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This is to certify that the

dissertation entitled

UNDERSTANDING THE DIFFUSION OF LEAN PRODUCTION:
THE INTEGRATION OF
TECHNOLOGY AND PEOPLE IN LEAN PRODUCTION

presented by

William Mark Mothersell

has been accepted towards fulﬁllment
of the requirements for

Ph.D. Labor and Industrial

degree in .
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Date August 24, 2000

 

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UNDERSTANDING THE DIFFUSION OF LEAN PRODUCTION:
THE INTEGRATION OF
TECHNOLOGY AND PEOPLE IN LEAN PRODUCTION

By

William Mark Mothersell

A DISSERTATION
Submitted to
Michigan State University
For the degree of
DOCTOR OF PHILOSOPHY
School of Labor and Industrial Relations

August 2000

ABSTRACT
UNDERSTANDING THE DIFFUSION OF LEAN PRODUCTION:
THE INTEGRATION OF
TECHNOLOGY AND PEOPLE IN LEAN PRODUCTION

By

William Mark Mothersell

This study examined the extent to which technical and people systems of lean
production, the interaction of these systems, and the integration of technical and people
systems affects department performance, the perceptions of department performance, and
work-related attitudes. A model was developed suggesting that the integration of
technical and people systems will predict department performance, perception of
department performance, and work-related attitudes.

Two manufacturing facilities from the automotive supplier industry participated
in the study. A total of 533 employees provided survey data. The responses to this
survey were used as a measure of people systems of lean production. A technical
systems assessment instrument was used to measure the extent to which the technical
systems of lean production had been implemented at’the department level. The total of
51 technical systems assessment instruments were completed (n = 51). A total of 121
supervisors and superintendents provided survey data regarding perceived department
effectiveness attributable to the implementation of lean production. Department archival
performance data was provided by one of the two plants. Department performance
measures included the number of employees to make at least one suggestion for the 1999

calendar year by department and shift as suggestion participation rate. Department

performance measures also included uptime by department and shift for an eight -month
period (January through August, 1999). Complete archival data was provided for 26
departments (n = 26).

The results of this study suggest that people systems predict work-related attitudes
and inﬂuence perceptions of department performance by employees. Speciﬁcally, people
systems were signiﬁcantly related to commitment to lean strategy, job satisfaction,
learning environment, and team efﬁcacy. Technical systems were strongly related to
management perception of department performance. The people systems composite was
signiﬁcantly related to employee perceptions of department performance, but not people
systems lean training. In contrast, the reverse relationship was shown for management
perception of department performance. However, technical systems and people systems
were not signiﬁcantly related department archival performance data. People systems
composite was found to moderate the relationship between technical systems and work-
related attitudes (i.e., job satisfaction), and people systems lean training moderated the
relationship between technical systems and work-related attitudes (i.e., team efﬁcacy).
Integration did not show a mediation effect on the relationship between technical systems
and people systems with department archival‘performance. However, integration did

have a direct effect on department archival performance.

Copyright by
William Mark Mothersell
2000

DEDICATION

This dissertation is dedicated to my wife Jeannie for the freedom, to my daughter Natalie
and my son Robert for their love, my parents for their unconditional support, and my

brother Bob for the courage.

ACKNOWLEDGEMENTS

I have many people to thank for my success at Michigan State University. I
would to thank my committee Michael Moore, Ed Montemayor, Peter Berg, Kevin Ford,
and Dan Ilgen for their efforts in making this a better dissertation. I would like to thank
the School of Labor and Industrial Relations and its faculty and staff for the opportunity
to complete the Ph.D. program. The faculty and staff have always been available and
supportive.

In particular, I would like to thank my chair and advisor, Michael Moore for his
continuous and unwavering support throughout my Ph.D. program. I have had an
opportunity to work closely with Michael Moore over the last few years. In addition to
being intellectually engaging, he has provided incredible learning opportunities, and he
has demonstrated remarkable support for students who are fortunate enough to get to
know him, a trait I hope to carry forward with me. I would also like to speciﬁcally thank
Kevin Ford who has provided support and guidance throughout much of my Ph.D.
program and speciﬁcally for his assistance in completing the dissertation. Ed
Montemayor has provided valued counsel and support throughout my time at Michigan
State University and speciﬁc support with data analysis. Also, I would like to thank Dan
Ilgen for his invaluable advice and Peter Berg for his assistance throughout my
dissertation.

I would also like to thank the Work Practices Diffusion Team for the exciting and
forever evolving research project. Shobha Ramanand has provided encouragement and
friendship. John Schweitzer has provided analytical support and more importantly has

instilled conﬁdence throughout the data analysis phase of my research. Kristi White has

vi

provided kindness and great support. Randy Fotiu has provided SPSS and statistics
advice and counsel, which saved me a tremendous amount of time.

Lastly, I would like to thank all those who supported this research project from
the sites that participated in this study. While it is necessary to maintain conﬁdentiality
of the sites that participated in this study, I would like to speciﬁcally thank Johan for
intellectual leadership and his initial support in providing entry for this study. I would
also like to thank Beth and Bonnie for all of their long hours and valuable advice
throughout this extended research project. Beth and Bonnie never complained about my

requests despite their many other immediate pressures.

vii

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION ......................................................... 1
Introduction to the Study ............................................................ 1
Statement of theProblem ............................................................ 3
Importance of the Topic ........................................................... 3
Research Need ........................................................................ 4
Deﬁnition of Lean Production .................................................... 5
Purpose of the Study ............................................................... 6
Research Questions ................................................................... 10
The Research Context: Automotive Supplier Industry .......................... 11
Research Methods .................................................................... 13
Contribution and Limitation of this Dissertation ............................... 15
Organization of this Dissertation ................................................ 16
List of References ................................................................... 18

CHAPTER 2: LITERATURE REVIEW ................................................ 22
A Conceptual Framework for the Diffusion of Lean Production ............ 22
Sociotechnical Systems ............................................................... 25
High Performance Work Practices ................................................. 29
Integration ............................................................................. 38
Hypotheses ............................................................................ 44
List of References .................................................................. 48

CHAPTER 3: RESEARCH METHODOGY ........................................... 52
Gaining Access ........................................................................ 52
Research Sites ......................................................................... 54
Subjects ................................................................................. 56
Measurement of Variables ........................................................... 60

Independent Variables ....................................................... 62
Mediating Variables ......................................................... 67
Dependent Variables ........................................................ 68
Covariate ...................................................................... 70
Organization and Assessment Structure .................................. 70
Data Collection Issues ................................................................ 72
Phase 1: Identifying the Sample ............................................ 72
Phase II: Testing the Assessment Instruments ........................... 73
Phase III: Collecting Quantitative Data ................................... 74
Phase IV: Collecting Qualitative Data .................................... 75
Data Analysis Procedures ............................................................ 76
Factor Analysis ............................................................... 76
Hierarchical Regression ..................................................... 77
List of References .......................................................... 83

viii

TABLE OF CONTENTS (CONTINUED)

CHAPTER 4: RESULTS .................................................................. 84
Descriptive Statistics and Correlation analysis .................................. 84
Perceptions Regarding the Implementation of Lean Production. . . .. 84
Implementation of the Technical Systems of Lean Production ...... 88
Lean Production and Perceived Effectiveness .......................... 90
Correlation of Independent and Dependent Variables ................. 92
Factor analysis ....................................................................... 95
Factor Analysis of People Systems ....................................... 95
Factor Analysis of Technical Systems ................................... 98
Results of Analyses by Hypothesis ................................................ 100
Regression ................................................................... 101
Hypothesis 1 ........................................................ 101
Hypothesis 2 ........................................................ 103
Hypothesis 3 ........................................................ 105
Hypothesis 4 ........................................................ 107
Hypothesis 5 ........................................................ 109
Moderated Regression ...................................................... 110
Hypothesis 6 ........................................................ 110
Hypothesis 7 ........................................................ 111
Hypothesis 8 ........................................................ 112
Mediated Regression ....................................................... 116
Hypothesis 9 ........................................................ 116
Hypothesis 10 ...................................................... 119
Hypothesis ll ...................................................... 119
Summary of hypotheses ............................................................. 124
CHAPTER 5: DISCUSION ............................................................... 129
Summary .............................................................................. 129
Implications for Research and Theory ............................................ 133
Implications for Practice ............................................................ 136
Suggestions for Future Research ................................................... 138
List of References .................................................................... 140
APPENDICES ................................................................................ 141
Appendix A ........................................................................... 142
Appendix B ........................................................................... 155
Appendix C .......................................................................... 171
Appendix D .......................................................................... 176
Appendix E .......................................................................... 180
Appendix F ........................................................................... 183

ix

Table 3.1

Table 3.2

Table 3.3

Table 3.4

Table 3.5

Table 3.6

Table 3.7

Table 3.8:

Table 4.1:

Table 4.2

Table 4.3

Table 4.4

Table 4.5

Table 4.6

Table 4.7

List of Tables

Number of Respondents by Functional Area, Plant Location and

Total ......................................................................... 57
Demographic Proﬁle by Plant Location and Total .................... 58
Total and Percent Participation in Lean Training by Facility and

Total ......................................................................... 59
Variables, Assessment Instruments, and Items on the Surveys. 60

Independent, Mediating and Dependent Variables, and

Operational Constructs .................................................... 64
Development of Survey Items and Source for Perceptions

Regarding the Implementation of Lean Production ................... 181
Assessment Instrument, Data Source, Number of Respondents. 66
Research Hypotheses ...................................................... 79

Descriptive Statistics, Correlations and Reliability Coefﬁcients
for People Systems of Lean Production ................................. 86

Descriptive Statistics and Correlations for Technical
Systems of Lean Production ............................................. 89

Means, Standard Deviations, and Correlations of Perceived
Effectiveness of Lean Production as Rated by Supervisors
and Superintendents ....................................................... 91

Correlation of Independent Variables with Dependent
Variables .................................................................... 94

Results of Factor Analysis of Lean Production People
Systems Scales ............................................................. 97

Results of Factor Analysis of Lean Production Technical
Systems ..................................................................... 99

Regression Results of the Test for Technical Systems Effect
on Suggestion Participation Rate and Uptime ......................... 103

Table 4.8 Regression Results of the Test for Technical Systems Effect
on Perceived Effectiveness and Work-Related Attitudes ............. 105

Table 4.9 Regression Results of the Test for People Systems Effect on
Suggestion Rate and Uptime .............................................. 106

Table 4.10 Regression Results of the Test for People Systems Effect on
Perceived Effectiveness and Work-Related Attitudes ................ 107

Table 4.11 Regression Results of the Test of Moderation of Technical
and People Systems on Suggestion Participation Rate and
Uptime ....................................................................... 114

Table 4.12 Regression Results of the Test of Moderation of Technical
and People Systems on Perceived Effectiveness and Work-
Related Attitudes ........................................................... 115

Table 4.13 Regression Results of the Test of Integration as a Mediator

for Technical and People Systems Effect on Suggestion

Participation Rate and Uptime ........................................... 118
Table 4.14 Regression Results of the Test of Integration as a Mediator

of Technical and People Systems Effect on Perceived

Effectiveness and Work-Related Attitudes ............................. 123

Table 4.15 Comparison of Hypothesized Versus Actual Relationships of
Research Findings ......................................................... 125

Table 4.16 Correlation of Independent, Mediating and Dependent Variables... 184

xi

Figure 1.1

Figure 1.2

Figure 2.1

Figure 2.2

Figure 2.3

Figure 2.4

Figure 3.1
Figure 3.2
Figure 3.3

Figure 3.4

List of Figures

Production Systems: Comparison of Mass and Lean Models of
Production .................................................................. 6

Conversion Approaches of Brownﬁeld Mass Production Plants
into Lean Production Facilities .......................................... 9

A Framework for Comparative Analysis of Alternative
Approaches in the Diffusion of Lean Production in Brownﬁeld

Sites .......................................................................... 23
Potential Beneﬁts and Risks of a Downgrading Strategy versus an
Upgrading Strategy ........................................................ 32
Two Systems of Workplace Industrial Relations ...................... 34
Control and Commitment HR Systems in Predicting

Manufacturing Performance ............................................. 34
Organization and Assessment Structure ................................ 71
Research Diagram for Hypothesis 1 through Hypothesis 5 .......... 78
Research Diagram for Hypothesis 6 through Hypothesis 8 ......... 80

Research Diagram for Hypothesis 9 through Hypothesis 11...... 82

xii

CHAPTER ONE

INTRODUCTION

Introduction to the Study

U. 8. manufacturing is currently undergoing a transformation of historical
signiﬁcance. In the late nineteenth and early twentieth centuries, manufacturing went
through the transformation from craft to mass production (Ford, 1926; Piore, 1984;
Taylor, 1967; Womack, 1990). Now, as we enter the twenty ﬁrst century, mass
production is giving way to a new paradigm described variously as lean production
(W omack, 1990), agile production (Preiss, 1997), knowledge-driven work (Cutcher-
Gershenfeld, 1998), ﬂexible manufacturing (Piore, 1984), innovative-mediated
production (Kenney, 1993), and sleek production (Handyside, 1997).l Documenting and
understanding the core elements of this new approach to manufacturing is critical to the
competitive success of U. S. industry.

This pressure on manufacturers is driven by global competitive pressure. In many
segments of manufacturing, lean production has been viewed as the key to Japanese
competitive success (W omack, 1990; Womack, 1996). As such, lean manufacturing has
become a critical global business strategy for many manufacturers. However, others
argue that it is not the mastery of manufacturing that explains the success of Japanese
manufacturing industry. Rather, it is the capability of Japanese companies to
continuously create organizational knowledge (Nonaka, 1995) as well as the intangible
elements of the work system (Cutcher-Gershenfeld, 1998; Lin, 1995). By ignoring these

people elements of lean production, organizations may be undermining the catalyst for

achieving a competitive advantage. Yet, many manufacturers continue to benchmark and
attempt to incorporate the technical aspects of the emerging production system and
largely ignore or fail to fully appreciate the people elements.

What is curious about the current transformation is how few manufacturers have
successfully imitated the Toyota Production System (T PS) (Spear, 1999). For example,
GM, Ford and Daimler-Chrysler have independently created major initiatives to develop
world-class production systems based on the TPS model. Automotive suppliers have also
constructed major initiatives to develop and implement lean production (Moses, 1999).
Yet, few organizations have reached the levels of manufacturing performance of Toyota.

This latest wave in the adoption of lean manufacturing is a system-wide
perspective (Adler, 1993a; Kenney, 1993). This strategy attempts to adopt the entire lean
production system and not borrow disconnected components of a larger system (Cutcher-
Gershenfeld, 1998; Handyside, 1997). The elements of lean manufacturing, often
discussed in the popular press include; Quality Circles, Employee Involvement,
Statistical Process Control, J ust-in-Time Inventory, Total Quality Management, Total
Productive Maintenance, and Teams-Based Work Systems (Ohno, 1988; Toyota, 1992).

Manufacturers have increasingly adopted various components of lean
manufacturing processes and practices with various levels of success (Keller, 1992).
There has been considerable debate regarding what cultural components and human
resource management practices and processes are consistent with, promote and sustain
lean manufacturing (Adler, 1993b; MacDufﬁe, 1992). There is some evidence that team-

based work systems and “high commitment” HR practices - including extensive training,

 

' Lean production is currently the most used term to characterize this emerging paradigm. Lean production
and lean manufacturing are used interchangeably to portray this new paradigm.

suggestion systems, and problem solving groups - are compatible with lean
manufacturing (Arthur, 1994; Arthur, 1992; MacDufﬁe, 1995a; MacDufﬁe, 1995b). Yet,
manufacturers’ continue to struggle in putting the pieces together into a new cohesive

whole.

Statement of the problem

The driver for change is clear. The MIT auto study (W omack, 1990) revealed a
clear performance gap between the Japanese producers compared to the US, European
and emerging auto producers (e. g., Korea, Brazil, and East Asia). For example, the MIT
auto study identiﬁed the performance gap between the Japanese and US producers as:
(1) half the defects in ﬁnished cars; (2) half the hours of human effort in factories; (3) a
tenth or less of in-process inventories; (4) half the factory space for the same output; and,
(5) two-thirds of the product development time. This performance gap was not just a
US. and Japanese phenomenon, but even larger gaps were revealed for the European
producers and the emerging auto producers. The MIT auto study forcefully argued that

the performance gap is attributable to lean production.

Importance of the Topic

Getting this mass to lean conversion process right has massive implications for
US. industry. Hundreds of thousands of manufacturing jobs in U. S. industry are at
stake. The major auto producers, General Motors, Ford, Diamler-Chrysler alone provide
the main employment for many U. S. communities. Add in the automotive supplier base

and millions of jobs can be seen as the stakes of a successful conversion.

While the implications for the US. economy are dramatic, the shift to lean
production is a global phenomenon (Kenney, 1993; Rinehart, 1994; Shadur, 1995). This
change has been an evolutionary process as manufacturers come to grips with intensive
competition. That is, this is not a sudden and dramatic shift to new work practices, but a
change that has emerged over the last 10-15 years (Adler, 1988; Cole, 1990; MacDufﬁe,
1997; Womack, 1996). During this period manufacturers have changed and developed
work practices in ways that are consistent in some cases and inconsistent in other cases
with the principal components of lean manufacturing. A key challenge for many
manufacturing organizations is to identify and implement work practices that are fully

integrated and maximize the full potential of lean production.

