance.”

Psychological Bulletin 99 (3): 422 – 431.

Kline, P. 1994. An Easy Guide to Factor Analysis . London: Routledge.

Kline, R. B. 2005. Principles and Practice of Structural Equation Modeling . New York: The Guilford Press.

Laraia, Anthony C., Patricia E. Moody, and Robert W. Hall. 1999. The Kaizen Blitz: Accelerating Breakthroughs in Productivity and

Performance. National Association of Manufacturers Series. New York: Wiley.

Lawal, B. 2003. Categorical Data Analysis with SAS and SPSS Applications. London: Lawrence Erlbaum Associates.

Liang, K. Y., and S. L. Zeger. 1986. “Longitudinal Data Analysis Using Generalized Linear Models.” Biometrika 73: 13 – 22.

MacKinnon, D. P., A. J. Fairchild, and M. S. Fritz. 2007. “Mediation Analysis.” Annual Review of Psychology 58: 593 – 614.

Magdum, V., and L. Whitman. 2007. “Sustainability of Kaizen Events.” Working Paper . Wichita, KS: Department of Industrial and

Manufacturing Engineering, Wichita State University.

Mann, David. 2005. Creating a Lean Culture: Tools to Sustain Lean Conversions . New York: Productivity Press. http://books.google.

com/books?id=aqffmYejsC0C&dq=Sustaining+Lean+book&lr=&source=gbs_navlinks_s.


http://dx.doi.org/10.1080/00207540802234050http://www.tandfonline.com/doi/abs/10.1080/00207540802234050http://www.emeraldinsight.com/10.1108/03090590210422058http://dx.doi.org/10.1007/BF02294706.http://dx.doi.org/10.1007/BF02294706.http://davidakenny.net/cm/mediate.htmhttp://books.google.com/books?id=aqffmYejsC0C&dq=Sustaining+Lean+book&lr=&source=gbs_navlinks_s.http://books.google.com/books?id=aqffmYejsC0C&dq=Sustaining+Lean+book&lr=&source=gbs_navlinks_s.http://books.google.com/books?id=aqffmYejsC0C&dq=Sustaining+Lean+book&lr=&source=gbs_navlinks_s.http://books.google.com/books?id=aqffmYejsC0C&dq=Sustaining+Lean+book&lr=&source=gbs_navlinks_s.http://davidakenny.net/cm/mediate.htmhttp://dx.doi.org/10.1007/BF02294706.http://dx.doi.org/10.1007/BF02294706.http://www.emeraldinsight.com/10.1108/03090590210422058http://www.tandfonline.com/doi/abs/10.1080/00207540802234050http://dx.doi.org/10.1080/00207540802234050


16/20

Martin, Karen, and Mike Osterling. 2007. The Kaizen Event Planner . New York: Productivity Press.

Martin, Susan C., Pamela K. Greenhouse, Amy M. Kowinsky, Renee L. McElheny, Con

the relationship between continuous improvement and rapid improvement sustainability 2014

Documents