Research Need

This shift to lean production has been wide spread and has spurred increased
research (Adler, 1993b; Florida, 1991; Klein, 1991). Much of this research activity in the
US. has focused on the Japanese transplants (Jenkins, 1994; Jenkins, 1999). Another
sector that appears to be making progress in adopting lean manufacturing is auto
suppliers (Florida, 1996; MacDufﬁe, 1997). The big three auto companies in the US.
have all initiated activities to adopt lean manufacturing as the predominate production
system - in an effort to replace “Taylorist” mass production. The electronics industry has
also been studied (Kenney, 1993; Kenney, 1995). Ironically, in the US. the electronics
industry has largely accepted traditional U.S. mass production as well as traditional U.S.
human resource and labor relations policies and practices. There are current activities in
the aerospace industry that is attempting to apply the principles of lean production to both

the public and private components of the aerospace industry (W omack, 1996).

Clearly there are signiﬁcant efforts by many organizations as well as entire
industries making the shift from mass to lean production and these efforts to become lean
are not limited to the manufacturing industries. The technical elements of lean
production have been extensively studied (Fry, 1987; Hyer, 1984; Womack, 1990).
However, few empirical studies have directly studied the people elements of lean
production and only one empirical study was found that examined the integration of the
technical and people elements of lean production (MacDufﬁe, 1992). The people

elements of lean production will be deﬁned in Chapter Two.

Deﬁnition of Lean Production

Figure 1.1 provides an abbreviated comparison of lean and mass production. It is
offered as an overview of some of the key differences in the two production systems. As
can be seen in this Figure, there are a number of fundamental differences between lean
and mass production. Some of these differences appear to be mirror opposites of one-
another. For example, traditional mass production is often characterized as consisting of
numerous job classiﬁcations, tightly supervised workers, with little or no job rotation,
which results in deskilling of the workforce (items 2, 3, 4 and 6). In comparison, lean
production can be characterized as using frequent job rotation, teams as a core building
block of the production system, with few formal job classiﬁcations, which interact to

deve10p and maintain a multiskilled workforce (items 12, 13, 14 and 16).

Figure 1.1

Production Systems: Comparison of Mass and Lean Models of Production

 

Mass Production

Lean Production

 

1. High levels of functional specialization
2. Infrequent job rotation

3. Tightly supervised, machine paced
production work

4. Many job classiﬁcations

5. Problem solving by experts

6. Deskilled workforce

7. Work standards performed and imposed on
workers

8. Wages and promotion based on seniority

9. Adversarial labor-management relations

10. Arms-length relations with suppliers, many
suppliers, short-term focus

11. High levels of functional integration
12. Frequent job rotation

13. Team-based production

14. Few job classiﬁcations

15. Kaizen (continuous improvement) by small
group problem solving

l6. Multiskilled workforce

17. Team members and team leaders actively
construct and improve work standards

18. Wages and promotion based on seniority,
merit and teamwork

19. Cooperative labor-management relations

20. Tight inter-ﬁrm linkage with supplier, few
suppliers, long-term focus

 

Adapted from (Cusumano, 1994; Florida, 1991; Jenkins, 1994)

Purpose of the Study

The purpose of this study is to increase understanding of the people elements that

foster and support the technical elements in the diffusion of lean production. While the

research and practitioner literatures are beginning to understand the management

practices and processes that are necessary to encourage lean manufacturing, little

empirical evidence is available to support their ﬁndings or deﬁne how individual and

group attitudes relate mutually with the production system. Additionally, there is little
empirical evidence that supports the position that investing in the people aspects of lean
production has a positive impact on performance beyond the technical elements of lean
production. This study will examine the relationship between the technical and people
elements of lean production as well as the integration of these elements in the
implementation of lean production. More speciﬁcally, this study will identify the key
characteristics of lean production and link these characteristics with effectiveness data
and work-related attitudes.

The technical elements of lean production will be deﬁned by six factors that are
crucial in the conversion to lean production. These six factors include: (1) flow
manufacturing; (2) employee environment and involvement; (3) workplace organization;
(4) quality; (5) operational availability; and, (6) material movement. The people
elements of lean production will be deﬁned by 13 factors, which include the following:
(1) supervisory behaviors; (2) management support; (3) cooperative union-management
relations; (4) development focus; (5) managing change; (6) teamwork; (7)
involvement/psychological participation; (8) process focus; (9) proactive problem
solving; (10) workplace trust; (1 l) workplace bonding; ( 12) workplace bridging; and,
(13) conﬂict resolution climate. The mediating variable for assessing the level of
integration is based on four items, which includes (1) The performance of standardized
work; (2) Team work adjustments to match takt time; (3) Problem solving is used and
consistently followed; and (4) That problem solving has become a methodology for
management change. The dependent variables include department performance data, and

individual and group work-related attitudes. Department archival effectiveness factors

will include suggestions and productivity measures as well as perceived department
performance. Department and individual work-related attitude factors will be deﬁned by
four factors, which include; (1) commitment to lean strategy; (2) job satisfaction; (3)
perceived learning environment; and, (4) team efﬁcacy.

The premise of this research proposal is that plants in the process of converting
from mass to lean production fall into one of two quadrants. In Figure 1.2 below,
traditional mass production (quadrant 1) brownﬁeld plants will follow either a
technologically focused approach to the diffusion of lean production (quadrant 4) or an
integrated approach to lean production (quadrant 3). The technological approach to lean
production will concentrate on the technical elements of lean production. Examples of
the technical elements of lean production commonly presented in the literature (Ohno,
1988; Spear, 1999; Toyota, 1992; Womack, 1990) include the following: (1) inventory
levels (e. g., J IT and kanban systems); (2) lot sizes for purchased or manufactured
components; (3) standardized work; (4) andon boards and cords; (5) technology centered
information systems; and, (6) error prooﬁng processes.

While some organizations will be primarin centered on these technical elements
of lean production, others will pay attention to the technical elements but also focus
attention on the people elements of lean production. Some examples of the people
elements of lean production include the following: ( 1) Process and product focus as
opposed to solely a product focus; (2) Efforts to create a labor-management climate
consistent with lean production; (3) The creation of a problem solving focus that allows

workers to resolve problems at the lowest possible level (at their source); and, (4) The

creation of a learning environment that allows idea generation and solution

 

 

implementation.
Figure 1.2
Conversion Approaches of Brownﬁeld Mass Production Plants
into Lean Production Facilities
Mass Lean

(2) (3)
High Commitment Sociotechnical Integrated
Systems Approach

(1) (4)

. Traditional Technology

Low Comrrutment Mass Centered
Production Approach

 

 

 

 

The sociotechnical systems approach (quadrant 2) represents organizations that
have adapted the social system to improve organization performance and quality of work
life consistent with the STS perspective (Chems, 1978). There are numerous examples of
organizations that have undertaken such initiatives. Using the framework presented in
Figure 1.2, this cell represents organizations that will be make the conversion from STS
(quadrant 2) to lean production context, and will follow either an integrated approach
(quadrant 3) or a technology centered approach (quadrant 4). Examples of organizations
making this shift from STS to lean include Saab, Volvo, and the Ford Sharonville Plant.
However, the participating organizations in this study are both traditional mass

production plants (quadrant 1) converting to lean production (quadrant 3 or 4).

Accordingly, the conversion from STS to lean production will not be part of this study.
This conceptual framework (Figure 1.2) will be more fully examined in Chapter Two.

The key assumption of Figure 1.2 is that organizations will follow one of two
strategies in diffusing lean production. Some organizations will interpret and understand
lean production as a technological innovation, while others will seek to understand and
implement lean production based on employees playing a different role in lean
production compared to traditional mass production systems. Plants converting to lean
production using a people focused approach will also implement the technical aspects of
lean production, but will do so in a way that encourages and involves employees
substantially in the implementation and adjustments to the new work system.

This study explores both the technical elements and people elements of lean
production as well as the integration of these elements in the conversion of brownﬁeld
plants into lean production facilities. Moreover, this study will assess the impact of these
different approaches on department performance measures and work-related attitudes.
This study will attempt to add insight into identifying what factors differentiate plants
that pursue a technical approach to lean production versus an integrated sociotechnical
approach. This integrated approach of people and technology in converting from to lean

production is the key contribution of this study to the current body of knowledge.

Research Questions

The primary research questions for this study are: (1) Do departments that have
implemented both the technical and people elements of lean production outperform those
departments that have implemented just the technical elements of lean production? (2)

Do departments that have integrated the technical and people elements of lean production

10

outperform departments that have implemented just the technical elements of lean
production and outperform departments that have implemented both the technical and

people elements in an un-integrated way?

The Research Context: Automotive Supplier Industry

Automotive companies and parts suppliers have undertaken immense initiatives to
convert established brownﬁeld facilities into best-in-class lean production plants (Spear,
1999). At its core, the implementation of lean production in existing plants requires
substantial rethinking of existing policies and practice as well as core assumptions and
behaviors of employees, managers and union leaders (Bluestone, 1992; Kenney, 1993;
Womack, 1990). The successful transformation of existing plants into lean production
facilities is a critical and fundamental building block for the future of these companies.
While some greenﬁeld plants have been cited as lean production facilities, few
brownﬁeld plants within these competitors have made this transition successfully.

A distinction often cited in the literature is the differences between brownﬁeld
and greenﬁeld facilities. A brownﬁeld site is an existing enterprise or manufacturing
plant that attempts to make a signiﬁcant change within a current facility. For example, if
an existing manufacturing plant attempted to implement team-based work systems, this
would be a signiﬁcant change initiative within a brownﬁeld site. The term greenﬁeld site
is used to connote an effort by an organization to create some type of signiﬁcant change
initiative when it launches a new facility. Organizations will often attempt to create new
work systems and practices when establishing a new work site and the hiring of a new
workforce. For example, when a manufacturing ﬁrm launches a new facility, it might

establish team-based work systems and fewer organizational levels from the outset.

ll

This distinction between greenﬁeld and brownﬁeld carries with it a recognition
that large-scale change is more difﬁcult in a brownﬁeld site. The reasons often given for
the increased difﬁculty are associated with the unfreezing or unleaming that most occur
before new routines can be learned and institutionalized. However, in the case of a
greenﬁeld, old routines, organizational structures, and preexisting cultures do not need to
be changed and unleamed before an organizational change is implemented. The
objective of a geenﬁeld is to avert the entrenched work culture that might impede the
introduction of new ideas and technology (Huczynski, 1987).

The difference between brownﬁeld and greenﬁeld is an important distinction for
the study at hand. The organizations participating in this research are both brownﬁeld
facilities. The challenges these two organizations face in converting to lean production
are very similar to what other manufacturers’ face in attempting to make this
transformation. If, as many argue (Kenney, 1993; Womack, 1990; Womack, 1996), most
manufacturers most become lean producers to remain competitive or even survive in the
future, then there are an enormous number of brownﬁeld sites that most make this
conversion. The lessons to be learned in the brownﬁeld conversion to lean production
have immense potential consequences at the local, state, regional, national and global
level.

The manufacturing industry (Standard Industrial Code 20-39) is a critical part of
the US. economy and in 1998 employed 16% of the US. workforce (BLS, 1998). As a
percent of the total US. gross domestic product the manufacturing sector represented
17% of GNP in 1997 (BEA, 1997). Approximately one in six employees in the US.

economy are directly employed in the manufacturing sector (BLS, 1998).

12

The competitive pressure on component suppliers has intensiﬁed as a result of
efforts by the domestic automotive companies to reduce the number of suppliers. For
example, General Motors (GM) and Ford Motor Company (Ford) has signiﬁcantly
reduced their number of suppliers, and the number of suppliers are projected to continue
to decline. For example, between 1979 and 1991 the “Big Three” closed 80
manufacturing facilities (McAlinden, 1993). In 1999 GM spun off its supplier
organizations and Ford and the United Auto Workers (UAW) reached an agreement that
will allow Ford to spin off its supplier organizations in the coming months furthering the
competitive pressure in the market place (McCracken, 1999; White, 1999a; White,
1999b). The competitive pressure in this industry has also increased by the number of
foreign car companies locating facilities within North America. This has resulted in these
companies relocating their respective preferred suppliers from their home countries to
North America. As a result, some of the top global suppliers already are locating supplier
plants in direct competition with the current supplier base in North America. In short,
becoming a lean producer is critical to the long-term success and survival for many

organizations within the automotive supplier industry.

Research Methods

The objective of this research is to study the effects of alternative approaches in
the implementation of lean production. The research focuses on individuals and groups
of individuals who make up the organization, their perceptions regarding the
implementation of lean production, and the impact of alternative approaches on

department performance. There are three key objectives to this study:

13

1. To investigate to what extent the integration of the technical and people
elements of lean production affect department performance and work-related
attitudes at the department level.

2. To investigate to what extent the people elements of lean production affect
department performance and work-related attitudes at the department level.

3. To investigate to what extent the technical elements of lean production affect
department performance and work-related attitudes at the department level.

The catalyst for this study resulted from the sheer size of the transformation
currently taking place in the manufacturing sector with the immense academic
opportunities inherent in such a large-scale change coupled with the enormous practical
implications in the conversion of brownﬁeld work sites into lean production facilities.
This research will investigate the effects of different strategies or approaches to the
implementation of lean production. It will assist in the identiﬁcation of the changing
roles of workers in this emerging work system and how it differs from traditional mass
production. Therefore, this research is designed to identify the differential impact of the
technical and people centered approaches, and more importantly the impact of the
integration of the people and technical elements in the implementation of lean
production.

Data were collected using multiple methods, which included the following: (1)
survey data to assess the people elements of lean production; (2) an assessment
instrument to measure the technical elements of lean production; (3) an assessment
instrument to assess the integration of the technical and people elements of lean
production; (4) archival performance data at the department level; (5) individual

interviews of key organizational leaders and internal experts; and, (6) follow-up

14

interviews with internal experts to provide understanding of the results of this
investigation.

Multiple regression will be used to test the relationship between the technical
systems, people systems and the integration of these elements of lean production with
department performance and work-related attitudes. Multiple regression analysis permits
the simultaneous analysis of multiple independent variables inﬂuence on dependent
variables (Kerlinger, 1986:138). Multiple regression analysis allows for the assessment

of whether each independent variable signiﬁcantly predicts the dependent variable.

Contributions and Limitations of this Dissertation

This study will conﬁne itself to the component industry supplying the automotive
manufacturers and assemblers in North America. As such, clear generalization of the
results will be limited to this industrial sector. While the company that participated in
this study is a global manufacturer and international supplier of automotive component
parts, it will be difﬁcult to generalize outside the US. This study will be able to suggest
that these same basic people and technical elements and the integration of these elements
are necessary to fully capture the full potential of lean production across the industry and
international boundaries. However, conﬁrmation of this relationship will require future
empirical research.

A cross-sectional survey design simultaneously surveys a number of different
groups to assess differences at the time of the survey (Saslow, 1982: 16). The primary
limitation of a cross-sectional survey design is that the direction of the relationships
between the independent and dependent variables cannot be determined. To obtain a

clearer understanding of the relationships between the technical elements, people

15

elements, the integration of these elements, and its impact on department effectiveness
and work related attitudes would require longitudinal analysis. Another limitation in this
study is the difﬁculty in obtaining common performance measures across departments.
To compensate for this limitation, perceptions of department performance will be
obtained from three different organizational levels (i.e., hourly workers, supervisors, and
superintendents) from each site, which will allow for correlation analysis of perceived
and actual department performance.

This study aims to provide key contributions to the existing literature. The lean
production literature has largely ignored people issues and measurement. This study puts
the people aspects of the production system center stage. The study identiﬁes
components of the people system and develops speciﬁc measures. This study also
focuses at the department level by attempting to link workplace attitudes and department
performance with lean production. The study uses multiple sources of data to test a
model of people and technology integration. These data sources include workers,
supervisors, superintendents and HR managers using both qualitative and quantitative
instruments. In addition, this study contributes by assisting organizations in the diffusion

of lean production.

Organization of this Dissertation

This dissertation will include ﬁve chapters. Chapter One provides the purpose for
the study, the rationale underlying the research objectives as well as the potential
contribution of this dissertation. Chapter Two contains a focused review of the
sociotechnical systems and high performance work practice literature. The methods

section is presented in Chapter Three. It includes the research design, organizations

16

involved in the study, the subjects for this research, the data collection procedures, the
operationalization of the variables, and the method for data analysis for each hypothesis.
The results of the data analysis will be presented in Chapter Four. The conclusions,
implications for theory and practice, and future research will be presented in Chapter

Five.

17

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(Rd), Remade in America: transplanting and transforming Japanese management
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Kenney, M., and Richard Florida. (1993). Beyond Mass Production: The Japanese
System and its Transfer to the U.S. New York: Oxford University Press.

Kenney, M., and Florida, Richard. (1995). The Transfer of Japanese Management Styles
in two US Transplant Industries: Auto and Electronics. Journal of Management
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Klein, J. (1991). A Reexamination of Autonomy in Light of New Manufacturing
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l9

MacDufﬁe, J. P., and John F. Krafcik,. (1992). Integrating technology and human
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MacDufﬁe, J. P., and Thomas A. Kochan. (1995a). Do U.S. ﬁrms invest less in human
resources? Training the world auto industry. Industrial Relations, 34(2), 147-168.

MacDufﬁe, J. P. (1995b). Human resource bundles and manufacturing performance:
Organizational logic and ﬂexible production systems in the world auto industry.
Industrial & Labor Relations Review, 48(2), 197-221.

MacDufﬁe, J. P., and Helper, Susan. (1997). Creating Lean Suppliers: Diffusing Lean
Production Through the Supply Chain,. California Management Review, 39(4),
1 18-15 1 .

McAlinden, S. P., and Smith, Brett C. (1993). The changing structure of the U.S.
automotive parts industry (U.S. Department of Commerce, Economic
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Automotive Transportation, University of Michigan Transportation Research
Institute.

McCracken, J. (1999, September 27, 1999). Delphi experience makes UAW wary of
Ford's Visteon spinoff. Automotive News, pp. 30P.

Moses, A. R. (1999, May 27, 1999). Delphi takes ﬁnal step to independence from GM.
The Associated Press State & Local Wire, pp. Business News Section.

Nonaka, 1., and Takeuchi, Hirotaka. (1995). The knowledge-creating company: How
Japanese companies create the dynamics of innovation. New York: Oxford
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Ohno, T. (1988). Toyota Production System: Beyond Large-Scale Production.
Cambridge: Productivity, Inc.

Piore, M. a. 8., Charles. (1984). The Second Industrial Divide: Possibilities for
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Preiss, K. (1997). A systems perspective on lean and agile manufacturing. Agility &
Global Competition, 1(1), 56-76.

Rinehart, J ., Huxley, Christopher, Robertson, David. (1994). Worker commitment and

labour management relations under lean production at CAMI. Relations
Industrielles, 49(4), 750-766. '

20

Shadur, R., and Bamber. (1995). Factors Predicting Employees Approval of Lean
Production. Human Relations, 48(12), 1403-1426.

Spear, 8., and Bowen, H. Kent. (1999). Decoding the DNA of the Toyota Production
System. Harvard Business Review, 77(5), 96- 106.

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Norton & Company.

Toyota. (1992). The Toyota Production System . Toyota City: Toyota Motor Corporation,
Operations Management Division.

White, J. B., and Ball, Jeffrey. (1999a, October 11, 1999). Ford, UAW Reach Accord On
Contract. Wall Street Journal, pp. A3.

White, J. B. (1999b, September 29, 1999). UAW, GM and Delphi Tentatively Agree To
National Contracts in Peaceful Talks. Wall Street Journal, pp. B-2.

Womack, J. P., Daniel T. Jones, and Daniel Roos. (1990). The Machine that Changed the
World. New York: Macmillan Publishing Company.

Womack, J. P. a. D. T. J. (1996). Lean Thinking: Banish Waste and Create Wealth in
Your Corporation. New York: Simon & Schuster.

21

CHAPTER TWO

LITERATURE REVIEW

As stated in Chapter One, the purpose of this dissertation is to investigate the
effect of alternative approaches to the diffusion of lean production on work-related
attitudes and department performance. This research will test the effect of an integrated
approach to lean production versus a more technology focused approach. This Chapter
will present a conceptual framework that will be used to provide a focused review of the

relevant theory and provide a foundation for analysis.

A Conceptual Framework for the Diffusion of Lean Production

Figure 2.1 presents a framework and perspective on converting brownﬁeld mass
production organizations to lean production. It will also furnish a foundation for
comparative analysis and provide a basis to utilize existing theories. There are four
characteristics of this perspective that will provide the foundation for this study.

The horizontal axis represents two major alternatives to manufacturing, which are
mass and lean production. Both of these alternative production systems are brieﬂy
deﬁned in Chapter One. The distinctions between lean and mass production has been
extensively discussed in the practitioner and academic literatures. Whether a
manufacturing organization is a mass versus lean production facility can be determined
by assessing speciﬁc production practices. For example, assessing production practices
such as inventory turns, part lot size, existence of standardized work, and usage and
method of application of an andon system could be used to determine which category

best characterizes a speciﬁc plant or work unit.

22

Figure 2.1

A Framework for A Comparative Analysis of Alternative Approaches
in the Diffusion of Lean Production in Brownﬁeld Sites

 

 

Production Systems
Mass Lean
High Commitment Sociotechnical Integrated
8 Approach
ystems
People systems
Low Commitment Traditional Technology
Mass Centered
Production Approach

 

 

 

 

The vertical axis represents the people systems in the differing production
systems. As can be seen in this framework, different people systems and practices can be
applied in both mass and lean production systems. While, the literature is rich with
descriptions of the differences and similarities in mass and lean production from a
technical perspective, little in the Mhavioral sciences literature speciﬁcally address
people systems in the context of the conversion from mass to lean production. The
literature that does exist builds on the high performance work practices and
sociotechnical systems perspectives. While much of the research in this area has been
conducted in a mass production context, it provides a viable theoretical basis for this
study. As such, these literatures will be used to provide the theoretical foundation for the

vertical axis in the present study. For example, these literatures could be used in

23

determining whether an organization has adopted a high commitment or low commitment
strategy by assessing such factors as training and development efforts, employment
security, selectivity in recruiting, incentive pay systems, levels of employee
participation/involvement, and participation in suggestion systems.

The concept of high commitment and low commitment people systems has many
different names in the academic and practitioner literature. For example authors use
terms such as HR and IR systems (Arthur, 1992), HR bundles (MacDufﬁe, 1995b), high
performance work practices (Becker, 1996), and social systems (T rist, 1978) in this
literature. Distinctions are further delineated by conceptual frameworks such as
downgrading and upgrading strategies (Susman, 1986), control and commitment (Walton,
1985), and administrative and human-capital-enhancing (Youndt, 1996). For the purpose
of this paper the terms high commitment and low commitment people systems will be
used. However, when a speciﬁc author or literature base is cited or discussed the terms
appropriate to that citation will be used.

Using both the vertical and horizontal axis identiﬁes four alternative
characteristics of a work system. An organization that pursues a low commitment
approach to people systems and a mass production strategy could be viewed as
traditional mass production facility. These types of organizations could be characterized
with technology systems such as high inventory levels, high number of repairs, poor
visual management, and focused largely on production numbers. This type of
organization low commitment people system strategies might consist of such policies as
adversarial labor-management relations, low participation rates in suggestion systems,

and control oriented supervision. Those organizations that fall within the technology

24

centered approach to lean production would implement technology consistent with lean
production (e. g., small lot size, standardized work, and visual controls), and retain low
commitment people systems strategies (e.g., adversarial labor-management relations and
control oriented supervision).

As can be seen in the framework, the upper left-hand comer of Figure 2.1 might
be described as the application of sociotechnical systems in a mass production context.
This has been an area of much research (Rice, 1953; Trist, 1951; Walton, 1972). While
most of the sociotechnical systems (STS) research has been conducted in a mass
production context, more recent research is beginning to use the STS perspective in a
lean production context (Dankbaar, 1997; N iepce, 1998). Organizations that fall within
the sociotechnical systems category would be those organizations that use current mass
production technology, but pursue a high commitment people systems strategy.
Organizations that fall within the integrated approach have converted to lean production
both in terms of technology, but have also adopted high commitment people systems that
are integrated with lean production.

In short, this study will use this framework to identify a theory base for this study
and hopefully provide a basis for this research as well as future research. This four
quadrant framework offers a foundation for comparing and contrasting organizations in
terms of both the technical elements and the people elements of the work system as well

as the integration of technology and people systems in the lean production context.

Sociotechnical Systems
The Tavistock Institute of Human Relations was founded in London in 1946 with

the assistance of a grant from the Rockefeller Foundation. The Institute was founded

with the speciﬁc purpose of actively relating the social and psychological sciences to the
needs of society. The founding members of the Institute had been at the pre-World War
11 British Army Unit in the Tavistock Clinic and became known as the “T avistock
Group.” The Tavistock Institute evolved into three theoretical perspectives, called the
socio-psychological perspective, the socio-technical perspective and the socio-ecological
perspective (T rist, 1990). The sociotechnical systems perspective is the appropriate
theoretical framework for the study at hand.

The sociotechnical systems theory emerged from the Trist and Bamforth study of
coal-mining in Durham England (Scarbrough, 1995). This seminal work by Trist and
Bamforth (1951) contrasted the psychological and social problems associated with the
Taylorist work organization of the prevailing “longwall” approach to coal mining with
the pre-mechanization “shortwall” approach, in which multi-skilled autonomous teams of
miners organized task responsibilities (T rist, 1951). In these early studies by Trist and
others at the Tavistock Institute the researchers found in the mining industry that it was
possible within the same technological and economic constraints to operate different
systems of work organization with different social and psychological effects. These
ﬁndings demonstrated the signiﬁcant degree of organizational choice available to
management to enable them to structure the social and psychological aspects of work
(Pugh, 1997).

A key proposition offered by the STS perspective is that all work organizations
are composed of two interdependent systems, a social and technical system. That
changes in either the technical or social systems affects the other system. To obtain high

organizational performance and employee satisfaction, organizations must optimize both

26

the technical and social systems. Katz and Kahn accept the importance of the ﬁt between
the technical and social systems, but argue that some technical systems are compatible
with several arrangements of the social system while others require a speciﬁc type of
social system (Katz, 1978). Accordingly, a fundamental premise of the STS theoretical
perspective is the importance of ﬁt between the social and technical aspects of work
(McCuddy, 1978) and that effective work systems must jointly optimize the relationship
between these subsystems (French, 1995).

Given the assumption above that joint optimization is necessary for effective
work systems, this proposition does not eliminate the possibility that they may differ in
effectiveness. That is, that social systems may vary in a match with a technical system,
but the adaptation of the social system may provide improvements in the effectiveness of
the overall work systems. The question naturally arises of which social system will
provide the optimum conditions as distinct from those that are just good enough for any
given technical system (T rist, 1978).

More speciﬁcally, Bamforth argued that a production system could not be seen as
a technical system or a social system but had to be seen in terms of both of these systems
(Kelly, 1978). From this argument the matching or joint optimizing of the technical and
social subsystems would result in effective performance typically deﬁned in terms of
output, morale, absenteeism, etc. (Kelly, 1978). If either of the systems are maximized at
the cost to the other system would result in suboptimization of the work system.

An important criticism of the joint optimization of the social and technical
research is that the technical system has rarely been altered in sociotechnical

interventions. In the overwhelming majority of cases only the social systems have been

27

altered, while the technical systems remained unchanged (Kelly, 1978). In those few
cases were the technical system has been altered in conjunction with a sociotechnical
intervention rarely has the change initiative been maintained for extended periods of
time.

Kelly (1978) in his critical review of the STS literature speciﬁcally used machine
utilization in the STS literature as proof that such efforts were designed to bring
recalcitrant social systems into line. That is, that recalcitrant workers and social systems
must be brought into alignment with the technology to maximize machine output. In fact,
Kelly supports his position by arguing that STS scholars maintained as long ago as 1966
that the Tavistock studies had taken the technical system as given.

While these arguments illustrate important theoretical inconsistencies in the STS
perspective, these same arguments are less relevant during the current transformational
period. No longer are manufacturing organizations maintaining “Taylorist” mass
production practices. For many manufacturers the ability to transform current technical
practices into lean production practices is of critical importance and the core assumptions
inherent in lean production are signiﬁcantly different from a mass production
environment. For example, the understanding and practice of machine utilization are
fundamentally different in a mass versus lean context. As argued by Kelly, machine
utilization in a mass production context is based on output maximization. In a lean
production context, the key objective is not machine utilization based on output
maximization, but throughput matched to customer demand. Machine utilization is
counterproductive in a lean production setting. Maximizing machine utilization leads to

high inventory levels, quality problems, increased costs, cluttered work areas, and the

28

degradation of visual management if not matched to customer demand and designed for
balanced throughput. Therefore, in the case of lean production the technical elements of
the production systems are being altered unlike most of the prior studies conducted in a
mass production context.

While the appropriate ﬁt of the social system with the technical systems has been
a key aspect of the STS theory from early in its formation, the issue remains unresolved.
McCuddy (1978) identiﬁed key empirical research in conﬂict regarding the consonance
hypothesis. The “consonance hypothesis” is the proposition that organizations will
perform effectively only to the extent that their structures are compatible with the
requirements and dictates of the technical system (Mohr, 1971). Several studies found
support for this consonance hypothesis (Rice, 1953; Trist, 1951; Walton, 1972).
However, Mohr (Mohr, 1971) directly challenged the consonance hypothesis. He argued
that there is little evidence in the literature that the social structure of organizations is
strongly affected by technology. In this study, Mohr found that routines and task
interdependence were positively associated with technical systems and found no
correlation with participativeness of supervisory style as the social structure dimension.
Additionally, the author found no support for the proposition that the effectiveness of an
organization is determined by the joint optimization of technology and social structure.
In short, Mohr did not ﬁnd support for the consonance hypothesis and as such challenged

the key proposition of joint optimization.

High Performance Work Practices
The STS perspective has been criticized for failing to adequately deﬁne the social

and technical systems (McCuddy, 1978). One of the earliest attempts to close this gap is

29

the link between the STS and high performance work practices literatures (I-IPWP).
Walton (Walton, 1985) identiﬁed .. two radically different strategies for managing a
company’s or a factory’s work force, two incompatible views of what managers can
reasonably expect of workers and the kind of partnership they can share with them”
(Walton, 1985:85). The author describes these opposing approaches as control and
commitment. The workforce strategies considered by the author in comparing control
and commitment approaches included; (1) job design principles, (2) performance
expectations, (3) management organization, structure, systems, and style, (4)
compensation policies, (4) employment assurances, (5) employee voice policies, and (6)
labor-management relations. Other researchers have since developed similar conceptual
models that are consistent with the early work of Walton (MacDufﬁe, 1995b; Pfeffer,
1995; Schuler, 1989; Susman, 1986).

More recently, Adler and Docherty (1998) in response to this criticism articulated
an important shift has that occurred since the 1950’s and 1960’s when STS theory
developed. The authors argue that during this early period the STS perspective failed to
adequately address the purpose of the work system to create customer value within
existing social and resource constraints, failed to adequately address the context or
external business environment, and failed to adequately include the dynamics of the
sociotechnical system. A critical and primary goal for organizations in the current
environment is to create value for its customers within certain resources and social
constraints. The authors acknowledge as a major development the growing awareness by
management and unions in many countries that strategy and business must be understood

and accepted as a key basis for action at all levels in the organization (Adler, 1998). In

30

addition, Adler and Docherty stated: “The key elements in efﬁciency and effectiveness
for an organization differ depending on the environment in which it is working. If
management regards the environment as stable or static, attention will be highly focused
on rationalization, productivity, and proﬁtability. Within the automobile industry, this
strategy is often referred to as “Fordism” (i.e., mass production). If management regards
the environment as characterized by change and turbulence, it will give high priority to
competence development and the abilities to adjust, develop, and innovate. Within the
automobile industry this strategy is often referred to as “T oyotism” (i.e., lean production)
(Adler, 1998:321).

Susman and Chase (1986) provided a STS analysis of the integrated factory and
offered a framework similar to that offered by Walton (Walton, 1985). In this framework
the authors argue that an organization converting to an integrated factory has two
different strategies available: (1) a down grading strategy; or, (2) an upgrading strategy.
Each of these strategies carries with it inherent beneﬁts and risks. The following Figure
2.2 is adopted from the Susman and Chase comparison of the beneﬁts and risks of a
downgrading versus an upgrading strategy (Susman, 1986:266).

Schuler (1989) offered a matching strategy of employee role behaviors with cost
and market strategy. This approach offered by Schuler identiﬁed; innovation, quality and
cost as three distinct competitive strategies and described key human resource
management practices that would appropriately match each of these competitive
strategies. The differing HR strategy types of innovation and quality would appear to be

a further delineation of the high commitment strategy offered by Walton.

31

Figure: 2.2

Potential Beneﬁts and Risks of a
Downgrading Strategy versus an Upgrading Strategy

 

 

 

 

Potential Beneﬁts Potential Risks
Downgrading Strategy Lower skills Workers will not recognize
Less pay key variances
Programmable tasks High costs of overhead
Turnover less of a concern Learning loop severed
Bargaining unit will shrink
Upgrading Strategy Workers will recognize key Average payroll be higher
variances Dependent on scarce human
Overhead will be lower resources
Learning loop facilitated Workers’ tasks are not
programmable

 

 

 

In a study by Youndt, Snell, Scott, Dean, James, and Lepak (1996), the authors

developed a similar approach. The authors explore the relationships among HRM

practices, manufacturing strategy, and performance. The authors’ framework for analysis

included administrative and human-capital-enhancing approaches. The authors

hypothesized that human-capital-enhancing HR systems would be positively associated

with operational performance. The authors identiﬁed three manufacturing strategies

often used by researchers; cost, quality, and ﬂexibility. For the purpose of their study,

the authors grouped the quality and ﬂexibility strategy together with a human-capital-

enhancing HR system. A cost strategy was grouped with an administrative HR approach.

The ﬁndings supported a direct link between HR practices and operational performance.

However, this effect was primarily the effect of linking human-capital-enhancing HR

32

 

systems with quality manufacturing strategy. The ﬁndings show that HR systems can
substantially inﬂuence performance when aligned with appropriate manufacturing
strategies. For the present study, the administrative HR approaches is similar to the low
commitment approach and the human-capital-enhancing HR system is similar to the high
commitment on the vertical axis in Figure 2.1.

In a pair of studies by Arthur (1994; 1992), he identiﬁed two types of human
resource systems, control and commitment. The author assessed how a pattern of HR
practices are related to organizational strategy and performance. That is, how do
different patterns of HR practices interact with ﬁrm strategy and impact organizational
performance? In the 1992 study, Arthur found that IR systems2 vary depending on
business strategy (cost versus differentiation strategy). Figure 2.3 presents Arthur’s
conﬁguration of IR systems.

The ﬁnding in Arthur’s (1992) study were consistent with the conceptual model in
which management selects a business strategy and in-tum shapes an appropriate
industrial relations system. In a follow-up study, Arthur (1994) used the two
conﬁgurations (control versus commitment IR systems) from the earlier study to evaluate
whether the combination of the HR systems are useful in predicting performance in steel
“minimills.” The essence of the research design is presented in Figure 2.4.

The results support Arthur’s contention. Commitment type HR systems were
related to lower scrap rates and higher labor efﬁciency than control oriented HR systems.
The results were mixed for employee turnover. For the study at hand, the studies by
Arthur suggest that high commitment strategies can impact performance when designed

to be in harmony with the manufacturing strategy.

33

Figure 23

Two Systems of Workplace Industrial Relations

 

IR System Types of System
Cost Reduction Commitment Maximizing_
Organization of Work 0 Job task narrowly deﬁned 0 Broadly deﬁned jobs
Employee Relations 0 Very little employee 0 High level of employee
inﬂuence over management participation/involvement
decisions 0 Formal dispute resolution
0 No formal employee procedure (nonunion ﬁrms)
complaint/grievance e Regularly share bus!
mechanisms economic information with
0 Little employees
communication/socialization
effort
Stafﬁng/Supervision 0 Low skill requirement 0 High % of skilled workers
- Intense supervision/control - Self-managing teams
Training Limited training efforts 0 More extensive, general
skills training
Compensation Limited beneﬁts 0 More extensive beneﬁts
Relatively low wages 0 Relatively high wages
Incentive-based 0 All salaried/stock ownership

 

 

Figure 2.4

Control and Commitment HR Systems in Predicting Manufacturing Performance

 

l.
2.

 

HR Practices

Commitment HR System
Control HR System

 

 

 

 

 

1.

Manufacturing Performance

Employee Turnover

2. Scrap
3. Labor efﬁciency

 

MacDufﬁe (MacDufﬁe, 1995b) also used a conﬁgurational approach by identify

consistent “bundles” or systems of HR practices. MacDufﬁe was interested in whether

innovative HR practices affect performance, not as individual HR practices, but as

interrelated elements in an internally consistent HR “bundle” or system. Secondly, the

author examined whether these HR systems contribute to assembly plant productivity and

quality when they are integrated with manufacturing policies under the logic of a ﬂexible

 

2 Arthur used the terms HR systems and IR systems interchangeably.

34

 

production system (i.e., mass versus ﬂexible production strategy). The study ﬁnds
support for the proposition that “bundles” of internally consistent HRM practices are
positively associated with higher employee productivity.

As indicated in the discussion above, several studies have examined the
relationship between high performance work practices and ﬁrm performance (Arthur,
1994; Huselid, 1995). Other studies have been performed in a manufacturing setting and
designed to study the impact of manufacturing strategy on HRM practices (Snell, 1992)
or the relationship between business strategy and industrial relations systems in a
manufacturing context (Arthur, 1992). However, few studies have been conducted that
speciﬁcally examine the linkage between HR practices and polices in a lean production
context.

Nevertheless, within a small group of researchers there has been considerable
debate regarding what cultural components, human resource management and labor
relations practices and processes are consistent with, promote and sustain lean
manufacturing (Adler, 1993; MacDufﬁe, 1992; MacDufﬁe, 1995a; MacDufﬁe, 1995b).
There is some evidence that certain key HR practices are compatible with lean
manufacturing (MacDufﬁe, 1992; MacDufﬁe, 1995b).

Yet, the rationale of ﬂexible or lean production systems implicitly require
different approaches to managing human resources (MacDufﬁe, 1995b). MacDufﬁe
suggests that innovative HR practices affect performance as a set of interrelated bundles
or systems and that these bundles contribute most to performance outcomes (productivity
and quality) when integrated with ﬂexible manufacturing strategies. MacDufﬁe argues

that, at least in the assembly plants he studied, innovative HR practices make little sense

35

in a mass production context, yet innovative HR practices in a lean production context
has a positive impact on operational performance.

Youndt, Snell, Dean & Lepak (1996) provided some additional evidence that
ﬂexible manufacturing does in fact require different HR systems. The authors found that
manufacturing strategy moderated the relationship between HR systems and operational
performance. That is, different bundles of HR practices are better aligned with ﬂexible
manufacturing, and these bundles, combined with ﬂexible manufacturing, have a positive
impact on operational performance. However, the authors argue that manufacturers
pursuing cost containment, as opposed to ﬂexibility, may be better off not investing in
human-capital-enhancements. These ﬁndings suggest that there may not be one universal
or best-practice approach to HR systems that is optimal for lean production.

While the integration of HR systems with lean manufacturing appears to be
critically important to many organizations, the research evidence is very limited. Some
qualitative research has provided some useful frameworks and added insight. For
example, Kochan and Lansbury (1997) provided a topical framework that summarizes an
international project that evaluates the diffusion of lean production and employment
patterns. The employment relations practices studied by the authors included; (1) work
organizations; (2) skill formation and development; (3) remuneration and compensation;
(4) job security and stafﬁng; and (4) enterprise governance and labor management
relations.

Cutcher-Gershenfeld and associates (1998) offered a similar framework in the
analysis of the transfer and diffusion of Japanese work practices to the U.S. The authors

argue that U.S. mass production practices contrast sharply from lean production practices

36

in Japan. The speciﬁc HR systems these authors present in their analysis included; (1)
recruitment and selection; (2) training; (3) compensation and reward systems; (4)
communication systems; (5) team-based work systems; (6) Kaizen; (7) employment
security; and (8) labor relations.

These examples of qualitatively based frameworks need to be empirically tested.
While such research might argue that these practices interact with manufacturing
processes to enhance ﬁrm performance, these ﬁndings are only suggestive. Upon
empirical investigation, these speciﬁc practices and policies may not directly or indirectly
have a positive impact on performance. For example, while some argue that employment
security is critical to the successful adoption of ﬂexible production (Bamber, 1992;
Cutcher-Gershenfeld, 1998), some empirical research has not found support for this
proposition (Osterman, 1994).

Studies within the HPWP have been conducted in many industries. Some of the
earliest work was in coal mining and shipping industries (T rist, 1990) and more recent
studies include the steel industry (Berg, 1999; Ichniowski, 1997), steel minimills (Arthur,
1994), the apparel industry (Appelbaum, 2000) as well as many others. In a review
article by Becker and Gerhart (1996) the authors provided a review of the current
empirical literature regarding HPWP and enhanced performance outcomes. Of the ﬁve
empirical studies cited by Becker and Gerhart only the study by MacDufﬁe (1995b) was
directly related to lean versus mass manufacturing strategy and performance. Yet, the
high performance work practices when applied to a lean production context suggests

bundles of innovative HR practices will positively. impact ﬁrm or plant performance.

37

Making this connection between the HPWP literature and the diffusion of lean production

is an area in need of future research.

Integration

In this section, I will examine the theoretical and seminal studies that support why
the integration of the technical and people elements will be positively and signiﬁcantly
related to department performance and work-related attitudes. Only two empirical
studies have been located that speak speciﬁcally to integration in a lean production
context (Dean, 1991; MacDufﬁe, 1992). Each of these studies will be reviewed and
related to the study at hand.

Dean and Snell (1991) identiﬁed the primary purpose for their study was to
construct a conceptual framework that characterizes the new manufacturing paradigm and
to develop theory about the impact on jobs. While the authors used the term “integrated
manufacturing,” the publication followed shortly after the printing of The Machine that
Changed the World, which coined the term lean production (W omack, 1990). Dean and
Snell identify the following as distinguishing features of new manufacturing practices:

( 1) Advanced manufacturing technology (e.g., computer based technologies such as
computer aided design, manufacturing and engineering); (2) J ust-in-time inventory
control (i.e., a system to reduce lead time, reduce inventory, and hence reduce costs); and,
(3) Total quality management (i.e., the philosophy; do things right the ﬁrst time, strive for
continuous improvement, and understand and meet customer demands, as well as speciﬁc
practices such as SPC, quality function deployment, and Taguchi methods) (Dean,
1991:777-778). While this is a limited deﬁnition of lean production, it clearly is related

to the emergence of lean production as the dominant production paradigm. The authors

38

argue that each of the above are a different aspect of integrated manufacturing, which is a
paradigm of manufacturing management whose core concept is the elimination of
barriers between different facets of a manufacturing operation. Manufacturing
organizations attempt to eliminate these barriers by integrating the stages of production,
by integrating functional departments, and by integrating manufacturing goals across the
organization.

The theoretical concept provided by Dean and Snell are related to the current
study in that each of these integration mechanisms converges at the shop ﬂoor worker. A
critical missing element in the framework offered by Dean and Snell is the integration
that must occur at the level where value is added to the product. Consequently, this study
provides a fourth critical element in achieving the full potential of lean production which
is the integration of the technical systems and people systems at the level of the shop
ﬂoor worker.

The Dean and Snell (1991) survey study was conducted in the metal-working
industry (Standard Industrial Classiﬁcations 33, 34, 35, and 37). Plants not
manufacturing ﬁrms were the unit of analysis. The surveys were mailed to plant
managers, functional managers, human resource managers, and non-managerial
employees. The valid data included 160 plant managers, 90 human resource managers,
102 operations managers, 109 quality managers, 97 production control managers, and
456 non-managerial employees distributed across the functional manager categories.

MacDufﬁe and Krafcik (1992) identiﬁed two propositions in their study. First,
that the link between the minimization of buffers and the extensive development of

human resources capabilities under lean production contributes signiﬁcantly to

39

productivity and quality. Second, that advanced technology will contribute more
effectively to manufacturing performance under lean production than under mass
production (quality and productivity). The authors base these propositions on the
premise that the “organizational logic” of lean production is signiﬁcantly different from a
mass production context. “Mass production uses highly specialized resources (both
equipment and people) applied to the high-volume production of standardized products to
achieve economies of scale. To ensure that these economies can be achieved, the
production process must be protected as much as possible from disruptions (such as sales
ﬂuctuation, supply interruptions, equipment breakdowns) by large buffers - of inventory,
repair space, extra equipment, and utility workers. These buffers moderate the tight
coupling among steps in the production process, which minimizes the impact of
contingencies” (MacDufﬁe, 1992:210).

In contrast, in a lean production context the “organizational logic” is signiﬁcantly
different. “. . .in a lean production system the stimulus to achieving cost and quality
improvement is the reduction of buffers, which has both a direct effect (e.g., reducing the
carrying cost of inventories), and a more signiﬁcant indirect effect providing valuable
information about production problems and an ongoing incentive to utilize that
information in incremental problem-solving activity. While the reduction of buffers can
promote this problem-solving approach, it will be effective only when human resource
policies are in place that generate the necessary skills in the work force and create a sense
of reciprocal commitment between company and worker” (MacDufﬁe, 1992121 1-212).

The logic offered by MacDufﬁe and Krafcik is related to studies that attempt to

identify the appropriate or best HR policies as well as speciﬁc practices that will assist in

40

achieving the most of lean production. Or from an STS perspective, the authors examine
the consonance between the technical and social systems in a lean production context.
However, this study differs in important ways from this research stream. In this study, it
is proposed that very speciﬁc integration activities must occur. These early studies
attempted to identify the appropriate array of HR policies and practices that best ﬁt lean
production. While MacDuffre and Krafcik deﬁne where integration occurs when
discussing “incremental problem solving,” yet provide empirical data and offer speciﬁc
HR policies at a different level. This study, in contrast, investigates integration practices
at the shop ﬂoor level where in part MacDufﬁe and Krafcik provide the logic for their
study.

MacDufﬁe and Krafcik were part of the research team that initiated the
International Assembly Plant Study in 1989. The survey data used for this study was part
of this larger international study. The sample consisted of 62 assembly plants from 6
different global regions from high volume product assemblers (versus luxury/specialty
product category). The regions identiﬁed in the study included: (1) Japan, (2) J apanese-
parent plants in North America, (3) U.S.-parent plants located North America, (4)
Europe, (5) New Entrants, including East Asia, Mexico and Brazil, and (6) Australia.

The MacDufﬁe and Krafcik study found support for two relevant research
questions for the study at hand. The research ﬁndings supported the proposition that the
link between the minimization of buffers and the extensive development of HR
capabilities under lean production contributes signiﬁcantly to productivity (hours per

vehicle) and quality (defects per 100 vehicles). Also, the study ﬁndings supported the

41

premise that advanced technology will contribute more effectively to manufacturing
performance under lean production than under mass production.

Other important results were also reported in this study. The Use of Buffers and
HRM Policies were highly correlated (r = .65), which supports the “organizational logic”
proposed by the authors. The Production Organization Index (which consists of a series
of measures for the Use of Buffers and HRM Policies) was strongly correlated with
performance (r = -.59) and quality (r = -.63). In sum, 36% of the variation in both quality
and productivity for this sample is explained by the Production Organization Index alone.
The authors also found that the Use of Buffers and HRM Policies contribute almost
equally to the strong relationship between Production Organization Index and
productivity. Yet, with quality as the outcome measure, the HRM measure is the most
inﬂuential component. This ﬁnding suggests that is may be possible to minimize buffers
as a cost reduction strategy, resulting in improved productivity without altering the plant
processes that lead to high quality. This would support the basic premise of this study
that two alternative approaches have emerged: (1) a low commitment lean production
strategy, and, (2) high commitment lean production strategy. The authors argue that
these ﬁndings support their proposition that the reduction in buffers must match HRM
policies that improve problem solving capacity.

The technology measures also had statistically signiﬁcant relationships with
productivity and quality (Total Automation Index with productivity = r -.67 and with
quality = -.41; Robotic Index with productivity = -.55 and with quality = -.41). And, the
correlation of Total Automation with productivity and quality is much stronger for lean

production than mass production.

42

In exploring the integration hypothesis for overall manufacturing performance, the
authors’ found that the amount of technology does not differentiate among the top three
performing categories of assembly plants. However, the Production Organization index,
including the component measures do differ signiﬁcantly across the top three performing
groups. And, the best performing category had the most lean production system, the
most minimal buffers, and the most high-commitrnent HRM policies. This suggests that
technology and production organization are important factors in explaining
manufacturing performance when examined independently and contribute most
signiﬁcantly to high productivity and high quality when they occur simultaneously. As
such, the authors suggest that technology has an important role in boosting performance
as plants move from very low levels of automation to moderate levels, even in a mass
production context, when both quality and productivity are jointly considered. However,
the performance gain in moving from moderate to high levels of automation appears to
occur only when linked with organizational, human resources, and manufacturing
practices of a lean production system.

This study builds on the STS approach by analyzing the technical and social
elements of work by evaluating two plants in the midst of a massive conversion from
mass to lean production. The goal is to determine whether investing in the social
elements of the larger work system impacts department performance and work-related
attitudes at the department level. The joint optimization aspect of the STS perspective
suggests that lean production will either ﬁt with only one social system or that a number
of social systems provide viable options in maximizing the effectiveness of the work

system. Using the framework in Figure 2.1 and consistent with the STS and high

43

performance work practice literatures two alternative pe0ple systems are proposed; (1)
low commitment people systems; or (2) high commitment people systems. This study
will use this theoretical basis coupled with the conceptual framework to assess whether
these alternative approaches to people systems impact department effectiveness and
work-related attitudes. Also, this study will assess the relationship of integration
practices as a partial mediator between the technical and people systems with the
dependant variables. In addition, this research will provide additional insight into the

consonance hypothesis.

Hypotheses

The review of the academic literature and the conceptual framework offered in
Figure 2.] indicates that links between people systems, technology systems, and the
outcomes measures are probable. Yet, empirical tests of these relationships need to be
performed. The ﬁrst step is to draw a direct link between the key technical systems and
people systems of lean production and the dependent variables (department performance,
perceived performance, and work-related attitudes). While it is expected that the
implementation of the technical elements of lean production will be positively and
signiﬁcantly related to both perceived performance as well and actual department
performance, this relationship needs to be conﬁrmed. Many studies have demonstrated a
strong correlation between actual measurable performance and individual perceptions of
performance and will be used in this study to strengthen the validity of the relationships
between the independent and dependent variables. Rooted in these earlier studies and the

practitioner literature it is expected that the implementation of the technical elements of

lean production independent of any adjustment to the people systems will result in
improved performance. Based on this discussion, the following hypotheses are proposed:

H1: Technical Systems of lean production are positively and signiﬁcantly
related to department performance.

H2: Technical Systems of lean production are positively and signiﬁcantly
related to perceived department performance.

The next step is to assess the relationship between people systems and the
dependent variables. The STS perspective posits that to achieve maximum organizational
performance and positive work-related attitudes both the technical and social systems
must be optimized. The ﬁndings by MacDufﬁe and Krafcik (1992) suggest that people
systems will have a direct impact on performance in a lean production context. The
matching of the technical and people systems to optimize organizational effectiveness is
typically deﬁned by output and worker attitudes. While the social system aspect of the
joint optimization framework remains unresolved in terms of its relationship to
performance outcomes, the high performance work practice literature has more
consistently found a relationship between people systems and performance. Other
researchers have found that high commitment people systems relationship with ﬁrm
performance are moderated by manufacturing strategy, which suggests that appropriately
designed people systems will impact organizational performance when matched with lean
production. Based on this discussion, the following hypotheses are offered:

H3: People Systems of lean production are positively and signiﬁcantly
related to department performance.

H4: People Systems of lean production are positively and signiﬁcantly
related to perceived department performance.

H5: People Systems of lean production are positively and signiﬁcantly
related to work-related attitudes.

45

As stated above, a fundamental premise of the STS literature is the importance of
ﬁt between the social and technical aspects of work. Moreover, that effective work
systems must jointly optimize the relationship between these systems. However, this
premise has remained unresolved in mass production context, and has not been directly
addressed in a lean production context. As stated above, the “consonance hypothesis” is
the proposition that organizations will perform effectively only to the extent that their
social structures are compatible with the requirements and dictates of the technical
system. While limited, the research in a lean production context suggests that people
systems must ﬁt the technical elements of lean production to achieve optimal
performance. Based on this discussion the following hypotheses are proposed:

H6: The consonance between the technical systems and people systems in
lean production is positively and signiﬁcantly related to department performance.

H7: The consonance between the technical systems and people systems in
lean production is positively and signiﬁcantly related to perceived performance.

H8: The consonance between the technical systems and people systems in
lean production is positively and signiﬁcantly related to work-related attitudes.

The integration literature suggests that a fundamental concept in lean production
is the elimination of barriers between different facets of a manufacturing process. A key
mechanism to eliminate these barriers is integration activities at the source where value is
added. As discussed above, this study will attempt to identify integration activities as a
partial mediator for both the technical and people systems of lean production. Hence,
empirical tests of these relationships need to be performed. Based on this discussion the
following hypothesis are offered:

H9: The relationship of the technical systems and people systems with
department performance will be partially mediated by integration practices.

46

H10: The relationship of the technical systems and people systems with
perceived performance will be partially mediated by the integration practices.

H1]: The relationship of the technical systems and people systems with
work-related attitudes will be partially mediated by the integration practices.

47

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50

Womack, J. P., Daniel T. Jones, and Daniel Roos. (1990). The Machine that Changed the
World. New York: Macmillan Publishing Company.

Youndt, M. A., Snell, Scott A. Dean, James W., and Lepak, David P. (1996). Human

resource management, manufacturing strategy and ﬁrm performance. Academy of
Management Journal, 39(4), 836-867.

51

CHAPTER THREE

RESEARCH METHODOLOGY

The purpose of this chapter is to describe the methodology used in this research.
Based on the previous discussion, a model was developed that examined the relationships
between people systems, technical systems, integration systems, department performance
and, worker-related attitudes. This chapter introduces the organizations under study,
provides a review of measurement issues, reviews the data collection procedures, and

reviews the data analysis procedures.

Gaining Access

A common difﬁculty in ﬁeld research is gaining access. This study then, like
many before it, and many to follow, found it difﬁcult and time consuming to gain access
to conduct research in a ﬁeld setting. What follows is a brief discussion of the protracted
negotiations involved in securing access to the research sites.

This study began with contacts with a key human resource (HR) leader within a
large global manufacturer. The manufacturer was looking for expertise in lean
production related to organizational change in new plant start-ups and existing
brownﬁeld facilities. This researcher had been involved with both practitioners and
academics for some time in the area of lean production and was looking for entry into
manufacturers engaged in implementing lean production to conduct this dissertation
research.

This researcher ﬁrst met this key human resource leader in April of 1998. While
the HR leader was looking for assistance, what emerged was an exchange that included

right of entry to manufacturing sites for conducting research. After numerous meetings

52

between April 1998 and December 1998 with the key HR leaders as well as meetings
with staff personnel and three European experts in lean production, the HR leader agreed
to begin work on securing site access to conduct this research. At this time, the HR
leader agreed to meet with a top manufacturing leader within the corporation with
expertise in lean production to identify four appropriate research sites. This then set in
motion a series of meetings at each of these four sites to further pursue access.

Between January and May of 1999, this researcher met with representatives from
each of these facilities on numerous occasions. Two of these facilities were located in
Europe. The German location, after three conference calls, the review and discussion of
two research proposals, and a meeting with the plant manager, HR managers, and
operations manager, decided not to support the research at their site. This ﬁnal decision
was made in May 1999.

The second European site located in Belgium decided that they would participate
in the study after several meetings (two with the plant manager and two separate
meetings with the HR , lean production, and employee development managers),
conference calls, and revisions to a research proposal. The initial discussion with this
plant began in November of 1998 and agreement was not reached until September 1999.
Given time constraints and needs of this researcher, this site is not included in this study.
However, research continues in this site and will come to fruition in the summer of 2000.

The two sites located in the U.S., and the data used for this study, agreed to
participate after several meetings with numerous individuals within each location.
Conference calls and meetings began at both locations in January 1999 and agreement

was not reached until May of the same year. Both sites required meetings with the HR

53

manager, plant manager, and operations managers. Meetings were then scheduled with
union leaders and staff personnel. In the case of the plant located in the Midwest, two
meetings were held with joint plant steering committees before access was granted. Once
both plants agreed to the study, a ﬁnal review of the proposal was requested by public
relations at the central ofﬁce.

Finally, in May 1999, the study got under way. Yet, both facilities had one last
request, that all data to be collected from unionized employees had to be collected before
July 1, 1999. The labor contract at these facilities was due to expire and as a result the
union and management representatives did not want the research to become a point of
conﬂict. The alternative was to postpone the study until after agreements were reached at
both facilities, but no guarantee to access at that time would be granted. As it turns out
the decision to move forward with the July 1, 1999 deadline was the correct decision.
One of these plants did not reach a ﬁnal labor agreement until January 2000. Deciding to
wait would have added at a minimum an additional eight or nine months to this research

project.

The Research Sites

In this study there are two manufacturing sites from the same large global
corporation that participated in this study. One of the facilities is located in the Southeast
and the other is located in the Midwest. These plants supply the automotive industry with
car and truck parts and pre-assembled sub-components. The hourly workforce at each
location are represented by the same national union, but belong to different local unions.

Both research sites are multi-plant locations.

54

Both plants have a history of workplace innovation. Like many other plants,
these facilities adopted workplace innovation in a piecemeal approach. The common
history of these adoptions is the failure to maintain these innovations. For example, each
plant has adopted such innovations as statistical process control (SPC), quality circles,
team-based work systems, just-in-time, and standardized work. The common cause cited
in interviews of key personnel as to why these innovations were not maintained is the
lack of a clear vision or systematic understanding of lean production as well as an
understanding of how the pieces of lean production ﬁt together into a cohesive whole.
For these two facilities the piecemeal adoption of these practices converged with the
development by the central ofﬁce of a vision for manufacturing strategy in the middle of
the 1990’s. This strategy and implementation plan provided a common manufacturing
vision and implementation strategy to encourage and accelerate the implementation of
lean production. Despite this common vision and implementation strategy each facility
still faced and continues to confront the challenge of shaping the adoption of lean
production to their unique circumstances.

Plant Located in the Southeast

The plant located in the Southeast began operations in 1980. At the time of the
study, the plant had approximately 1300 employees and the facility occupied
approximately 646,000 square feet. The plant produces approximately 150 end items, and
its primary products include halfshafts, intermediate shafts, power steering hoses, and tie
rods. The plant consists of 563 machines with 8 % located in assembly, 82 % used as
process equipment, and 10 % welding and forming machines. The plant’s primary

customers include General Motors, Saturn, Saab, Toyota, Volkswagen, and Volvo.

55

Plant Located in the Midwest

The plant located in the Midwest began operations in 1966. At the time of the
study the plant had a workforce of approximately 1900 employees and the facility
occupied approximately 645,000 square feet. The plant produces approximately 222 end
items and its primary product supplied to automotive assembly plants is steering
columns. The plant consists of 1044 machines with 16% located in assembly, 26% used
as process equipment, 30% in welding and forming machines, and 24% in plastic

injection machines. Its primary customers include Chrysler, General Motors, and Toyota.

The Sample of Subjects

The sample of subjects included 471 hourly employees and 62 salaried employees
from the participating organizations for a total of 533 respondents. Employees from all
functional areas were included in the pool of subjects for survey administration. Given
the focus of this study the subsequent functional areas were speciﬁcally targeted to
complete the survey and the number of respondents by functional area is as follows: ( 1)
Assembly operations (248 respondents); (2) Component operations (162 respondents);
(3) Quality assurance (37 respondents); (4) Support areas (e.g., skilled trades, cleaners,
tool crib attendants) (38 respondents); (5) Production control & logistics/materials
management (7 respondents); (6) Engineering (23 respondents); (7) Appointed and
elected union ofﬁcials (6 respondents) and, (8) All others (14 respondents). Table 3.1

provides an overview of respondents by functional areas.

56

Table 3.1

Number of Respondents by Functional Area, Plant Location and Total

 

 

 

Midwest Plant Southeast Plant Total

Function Total Percent Total Percent Total Percent
Assembly 194 62.8 54 23.9 248 46.5
Components 47 15.2 1 15 50.9 162 30.4
Quality Assurance 18 5.8 19 8.4 37 6.9
Support Functions 19 6.1 19 8.4 38 7.1
Production Control 4 1.3 3 1.3 7 1.3
Engineering 15 4.9 8 3.5 23 4.3
Appointed/Elected 5 1 .6 1 0.4 6 1. 1
Union Ofﬁcials
All Others 7 2.3 5 2.2 12 2.3
Demographic Data

Demographic data collected in this study included several items of potential
interest to the research sites and this study. Each survey identiﬁes what shift the
employee works, years of service in the speciﬁc plant, age of the employee and
functional area is identiﬁed. Gender and race are recorded. Hourly or salaried employee
status is identiﬁed as well as whether the person completing the survey supervises other

employees. Table 3.2 provides a demographic proﬁle for this sample.

57

Table 3.2

Demographic Proﬁle by Plant Location and Total

 

 

Midwest Plant Southeast Plant Total
Item Total Percent Total Percent Total Percent
Shift
First 183 59.4 142 56.6 325 58.2
Second 107 34.7 63 25. l 170 30.5
Third 18 5.8 45 17.9 63 11.3
Gender
Female 90 29.6 62 26.7 152 28.4
Male 214 70.4 170 73.3 384 71.6
Years of service
1-2 years 41 13.4 17 7.4 58 10.8
3-5 years 36 11.7 19 8.2 55 10.2
6-10 years 14 4.6 13 5.6 27 5.0
11-20 years 50 16.3 1 13 48.9 163 30.3
21-30 years 128 41.7 60 26.0 188 34.9
More than 30 yrs. 38 12.4 9 3.9 47 8.7
Age
18-25 years 17 5.6 3 1.3 20 3.7
26-30 years 16 5.2 6 2.6 22 4.1
31-35 years 8 2.6 12 5.2 20 3.7
36-40 years 19 6.2 37 15.9 56 10.4
41-45 years 77 25.2 70 30.2 147 27.3
46-50 years 96 31.4 44 19.0 140 26.0
51-55 years 52 17.0 44 19.0 96 17.8
Over 55 years 21 .- 6.9 16 6.9 37 6.9
Race
African American 22 7.4 27 11.9 49 9.3
Caucasian 244 81.6 160 70.5 404 76.8
Hispanic 1 l 3.7 0 0.0 1 1 2.1
Native American 15 5.0 28 12.3 43 8.2
Other 7 2.3 12 5.3 19 3.6
Employment Status
Hourly 271 88.9 210 87.9 481 88.6
Salaried 34 11.1 28 l 1.7 62 11.4

58

Supervise Others
Yes 26 8.0 26 10.9 52 9.5
No 281 86.7 213 89.1 494 90.5

In addition, the subjects were requested to indicate whether they had received lean
production training. This question was followed by the identiﬁcation of seven categories
of potential lean training received as well as an open-ended item to identify other training
received related to lean production. Table 3.3 provides an overview of the results.

Table 3.3

Total and Percent Participation in Lean Training by Facility and Total

 

Midwest Plant Southeast Plant Total

Training Area Total Percent Total Percent Total Percent

 

Lean Training 271 88.9 134 56.3 405 72.6
5 S 214 70.4 111 46.8 325 58.6
7 Forms of Waste 129 42.4 60 25.3 189 33.8
Introduction to 211 69.4 69 29.1 280 49.3
Lean Production

People Focused 196 64.5 47 19.8 243 42.2
Factory

Factory Simulation 143 47.0 73 31.1 216 39.1
Team Building 168 55.3 55 23.3 223 39.3
Problem Solving 158 52.0 69 29.2 227 40.6
Other Related 35 l 1.6 12 5.1 47 8.9
Training

 

59

Measurement of Variables

In the following section, a description of the measurement of the central variables
is presented. The literature examining the relationship between people and technical
systems with performance and work-related attitudes has included a variety of measures.
As discussed earlier, little of this literature was conducted in a lean production context.
Therefore, this research uses measures and constructs when deemed applicable from the
existing literature and has speciﬁed and constructed suitable measures and constructs at
other times when determined appropriate.

The data sources for this dissertation were based on a combination of sources.
The data collection instruments include an attitude survey, an assessment of the technical
elements of lean production, an assessment of perceived department effectiveness,
archival department performance, and interview data. Table 3.4 provides an overview of
the variables, assessment instruments and items on the surveys linking the independent,
mediating and dependent variables. The hypotheses testing is based on 61 departments
across the two sites that participated in this study (N =61).

Table 3.4

Variables, Assessment Instruments, and Items on the Surveys

 

 

Variable Assessment Instrument Items on Survey
Independent Appendix A: Perceptions Regarding the
Variable: Implementation of Lean Production
People Systems
Supervisory behaviors 1-14
Management support 15-22
Cooperative union management relations 23-29
Developmental focus 50-56
Managing change 65-69

Independent
Variable:

Technical Systems

Independent
Variable:

Technical Systems

Teamwork

Involvement/psychological participation
Process focus

Proactive problem solving

Workplace trust

Workplace bonding

Workplace bridging

Conﬂict resolution climate

Lean training

Appendix B: Implementation of lean
production

Flow Manufacturing:
Manufacturing is organized by
value stream
Takt time

Employee Environment:
Cross-functions/multi-
skills/certiﬁcation
Natural work group structure &
support

Workplace Organization:
Clear/clean/organized & maintain
the production area & ofﬁce
Visual controls

Quality:
Inspection & test
Process capability

Appendix B: Implementation of lean
production

Operational Availability
Owner operator
Quick set-up

Material Movement:
Container right sizing &
supporting the operator
Internal material delivery

61

70-76
77-80
81-88
89-95
96-103
104-109
1 10-120
121-129
Page 12

oo

12
13

14

15

Mediating

Appendix B: Implementation of Lean

 

Variable: Production
Integration
Employee Environment and Involvement:
People focused practices 3
Suggestion system 16
Quality:
Detect, solve & prevent quality 10
problems
Operational Availability:
Continuous improvement 11
Dependent Appendix A: Perceptions Regarding the
Variable: Implementation of Lean Production
Work-Related
Attitudes
Commitment to the lean production 30-34
strategy
Job satisfaction 35-39
Perceived learning environment 4049
Team efﬁcacy 130-140
Dependent Appendix A: Perceptions Regarding the
Variable: Implementation of Lean Production
Perceived Perceived department performance 55-64
Performance
Appendix C: Perceptions Regarding the l- 12
Effects of the Implementation of Lean
Production
Independent Variables

The two independent variables include the technical systems of lean production

and the people systems of lean production. The people elements of lean production are

measured by a questionnaire. The independent variables assessed in the questionnaire

include the following constructs: (l) Supervisory practices; (2) Management support; (3)

62

Cooperative union-management relations; (4) Developmental focus; (5) Managing
change; (6) Teamwork; (7) Involvement/psychological involvement; (8) Process focus;
(9) Proactive problem solving; (10) Workplace trust; (11) Workplace bonding; (12)
Workplace bridging; (13) Conﬂict resolution climate; and (14) Lean training. The scales
are all ﬁve-point items except lean training, which is a yes/no response to speciﬁc lean
training items and the results are presented above in Table 3.3. Table 3.5 provides a list
of the independent, mediating and dependent variables and the operational constructs for
each variable. I

The assessment instrument entitled Perceptions Regarding the Implementation of
Lean Production was completed by 261 employees from the facility located in the
Southeast and by 324 employees from the plant located in the Midwest for a total of 585
completed surveys across the two facilities. This survey is located in Appendix A. The
survey was designed to collect data related to the independent variables associated with
the people systems of lean production.

The development of the Perceptions Regarding the Implementation of Lean
Production survey resulted from a number of different sources (Cook, 1981). As stated
above, some of the measures and items were based on existing instruments, while others
were developed speciﬁcally for this study, while yet others were amended to ﬁt the needs
of this research design. Each of the constructs, source or sources is provided in Table 3.6
located in Appendix B. Table 3.7 provides a summary of the assessment instrument, data

source, and number of respondents.

63

Table 3.5: Independent, Mediating, Dependent Variables, and Operational

 

 

Constructs
Independent Variables Mediating Variables Dependent Variables
Technical Elements of Lean Integration Elements of Department

Production Lean Production

El

Manufacturing org. by D Standardized work

value stream (PFP) performed by
shop ﬂoor people and

Manage by takt time focused on continuous
improvement efforts

Operators Cross-

functional & multi- Cl Teams adjust work

skilled, certiﬁcation assignments to match
takt time

Team structure and

support Cl Problem solving in
place and consistently

5 S (clear, clean, etc.) followed

Visual control 121 Problem solving has
become a change

Inspection methodology process

Error prooﬁng
Process capability

Operator monitor, clean,
& performs minor maint

Quick set-up
Container right sizing

Line side delivery, small
lots, will pull signal

Eﬁ'ectiveness/Perfonnance

Cl

0

Cl

0

0

Cost
Productivity
Quality
Delivery

Suggestions

Individual Perceptions

Cl

Commitment to lean
strategy

Job satisfaction

Perceived learning
environment

Perceived department
performance

Team efﬁcacy

 

Table 3.5: Independent, Mediating, Dependent Variables, and Operational
Constructs (continued)

 

Independent Variables Mediating Variables Dependent Variables

 

People Elements of Lean
Production

0 Supervisory behaviors
D Management support

13 COOperative union-
management relations

0 Developmental focus
D Managing change
0 Teamwork

C1 Involvement/psycho-
logical participation

cr Process focus

E1 Proactive problem
solving

Cl Workplace trust
0 Workplace bonding
o Workplace bridging

Cl Conﬂict resolution
climate

0 Lean training

 

65

Table 3.7

Assessment Instrument, Data Source, Number of Respondents

 

 

Assessment Instrument Data Source Respondents
Perceptions Regarding the Implementation of Stratiﬁed Sample 585
Lean Production (Appendix A) of Employees
Implementation of Lean Production (Appendix Superintendents 12
B)
Perceptions Regarding the Effects of the Supervisors 71
Implementation of Lean Production (Appendix Superintendents 12
C)
Interview Protocol (Appendix D) Superintendents 12
Department Performance Data Archival Data 2

 

A second instrument was adapted to assess the technical elements of lean
production entitled The Implementation of Lean Production, which is contained in
Appendix B. This instrument was designed and administered to assess the degree to
which the technical elements of lean production had been implemented in each
department. That is, this measurement instrument quantiﬁes the extent to which each
department has become a lean producer. The instrument was administered to the
superintendent for each department, which resulted in each superintendent completing an
assessment instrument for more than one department. The number of departments
assessed by each superintendent varied between two and eight departments.

The technical systems of lean production have been deﬁned in various forms by a

number of different sources. However, the key elements of lean production are based on

the Toyota Production System. The document that provides the basis for this assessment
instrument was used by the participating organizations to assess the gap between the
current state and their future vision for lean production, which likewise is based on the
Toyota Production System. Other analytic instruments were considered for this purpose.
The existing internal assessment instrument was adopted because of its high quality and
the familiarity of the subjects with the terms on the lean assessment instrument.

The technical elements of lean production measured include the following lean
production categories: ( 1) Flow manufacturing (manufacturing is organized by value
stream and takt time); (2) Employee environment and involvement (cross
functional/multiskilled certiﬁcation, and natural workgroup structure and support); (3)
Workplace organization (clear, clean, organized and maintain work area, and visual
controls); (4) Quality (inspection and testing, process capability,); (5) Operational
availability (owner operator and quick set-up); and, (6) Material movement (container
right sizing, supporting the operator, and internal material delivery). The gap analysis
developed by this organization is a plant assessment tool, and as such, was adjusted for
this study to focus at the department level. The response scales are four-point, with a
score of 1 being the least lean.

Mediating Variables

There are four measures of the integration of the technical and people systems of
lean production. The premise is that to fully maximize the full potential of lean
production, an organization must have fully developed and implemented both the
technical and people aspects of lean, and that these sub-systems of lean production are

fully integrated at the level in which value is added to the product. While the integration

67

of these systems needs to occur at other levels of the organizations, this study focuses on
integration at the shop ﬂoor. The integration variable will be measured by assessing the
existence of the following practices: (1) Standardized work is performed by shop ﬂoor
people and focused on continuous improvement; (2) Teams adjust work assignments to
match takt time; (3) Problem solving is in place and consistently followed; and (4)
Problem solving has become a change methodology process. The integration questions
are located within the lean assessment instrument (questions 3, 10, 16 and 11) and is
located in Appendix B. Accordingly, the integration items were also completed by the
superintendent level at each location.
Dependent Variables

In this study the dependent variables fall into three categories. The ﬁrst category
is actual department performance. The request by this researcher was to identify common
measures for cost, quality, productivity, delivery and suggestion data. These
measurement categories are common measures within a manufacturing setting. Plant
personnel and the researcher worked through numerous measures until the best available
data was obtained. The primary challenge in this aspect of the study was in obtaining
common data at the department level. Manufacturing organizations track and retain vast
amounts of data. In fact, it is not unusual for manufacturing organizations to track and
retain data that is not used for further analysis or for data based decision making.

This performance data is based on data already tracked by the participating
departments. Given the difﬁculty in ﬁnding common measures across departments and
plants making different products, the supervisory perceptions of performance resulting

from the implementation of lean production will be used to bolster this aspect of the

68

study. The department performance data was provided for the end of month and year to
date performance for February, May and August 1999.

In the end, the following data were provided. The actual number and percent of
suggestions were made available for each department. Up-time by department was also
provided. This is an efﬁciency measure that indicates the amount of time that all of the
equipment in any given department is available. Actual downtime for assembly areas
was also provided. This is a measure of similar meaning to up-time for manufacturing
operations. Perforrnance-to-plan was also provided. This performance measure provides
actual numbers of products produced compared with performance objectives. The plant
was unwilling to share cost or quality data due to public disclosure concerns.

A third data collection tool was developed to collect input from supervisors and
superintendents regarding the consequences of the implementation of lean production on
department effectiveness. This third assessment instrument is entitled Perceptions
Regarding the Effects of the Implementation of Lean Production. The goals for this
instrument were twofold. One, it was designed to obtain the perceptions of supervisors
and superintendents related to the implementation of lean production and its impact on
department performance. Two, this assessment instrument was designed to augment
archival performance data from the same departments. This two-part approach (i.e.,
perceptions of department performance and actual performance) was developed to offset
potential problems that often occur in obtaining accurate and useful performance data at
the department level. This instrument consists of 12 survey questions with seven-point

response scales and is located in Appendix C.

69

In addition, department work-related attitudes are measured as dependent
variables. These work-related attitudes include the following: (1) Commitment to the
lean production strategy; (2) Job satisfaction; (3) Perceived learning environment; and,
(4) Team efﬁcacy. These constructs are used to assess differences in work-related
attitudes of employees in a technical approach to lean production and in contrast with an
integrated approach to lean production. This data was collected as part of the larger
attitude survey. That is, Perceptions Regarding the Implementation of Lean Production
located in Appendix A. In total, this survey consists of 10 demographic questions, one
open ended question, and 140 survey items acrosslS constructs. .

Covariate

Department size has been identiﬁed as a covariate for this study. Department size
is often cited as potential confounds in organizational studies at the department level. As
such, data regarding department size has been identiﬁed and its potential impact on the
dependent variables will be examined and controlled for.

Organization and Assessment Structure

In summary, workers at the shop ﬂoor level completed the attitude survey
Perceptions Regarding the Implementation of Lean Production, which contains items
related to people systems, work-related attitudes, and perceived performance (see
Appendix A). The supervisor level completed perceived performance survey entitled
Perceptions Regarding the Effects of the Implementation of Lean Production (see
Appendix C). The superintendent level completed the technical assessment instrument
entitled Implementation of Lean Production (see Appendix B), completed the integration

assessment instrument (also see Appendix B), and the perceived performance assessment

70

instrument entitled Perceptions Regarding the Effects of the Implementation of Lean
Production (see Appendix C). Figure 3.1 provides an overview of the level of personnel
to complete each of the assessment instruments.

Figure 3.1

Organization and Assessment Structure

 

 

 

 

 

 

 

 

 

 

 

Plant Manager
I I l | I
Superintendent
Perceived Performance I
Su rvisor Technical Assessment
pe Integration Assessment

 

 

 

Perceived Performance

 

 

 

 

 

/ Workers

Attitude Survey

Pe0ple Systems
Work-Related Attitudes
Perceived Performance

71

Data Collection Procedures

This section describes the data collection procedure used in this study. The
methodology encompasses four phases: (1) Identifying the sample; (2) Testing the
assessment instruments; (3) Collecting quantitative data; and, (4) Collecting qualitative
data.
Phase 1: Identifying the Sample

A model and description of approaches to the diffusion of lean production was
reviewed with key leaders from the headquarters of the sponsoring organization in this
study. (See Chapter 2, Figure 2.1.) Based on this deﬁnition and framework, these key
leaders were asked to identify plants that best represented the integrated approach to lean
production and the technical approach to lean production. These key leaders from the
participating organization included both human resource and manufacturing leaders.
These leaders identiﬁed one assembly plant (located in Belgium) and one component
plant (located in North America) that best represented the integrated approach. These
leaders also identiﬁed one assembly plant (located in Germany) and one component plant
(located in the U.S.) that best represented the technology focused approach to lean
production. While the larger research design includes international comparisons, this
dissertation is focused only on the component plants located in the U.S.

Key leaders were then interviewed and provided with the same deﬁnitions and
model of alternative approaches to the diffusion of lean production within the U.S.
component plants and requested to identify at least 30 departments to participate in this
study. And, that at least 10 of these departments should represent the plants best and

worst lean producing departments within their respective plants. A collection of 3-5

72

representatives from each location identiﬁed the departments to participate in this study
based on the model and deﬁnitions. Neither plant revealed which of the 10 plus
departments represented the best and worst lean production work areas.

The plant located in the Midwest identiﬁed 31 departments to participate in the
research and the plant located in the Southeast identiﬁed 30 departments for a total of 61
participating departments. The departments were drawn from assembly and component
operations at both plant locations.

Phase II: Testing the Assessment Instruments

The attitude survey entitled Perceptions Regarding the Implementation of Lean
Production (see Appendix A) was tested with a number of different groups. First, the
survey was reviewed with three university faculty members. Their feedback was used to
amend and improve the questionnaire. Next, a focus group of 3-5 internal experts was
held to review the questionnaire at each location and the survey was further reﬁned. The
survey was then tested with two faculty members and one student before testing it with a
group of 3-5 employees from each facility. During this phase, each test subject
completed the survey and identiﬁed any items that were confusing or redundant. Pilot
testing the survey suggested that the questionnaire could be completed within 30 minutes.
Finally, the questionnaire was tested with a cross-functional group. The cross-functional
group included: (1) a division labor relations manager; (2) four union representatives; (3)
two training and organizational change employees; (3) two production supervisors; (4) a
superintendent; (5) a quality control manager; (6) two lean production experts; and, (7)

two plant managers. The survey was then administered.

73

As stated above, the Lean Production assessment instrument (see Appendix B) is
' based on an internal gap assessment. The original lean gap assessment device is a plant-
wide instrument. This instrument was adjusted to assess the degree of lean
implementation at the department level. The Lean Production assessment instrument was
tested with a manager of lean production implementation, an organizational change and
training manager and the researcher. The amended instrument was then administered.

The Perceptions Regarding the Effects of the Implementation of Lean Production
assessment instrument (see Appendix C) was developed with the advice and counsel of
two faculty members. This assessment instrument was reviewed with a manager of
organizational change and training from the facility located in the Midwest and the
manager of lean production and employee development located in the Southeast. The
instrument was pilot tested with three university associates. The assessment instrument
was then administered.
Phase III: Collecting Quantitative Data

The research site located in the Southeast elected to administer the attitude survey
on site. The hourly workforce completed the survey in a large conference room located
near the production ﬂoor. Each survey was coded for each department to ensure accurate
administration and analysis. Some of the surveys were completed at other sessions in
other conference rooms for speciﬁc employees (e. g., engineers, supervisors, and
superintendents). This adjustment to survey administration was adopted for the
convenience of the employees and to encourage subject participation. This type of
ﬂexibility is more difﬁcult in the case of production workers. Also, a few employees

completed the survey at their desks when the employees were able to ﬁnd the time. The

74

questionnaires were administered by the researcher. The total number of surveys
returned was 261, which represented a capture rate of 40% for the targeted departments.

The research site located in the Midwest decided to administer the survey by
mailing the questionnaire to the employees’ homes. The surveys were coded by
department for those departments that participated in the full research design. The total
number of surveys returned was 228. This represented a capture rate of 12% of the plant
population. As a result of the limited capture rate, additional efforts were made to
administer the survey. These efforts included going to each of the targeted departments
and requesting that those employees that did not return the survey to take time at their
team meetings to complete the survey. Similar efforts were made to increase the number
of surveys completed by salaried employees. These efforts resulted in an additional 96
surveys being returned, increasing the total number of surveys returned to 324 and a
capture rate of 17% of the plant population.
Phase IV: Collecting Qualitative Data

Interviews with key leaders at each facility occurred in June 1999. The interview
data are used to provide insight and understanding. The interviews were conducted
during the same period in which the attitude surveys were being collected. The
interviews were conducted with the following positions at each location: (1) Plant
managers; (2) Human resource managers; (3) Superintendents; (4) Union ofﬁcials; and
(5) People identiﬁed as internal experts in the area of lean production. The interview
protocol is located in Appendix D.

This research design is a cross-sectional study and as such data were collected as

close to one point in time as allowed by the participating organizations. The survey of

75

the employee attitudes was conducted during June of 1999. The employee attitude
instrument was completed during June as a result of labor negotiation for the unionized
workforce. Both plant management and the union required that this data be collected
prior to .J uly 1999. Department performance data is based on actual performance tracked
as a required on-going plant activity for the end of month performance and year-to-date
for the periods of Febnrary, May and August 1999. For the plant located in the Midwest,
the supervisors and superintendents completed the assessment of perceived performance
during the month of October 1999 and the superintendent level completed the technical
lean production and integration instrument at the same time. As a result of internal
complications, the plant located in the Southeast was not able to provide this data until

January 2000.

Data Analysis Procedures

Multiple regression analysis and factor analysis will be used in this study.
Multiple regression analysis will be employed to estimate the model of the determinants
of the production work system. The determinants will include variables that deﬁne the
technical and people systems as well as the integration variables. The analysis of the
determinants will be assessed at one point and time and is thus a cross-sectional study.
Data at the department level will be analyzed to provide comparisons of the technical
system, people system and integration variables. Factor analysis will be used to evaluate
and possibly reduce the number of survey constructs and items.
Factor Analysis

Factor analysis will be conducted on the attitude survey entitled Perceptions

Regarding the Implementation of Lean Production. Factor analysis is used to discover

76

which variables in a data set form coherent subgroups (factors) that are relatively
independent from one another (T abachnick, 1983: 372). The speciﬁc purpose for factor
analysis in this study will be to reduce the number of items and variables to a smaller
number of clusters while retaining maximum spread among each of the survey constructs.
While each of the survey items have been reviewed extensively by numerous experts,
tested and discussed, factor analysis will provide additional content validity for the
survey constructs. In short, exploratory factor analysis will be conducted to consolidate
the number variables and items within the survey. The following steps are followed in
conducting the factor analysis: (1) Select and measure of the variables; (2) Prepare the
correlation matrix; (3) Determine the number of factors to be considered; (4) Extract the
factors from the correlation matrix; (5) If needed, rotate the factors to increase
interpretability; and, (6) Interpret the results (T abachnick, 1983: 373).
Hierarchical Regression

Hierarchical regression analysis permits assessment of whether each variable
signiﬁcantly predicts the dependent variable with the variance due to other independent
variables controlled (Cohen, 1983). Or put differently, hierarchical regression allows the
researcher to determine how to enter the independent variables (Tabachnick, 1983).

Hierarchical regression will be used to test the relationships in the following
hypotheses. In each of these‘hypotheses department size will be controlled for in
advance of entering the independent variables. The proposed analysis that ensues will
use three different but related analytical approaches. Approach 1 will be used for
hypothesis 1 through hypothesis 5 and will proceed along the following steps (See Table

3.8 for a review of the hypotheses):

77

Step 1: Department size will be entered ﬁrst as control variables.

Step 2: For hypotheses 1 and 2 technical systems will be entered second to

investigate the degree of association with department performance for hypothesis

1 and the degree of association with perceived performance for hypothesis 2.

These same steps will be followed in examining the degree of association between
people systems and the dependent variables.

Step 1: Department size will be entered ﬁrst as a control variable.

Step 2: People systems will be entered second to investigate the degree of

association with department performance (hypothesis 3), perceived performance

(hypothesis 4) and with work-related attitudes (hypothesis 5).

The research model for hypotheses 1 through 5 is as follows:

Figure 3.2: Research Diagram for Hypothesis 1 through Hypothesis 5

 

 

 

Control Independent Dependent
Variable “—V Variables “"‘P Variables

 

 

 

 

 

 

 

 

 

78

Table 3.8

Research Hypotheses

 

H1

H2

H3

H4

H5

H6

H7

H8

H9

H10

H11

Technical Systems of lean production are positively and signiﬁcantly related to
department performance.

Technical Systems of lean production are positively and signiﬁcantly related to
perceived department effectiveness.

People Systems of lean production are positively and signiﬁcantly related to
department performance.

People Systems of lean production are positively and signiﬁcantly related to
perceived department performance.

People Systems of lean production are positively and signiﬁcantly related to
work-related attitudes.

The consonance between the technical systems and people systems in lean
production are positively and signiﬁcantly related to department performance.

The consonance between the technical systems and people systems in lean
production are positively and signiﬁcantly related to perceived performance.

The consonance between the technical systems and people systems in lean
production are positively and signiﬁcantly related to work-related attitudes.

The relationship of the technical systems and people systems with department
performance will be partially mediated by integration practices.

The relationship of the technical systems and people systems with perceived
performance will be partially mediated by the integration practices.

The relationship of the technical systems and people systems with work-related
attitudes will be partially mediated by the integration practices.

 

Hierarchical regression is also called moderated regression (Stone, 1984). The

term moderator variable refers to an independent variable that potentially enters into

interaction with “predictor” variables, while having a negligible correlation with the

criterion itself (Cohen, 1983; Stone, 1984). The order of entry of the independent

79

variables becomes very important in studying moderating effects (Stone, 1984). In this
research regarding the consonance hypothesis, the proposition is that the people systems
must be in harmony with the technical system to maximize the effectiveness of the work
system. That is, the premise of the consonance hypothesis within the STS literature
proposes that organizations will perform effectively only to the extent that their social
structures are compatible with the requirements and dictates of the technical systems.
The second approach to be used in this research for hypothesis 6 through hypothesis 8
will follow the ensuing steps:

Step 1: Department size will be entered ﬁrst as a control variable.

Step 2: Technical systems and people systems are entered second and

simultaneously.

Step 3: Next, an interaction effect is assessed by multiplying technical systems

and people systems and entered third to investigate any moderating effect of

people systems on the dependent variables.

The research diagram for hypotheses 6 through 8 is as follows:

Figure 3.3

Research Diagram for Hypothesis 6 through Hypothesis 8

 

 

 

C tr 1 Technical Dependent
on O _’ Systems T Variables

Variable
People Systems as a
Moderator

 

 

 

 

 

 

 

 

 

 

 

 

 

80

The last three hypotheses build further upon the analyses proposed thus far.
Hypotheses 9 through 11 are designed to test for the mediating affect of the integration
variable. The ensuring steps will be followed for hypotheses 9 through 11:

Step 1: Department size will be entered ﬁrst as a control variable.

Step 2: Technical systems and people systems will be entered simultaneously.

Step 3: Integration will be entered last. My entering the integration variable last

will allow the examination of any additional variance that can be explained by the

integration activities.

After completing this series of steps, the order of entering the technical and
people systems variables and the integration variables are reversed. By reversing the
order of entry allows evaluation of any additional variance explained by the mediating
variable. The following steps are followed:

Step 4: Department size will be entered ﬁrst as a control variable.

Step 5: The integration variable is entered next.

Step 6: And, in this phase technical and people systems will be entered

simultaneously in the last step.

The research model for hypotheses 9, 10 and 11 is as follows:

81

Figure 3.4

Research Diagram for Hypothesis 9 through Hypothesis 11

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Department
Technical 5 Performance
Systems ’
Integration
People a
Systems a Work-Related
Attitudes

 

 

 

 

 

82

LIST OF REFERENCES

Cohen, J ., and Cohen, P. (1983). Applied multiple regression/corrleation analysis for the
behavioral sciences. (Second ed.). Hillsdale: Wiley.

Cook, J. D., Hepworth, Susan J ., Wall, Toby D., and Warr, Peter B. (1981). The

experience of work: A compendium and review of 249 measures and their use.
London: Academic Press.

Stone, E. F., and Hollenbeck, J. R. (1984). Some issues with the use of moderated
regression. Organizational Behavior and Human Performance, 34, 195-213.

Tabachnick, B. G., and Fidell, Linda S. (1983). Using Multivariate Statistics. New York:
Harper & Row.

83

CHAPTER FOUR
RESULTS
In this chapter, the results of each hypothesis are presented. The chapter also
includes descriptive statistics, correlation analysis, and factor analysis as well as a

summary of the research ﬁndings.

Descriptive Statistics and Correlation Analysis

In this section, the descriptive statistics and correlation analyses will be presented
for each of the assessment instruments. The assessment instruments include: 1)
Perceptions Regarding the Implementation of Lean Production, which includes the
independent people systems variables (e.g., supervisory behaviors and management
support) as well as the dependent work-related attitude variables (e. g., team efﬁcacy and
commitment to lean production strategy); 2) The Implementation of Lean Production
assessment instrument, which includes the technical systems assessment questions and
the integration items; and, 3) Perceptions Regarding the Effectiveness of the
Implementation of Lean Production. This section also provides a correlation analyses of
the independent variables with the dependent variables.

Perceptions Regarding the Implementation of Lean Production

The mean, standard deviations, and correlations for each scale are presented in
Table 4.1. (The people systems assessment instrument is located in Appendix A.) The
reliability coefﬁcients (alphas) are presented on the diagonals. The people systems scales
were created in a two step process. First, the data was aggregated at the individual level.
Second, mean responses were created at the work unit level. All questions are based on a

ﬁve-point scale, except team efﬁcacy (item 18), which is based on a seven-point scale.

The measure for lean training is calculated based on a series of yes/no (ordinal) responses
for speciﬁc lean training participated in by each survey respondent. Mean responses
were then created for each department. There are eight lean training questions in total.
Hence, the range of responses for any respondent varies between zero and eight (0-8).
There is no reliability coefﬁcient for lean training (item 19).

Table 4.1 shows that the majority of the scales are signiﬁcantly correlated. Of the
171 possible correlations, 139 are correlated at the .01 level, 8 are correlated at the .05
level, and 24 are not correlated. The table shows that lean training is not signiﬁcantly
correlated with any of the scales, except for a -.26 correlation with workplace bridging at
the .05 level. The table also shows that the reliability coefﬁcients (alphas) range from the
high of .96 for supervisory behaviors to a low of .74 for both cooperative union
management behaviors and for commitment to lean strategy. The average reliability for

the entire instrument is .85.

85

Table 4.1

Descriptive Statistics, Correlations and Reliability Coefficients for
People Systems of Lean Production

 

Variable M SD 1 2 3 4 5 6 7 8

 

l. Supervisory 3.10 .6159 (.96)

behavior

2. Management 2.52 .5527 .56" (.92)

”FPO“

3. Cooperative 3.10 .4992 .34" .63" (.74)

Union Management

Relations

4. Commitmentto 3.27 .3928 .29" .51" .33“ (.74)

lean strategy

5.Jobsatisfaction 2.93 .5816 .67" .69" .38" .49” (.81)

6. Perceived 2.98 .4471 .68" .62" .39" .64" .67" (.87)

learning

environment

7. Developmental 2.95 .4503 .74" .71“ .52" .39" .66" .62" (.79)

focus

8. Perceivedteam 3.24 .4651 .37" .71" .62“ .58" .64“ .55" .59" (.92)
performance

9. Managing change 2.74 .5167 .55“ .67” .65" .51“ .52" .68“ .68" .65"
10. Teamwork 2.92 .5211 .42" .57" .40“ .41" .36" .59" .48" .41"
ll.lnvolvementl 2.58 .6411 .63" .50“ .15 .44" .48M .66" .61" .36"
psychological

participation

12.Processfocus 3.25 .4893 .48" .65" .51“ .43“ .55" .58“ .56" .61“
13. Proactive 2.51 .5363 .52“ .65“ .51" .49" .47" .65" .63" .47"

Problem solving
l4.Workplacetrust 2.58 .4618 .52“ .61" .44“ .26“ .53" .52" .53" .38"

15. Workplace 2.96 .4645 .59“ .38" .29* .35" .44" .60“ .51" .29"
bonding

l6. Workplace 2.68 .4678 .55“ .69“ .62" .29“ .62" .56“ .76“ .58"
bridging

l7. Conﬂict 2.91 .4731 -78" .61“ .48" .34“ .56" .57“ .73" .48"
resolution climate

18. Team efﬁcacy 4.65 .8607 .16 .36" .27“ .10 .13 .25* .24 .28“
l9.Leantraining 4.08 1.57 .10 -.07 -.14 .08 -.06 .15 .06 -.13

 

N = 66 departments listwise.

*Correlation is signiﬁcant at the .05 level (2-tailed).
"Correlation is signiﬁcant at the .01 level (2-tailed).

Internal consistency reliability coefﬁcients (alphas) appear in parentheses along the main

diagonal. Lean training was based on a series of yes/no (ordinal) responses and as such
there is no reliability coefﬁcient.

86

Table 4.1 (Continued)

Descriptive Statistics, Correlations and Reliability Coefficients for
People Systems of Lean Production

 

Variable

10

ll

12

l3

I4

15 16 17 18

 

l. Supervisory
behavior

2. Management
support

3. Cooperative
Union Management
Relations

4. Commitment to
lean strategy

5. Job satisfaction

6. Perceived
learning
environment

7. Developmental
focus

8. Perceived team
performance

9. Managing change
10. Teamwork

l l. Involvement!
psychological
participation

12. Process focus
13. Proactive
Problem solving
l4. Workplace trust
15. Workplace
bonding

l6. Workplace
bridging

1 7. Conﬂict
resolution climate
18. Team efﬁcacy
l9. Lean Trainiﬂ

(.84)
.56"
.54“

.69“
.69"

.57"
.47“

.65"
.57"

.34“
.15

(.86)
.65""'I

.71"
.82“

.55"
.67"

.63"
.51“

.54"
.12

N = 66 departments listwise.

( .86)
.55 *"
.70"

.47**
.66*‘

.54"
.65"

.20
.12

(.87)
.78"

.531.".
.47**

.65"
.48"

.59"
.23

(.93)

_60et
.60'”

.74**
.60**

.47""I
.05

*Correlation is signiﬁcant at the .05 level (2-tailed).

"Correlation is signiﬁcant at the .01 level (2-tailed).

(.77)
.69"

.67"
.43"

.54“
-.O3

(.87)

.57" (.86)

.53" .68" (.81)
.36“ .33" .09

.08 -.26* -.13

(.95)
.17

Internal consistency reliability coefﬁcients (alphas) appear in parentheses along the main
diagonal. Lean training was based on a series of yes/no (ordinal) responses and as such

there is no reliability coefﬁcient.

87

Implementation of the Technical Systems of Lean Production

The mean, standard deviation, and correlations for each item are presented in
Table 4.2. (The technical systems assessment instrument is located in Appendix B.) The
reliability coefﬁcient (alphas) for the technical systems assessment items is .91. The
reliability coefﬁcient (alphas) for the integration items is .82. The integration questions
include items 3, 10, 11, and 16. All other questions are technical systems assessment
items. All questions on this instrument are four-point scales. Questions 9 and 13 were
eliminated from this analysis because of a low response rate. Follow-up questions of
internal plant experts revealed that item 9 regarding process capability achieved a low
response rate because the respondents found the item to be confusing. In the case of item
13 regarding quick set-up, this technical system is not used extensively in some
departments resulting a low response rate.

Table 4.2 shows that most of the items are signiﬁcantly correlated at the .01 level.
Of the 91 possible correlations, 86 are signiﬁcantly correlated at the .01 level, two items
are correlated at the .05 level, and two are not signiﬁcantly correlated. Of the ten

remaining technical assessment questions, the reliability coefﬁcient (alpha) is .92.

88

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89

Lean Production and Perceived Effectiveness

The Perceptions Regarding the Effects of the Implementation of Lean Production
assessment instrument was designed to measure the perceived effectiveness from the
supervisor and superintendent perspective. The mean, standard deviation and correlation
for each item is presented in Table 4.3. The internal reliability coefﬁcient (alpha) is .97.
All questions in the instrument are seven-point scales. All items are statistically
signiﬁcant at the .01 level.

A total of 51 valid effectiveness surveys were completed by 14 superintendents
across the two facilities. On average, each superintendent completed 3.6 surveys. A total
of 70 supervisors completed 70 valid surveys across the two plants. Each survey was
completed at the department and shift level, which is referred to throughout this analysis

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91

Correlation of Independent and Dependent Variables

Table 4.4 provides the correlations of the independent and dependent variables.
Bivariate correlation was used to compare the variables. Items 1 through 13 are scales
based on 533 valid survey respondents as well as the work-related attitudes (commitment
to lean strategy, job satisfaction, learning environment, and team efﬁcacy), and perceived
department performance. Mean effectiveness (MGT) is based on 51 effectiveness
surveys completed by 14 superintendents and 70 supervisor completed surveys for 70
different departments across the two plants. The effectiveness surveys completed by the
supervisors and superintendents were averaged for each department and shift. People
systems composite is a scale based on items 1 through 13 in Table 4.4. Lean training is
based on eight yes/no questions on the survey. Each of the yes responses was summed to
provide an overall lean training measurement at the individual level. Hence, the range for
individual responses to lean training is 0-8.

Mean effectiveness of the supervisor and superintendent level (MGT) is positively
and signiﬁcantly correlated with lean training, integration and the technical systems of
lean production at the .05 level. In contrast, developmental focus and workplace bridging
is negatively and signiﬁcantly correlated at the .01 level. Each of the following work-
related attitudes including commitment to lean strategy (CLS), job satisfaction (J 8),
learning environment (LE), and perceived department performance (PTP) is positively
and signiﬁcantly associated with each of the people system scales (items 1-13) and with
people systems composite at the .05 level. Team efﬁcacy is statistically and positively
associated at the .05 with these same measures, except for supervisory behaviors,

involvement psychology, and conﬂict resolution, which are not correlated. Lean training

92

is positively and signiﬁcantly correlated only with the mean effectiveness of the
supervisor and superintendent level (MGT), integration is positively and signiﬁcantly
correlated with mean effectiveness (MGT) at the .05 level and is statistically and
negatively associated with job satisfaction (J S) at the .01 level. Technical systems is
statistically and positively associated with mean effectiveness (MGT) at the .05 level and
correlated with learning environment at the .01 level. A correlation analyses of all of the

variables in this study are provided in Table 4.16 located in Appendix F.

93

Table 4.4

Correlation of Independent Variables with Dependent Variables

 

 

Independent Variables Dependent Variables
MGT CLS JS LE PTP TE
1. Supervisory Behaviors .08 .29M .67** .68** .36** .16
2. Management Support -. 12 .53** .69** .62** .71** .34**
3. Cooperative L-M Relations -.06 .35** .38** .39** .62** .27*
4. Developmental Focus. -.23* .39** .67** .63** .59** 24* '
5. Managing Change .07 .51** .52" .68** .65** .34M
6. Teamwork .16 .41** .36** .59** .41** .54“
7. Psychological Participation .09 .44** .48** .66** .35** .20
8. Process Focus .08 .43** .55** .58** .61** .59**
9. Proactive Problem Solving .08 .49** .47** .65** .47** .47**
10. Workplace Trust -.06 26* .53** .52** .38** .54**
11. Workplace Bonding .10 .35** .43** .60** .28* .36**
12. Workplace Bridging -.22* .29** .62** .56** .58** .33**
13. Conﬂict Resolution -.17 .34** .56** .57 ** .48** .09
14. People Systems Composite -.03 .46** .64** .75** .58** .41**
15. Lean Training .43** .08 -.06 .15 -. 13 .17
16. Integration .73** .11 -.23* .10 -.20 .03
i7. Technical Systems .82** .16 -.05 .28* .04 .17

 

Listwise N = 66.

Where: MGT = Mean score of supervisor and superintendent rating of department
effectiveness; CLS = Commitment to Lean Strategy; J S = Job Satisfaction; LE =

Learning Environment; Perceived Team Performance = PTP; and, TE = Team Efﬁcacy.

"Correlation is signiﬁcant at the .01 level (l-tailed).
*Correlation is signiﬁcant at the .05 level (l-tailed).

Factor Analysis

This section of chapter 4 will present the factor analysis for the people systems
assessment instrument. Next, the results of the factor analysis of the technical assessment
instrument will be presented.

Factor Analysis for People Systems

Exploratory factor analysis using varimax rotation was used to assess the factorial
structure of the thirteen people systems scales and lean training (see Appendix A). The
principal components method was used and the rotation converged in ﬁve iterations upon
three interpretable factors. Table 4.5 shows the rotated factor matrix. Overall, these
factors accounted for 75.20% of the variance in these data.

Factor 1 consists of the following scales: 1) Labor management relations; 2)
Managing change; 3) Management support; 4) Process focus; 5) Workplace bridging; 6)
Problem solving; and, 7) Team work. These seven scales accounted for 32.81% of the
total variance in these data. For the purpose of this study, factor 1 will be labeled as inter
group connections.

Factor 2 consists of the following scales: 1) Involvement psychology; 2)
Supervisor behavior; 3) Workplace bonding; 4) Conﬂict resolution; 5) Developmental
focus; and, 6) Workplace trust. These six scales accounted for 32.52% of the total
variance in these data. Factor 2 will be labeled as intra group connections. Factor 3
consists of eight yes/no lean production training questions that accounts for 9.86% of the
variance and will be labeled as lean training.

Correlation analysis was conducted on these three factors and a strong statistical
signiﬁcant relationship between factor 1 and factor 2 (r = .78) was found. Even after

factor 1 was limited to include just labor management relations through workplace

95

bridging, and factor 2 was amended to include involvement psychology through conﬂict
resolution, a strong statistical signiﬁcant relationship was still found between these
factors (r = .65). In neither case was lean training (factor 3) found to be signiﬁcantly
correlated with factor 1 (i.e., r = .02 in the ﬁrst case and r = -.09 in the second case) or
factor 2 (i.e., r = .03 in the ﬁrst case and r = .06 in the second case).

As a result people systems is divided into two factors for this analysis. Factor one
is based on all those items identiﬁed in factor 1 and 2 above and is identiﬁed as people
systems composite. The second factor of people systems will be based solely on lean

training. The correlation is .02 between lean training and people systems composite.

Table 4.5

Results of Factor Analysis of Lean Production People Systems Scales

 

 

 

Factor Loadings

1 2 3
Labor management relations .87 .05 -.21
Managing change .76 .38 .15
Management support .76 .39 -.09
Process focus .74 .36 .36
Workplace bridging .70 .53 -.23
Problem solving .67 .55 .21
Teamwork .55 .53 .35
Involvement psychology .21 .84 .22
Supervisory behavior .26 .81 «.05
Workplace bonding .24 .78 .18
Conﬂict resolution .38 .75 -.25
Developmental focus .54 .65 -. 18
Workplace trust .53 .53 .03
Lean training -.05 .01 .88
N=67

97

Factor Analysis for Technical Systems

Exploratory factor analysis using varimax rotation was used to assess the factorial
structure of the 12 technical system questions (see Appendix B). The principal
components methods was used and the rotation converged in three iterations upon two
factors. Table 4.6 shows the rotated matrix. Overall, these two factors accounted for
71.76% of the variance in these data.

The 12 questions in the technical assessment instrument are based on six different
areas of lean production. Each of the six technical aspects of lean production consisted of
two questions. The six areas included the following: 1) Flow manufacturing; 2)
Employee involvement; 3) Workplace organization; 4) Quality; 5) Operational
availability; and, 6) Material movement. This instrument measures the current status of
lean production at the department and shift level in terms of the technical aspects of lean
production. As noted previously, question 9 and 13 was excluded from this analysis as a
result of low response rate on these items. Process capability was not answered because
the item appeared to be confusing to the respondents. In the case of quick-set up, this
process is not used extensively in some work situations.

As shown in Table 4.6.items 5, 6, 4, 7, 2 and 15 formed factor 1. This factor may
be labeled workplace organization and employee support. Factor 1 accounted for 40.74%
of the variance in these data.

Table 4.6 also shows that items 8, l, 14, and 12 formed factor 2. This factor may
be labeled external support and quality. This factor accounted for 31.02% of the variance

in these data.

98

Correlation analysis was conducted on these two factors and strong statistical
signiﬁcant relationship between factor 1 and factor 2 was (r = .67) found. If factor 1 is
limited to items 5, 6, 4, and 7, a strong statistical signiﬁcant association (r = .61) between
the amended factor 1 and factor 2 prevails. As a result, a single scale will be used for
technical systems in this analysis. The reliability coefﬁcient for the technical systems
instrument is .91 (alpha).

Table 4.6

Results of Factor Analysis of Lean Production Technical Systems

 

 

Factor Loadings
Item 1 2
5. Natural workgroup .88 .23
6. Clear, clean, organized & maintained .88 .08
4. Cross function & skills .84 .38
7. Visual controls .79 .25
2. Takt time .75 .42
15. Internal material delivery .55 .47
8. Inspection & testing .02 .91
1. Organized by value stream .31 .77
14. Container right sizing & operator support .37 .76
12. Owner operator .31 .70

 

N=50 listwise.

Results of Analyses of Hypothesis

This section of Chapter Four will conduct the tests of the hypotheses. The plant
located in the Southeast was unable to provide archival data at the department and shift
level. As such, the analyses related to archival performance measures will be limited to
the plant located in the Midwest. All other analysis will be based on data supplied by
both facilities.

The archival performance data originally designed into this research project
included cost, productivity, quality, delivery and suggestion data. Representatives from
each location assured this researcher that ample data would be available at the department
level. However, after a great deal of effort, the plant located in the Southeast was unable
to provide any useful data at the department level and the plant located in the Midwest
was only able to provide suggestion and uptime data at the appropriate level for this
study. Because on the reduced number of the departments included in the analysis
related to the archival performance data the opportunity to ﬁnd signiﬁcant results is
substantially diminished. In addition, since the two plants were chosen to participate in
the research based on anticipated variance, by eliminating one of the plants from the
archival performance analysis. further reduced the opportunity for signiﬁcant ﬁndings.

Three types of hypothesis testing will be conducted. For hypotheses 1 through
hypotheses 5 multiple linear regression will be used. Moderation hypothesis testing will
be used for hypothesis 6 through hypothesis 8. And, mediation hypothesis testing will be
used for analyzing hypotheses 9 through 11. Department size is a control variable for

each hypothesis and will be covaried out in step one for each hypothesis.

100

Hypothesis 1

Hypothesis 1 states that the technical systems of lean production are positively
and signiﬁcantly related to department performance. That is, as department ratings of the
technical elements of lean production increase department performance measures
improve. Department performance measures in this analysis include the number of
employees to make at least one suggestion per department and shift annually calculated
as suggestion participation rate (for calendar year 1999) and uptime by department and
shift as a percent of uptime over an eight month period (January through August, 1999).
Table 4.7 shows the results. Technical systems had no statistical signiﬁcant impact on
suggestion participation or uptime. Hypothesis 1 is not supported.

While the technical systems of lean production was not a signiﬁcant predictor of
department performance, the limited performance data made available by the plant
located in the Midwest signiﬁcantly limited the Opportunity to ﬁnd signiﬁcant results.
Uptime data has little variance across the departments in this study. While uptime
performance is an important measure for manufacturers it has limited capability in
delineating mass from lean producers. This is a counter intuitive point, yet important.
Mass producers are driven by production numbers and as such attempt to maintain high
uptime to maximize output. In contrast, lean producers also strive to for output, but use
uptime performance as tension to drive improvement efforts. High uptime performance
may suggest good performance, but it may also be an indicator of poor improvement in
terms of incremental improvement efforts through the elimination of waste. This tension
provides a key competitive advantage for lean producers founded on the integration of

the technical and people systems of lean production.

101

In addition, the suggestion data tracked and provided for this study at the
department level has limited potential for this analysis. For example, each member of a
department could make one suggestion in 1999 and receive the same measure for
suggestion rate as another department in which each member of a department provided
ﬁve suggestions. That is, once a department member made just one suggestion, any
future suggestions by that member are irrelevant in calculating the suggestion rate for that
department.

Moreover, without the data from the plant located in the Southeast, potential
variance among the participating departments in this study is signiﬁcantly reduced. This
limitation is not limited to just the reduced number of departments included in the study
(i.e., a smaller N). It is also speciﬁc to this study. Each of the locations were identiﬁed
as following a diffusion strategy in the adoption of lean production as either a more
technical focused or a more integrated application of lean production. As such, the

possible variance in the study was signiﬁcantly reduced for assessing hypothesis 1.

102

Table 4.7

Regression Results of the Test for Technical Systems Effect on
Suggestion Participation Rate and Uptime

 

Independent Variables Suggestion Participation Uptime

 

Step 1: Control
Team Size (Beta) -.04 .11

R square .00 .02

Step 2: Main Effect

 

Technical Systems (Beta) .20 .31
R square change .04 .09
N = 26

*p<.10, **p<.05, and ***p<.01
Hypothesis 2

Hypothesis 2 states that the technical systems of lean production are positively
and signiﬁcantly related to perceived department effectiveness. In other words, the
adoption of the technical components of lean production will result in the perception of
improved department effectiveness. This hypothesis was tested using linear regression
analyses. The results are shown in Table 4.8. Technical systems were regressed with
perceived effectiveness. The results, after controlling for department size, indicate a
positive and signiﬁcant relationship between technical systems and perceived
effectiveness. That is, as department ratings of the technical elements of lean production
increase, perceived department effectiveness improves as rated by supervisors and

superintendents (MGT). This relationship was signiﬁcant at the .01 level. However,

103

after controlling for team size, this same relationship was not signiﬁcant as assessed by
department level employees (PTP). Hypothesis 2 is therefore supported in part, when
assessed by management level employees, but not supported when assessed by
department level employees.

The partial support for hypothesis 2 suggests that managers and employees at the
department level differ signiﬁcantly in terms of their perceptions related to the adoption
of the technical systems of lean production and its impact on performance. These results
for managers are consistent with a “Tayloristic” or scientiﬁc management perspective on
change. That is, managers view the brownﬁeld conversion to lean production as a

technological transformation.

104

Table 4.8

Regression Results of the Test for Technical Systems Effect on
Perceived Effectiveness and Work-Related Attitudes

 

 

 

 

Dependent Variables
Independent Perceived Work-Related Attitudes
Variables Effectiveness

MGT PTP CLS J S LE TE
Step 1: Control
Team Size

-.19** .38** .23 .43*** .40*** .28*
Beta

.063* .10** .065 .10** .I9*** .09”
R square
Step 2: Main effect
Technical Systems
Beta .91*** -. 14 .06 -.25* .10 .04
R square change .63 *** .02 .00 .05* .01 .00
N=66

*p<.10, **p<.05, and ***p<.01
Where: MGT = Superintendent and Supervisor rating of perceived effectiveness; PTP =
Employee rating of perceived department performance; CLS = Commitment to lean
strategy; I S = Job satisfaction; LE = Learning environment; TE = Team efﬁcacy.
Hypothesis 3

Hypothesis 3 asserts that people systems of lean production are positively and

signiﬁcantly related to department performance. In other words, as department ratings of

the people systems of lean production increase, department performance measures

105

improve. As stated above, department performance measures include the number of
employees to make at least one suggestion per department and shift annually calculated
as suggestion participation rate and uptime by department and shift as a percent of uptime
over an eight-month period. Also stated above, this hypothesis will be limited to the
plant located in the Midwest. Table 4.9 shows the results of the regression analysis.
People systems measures include people systems composite (composite) and lean training
(training). People systems had no statistical signiﬁcant impact on suggestion
participation or uptime. Hypothesis 3 is not supported.

Table 4.9

Regression Results of the Test for People Systems Effect on
Suggestion Participation Rate and Uptime

 

Independent Variables Suggestion Participation Uptime

 

Step 1: Control
Team Size (Beta) .10 .06

R square .00 .02

Step 2: Main Effect

People Systems

 

Composite (Beta) -.06 .14

Training (Beta) .30 .03
R square change .09 .02
N = 26

*p<.10, **p<.05, and ***p<.01

106

Table 4.10

Regression Results of the Test for People Systems Effect on
Perceived Effectiveness and Work-Related Attitudes

 

 

Dependent Variables
Independent Perceived Work-Related Attitudes
Variables Effectiveness

MGT PTP CLS J S LE TE
Step 1: Control
Team Size
Beta .20 .06 .00 .08 . 10 .10
R square ' .05* .07** .11*** .04 .16*** .07**
Step 2: Main effect
People Systems
Composite (Beta) -.11 .55*** .14*** .69*** .67*** .34***
Training (Beta) .40*** -.13 .03 -.06 .11 .15

R square change .16*** .27*** .09** .40*** .39*** .12**

 

*p<J0
** p < .05
*** p < .01
Hypothesis 4

Hypothesis 4 states that the people systems of lean production are positively and
signiﬁcantly related to perceived department effectiveness. That is, the adoption of the
people system components of lean will result in the perception of improved department

effectiveness. The two measures of people systems are used in this analysis include;

people systems composite (composite), and lean training (training). Table 4.10 provides

the results of this analysis. The two measures of people systems were regressed with
perceived effectiveness at the supervisor and superintendent level (MGT) and department
employee level (PTP). The results identify no statistical signiﬁcant relationship between
people systems composite (composite) and perceived improved effectiveness at the
management (MGT) level (B = -.1 1). However, there is a statistically signiﬁcant
relationship between lean training (training) and perceived improved effectiveness as
rated by supervisor and superintendents (MGT) at the .01 level (B = .40). The R square
change attributable to the lean training factor of people systems is .16.

In contrast, when the two measures of people systems are regressed with
perceived effectiveness as rated by employees the reverse was found. The people
systems composite (composite) is statistically related to perceived performance as rated
by department level employees (PTP) at the .01 level (B = .55). However, lean training
(training) is not statistically associated with perceived performance at the employee level.
The R square change accounted for by people composite systems is .27. Hypothesis 4
was partially supported.

The ﬁndings in hypothesis 4 provide further support to differences in perceptions
of managers and employees in the transformation to lean production. That is, managers
perceive no relationship between people systems of lean production and department
performance. However, lean training is associated with perceptions of improvement in
department performance. The ﬁndings in hypothesis 4 coupled with hypothesis 2
suggests that managers perceive the transformation to lean production as phenomenon
driven by technological change combined with changes by their employees achieved

through lean training. However, the reverse ﬁndings found for employees suggest that

108

employees look for changes in management behaviors (people systems) not technological
change or training. Or put differently, employees believe changes have occurred when
they observe changes in management behaviors.
Hypothesis 5

Hypothesis 5 states that the people systems of lean production are positively and
signiﬁcantly related to work-related attitudes. Table 4.10 shows the results of this
hypothesis. These work-related attitudes include commitment to lean strategy (CLS), job
satisfaction (J S), learning environment (LE), and team efﬁcacy (TE). In other words, the
adOption of the people system components of lean production will result in improved
employee perceptions of work related-attitudes. A positive and statistically signiﬁcant
relationship between people systems and work-related attitudes indicate that, as the rating
of people systems increases, work-related attitudes towards commitment to lean strategy,
job satisfaction, learning environment, and team efﬁcacy improves. When people
systems composite (composite) were regressed with each of the work-related attitudes,
the composite measure was found to have a positive and statistically signiﬁcant
association with each of the work-related attitudes at the .01 level. In contrast, lean
training (training) had no impact on work-related attitudes. Hypothesis 5 was partially
supported.

Hypothesis 5 provides further support to the suggestion above that employees
look to changes in management behaviors. These ﬁndings suggest that training can

provide understanding, but change occurs when behaviors change - not good intentions.

109

Moderation Hypothesis Testing

This second set of hypothesis testing that follows (hypotheses 6-8) involves
moderating hypotheses. Moderation was tested using multiple regression. Two cross-
products are formed by multiplying the independent variables. A signiﬁcant regression
for the cross product terms indicate the presence of an interaction. The means are
evaluated to see the direction of the interaction. One cross-product is created by
multiplying technical systems by people systems composite (i.e., tech. x composite). A
second cross-product is created by multiplying technical systems by lean training (i.e.,
tech. x training). The results are presented in Table 4.11 and Table 4.12.
Hypothesis 6

Hypothesis 6 states that the consonance between the technical systems and people
systems in lean production are positively and signiﬁcantly related to department
performance. In other words, the congruence between the technical systems and people
systems in lean production is predicted to moderate the effects of these systems resulting
in improved performance. Again, department performance measures in this analysis
include the number of employees to make at least one suggestion per department and
shift annually calculated as suggestion participation rate and uptime by department and
shift as a percent of uptime over an eight-month period. Table 4.11 shows the results of
the regression analyses. Again, this analysis is limited to the plant located in the
Midwest. The cross-product variables (tech. x composite and tech. x training) were
regressed with suggestion participation rate and uptime. The control variable as well as
the independent variables was not found signiﬁcant. And, the cross-products or

interaction was not statistically signiﬁcant. Hypothesis 6 was not supported.

110

Hypothesis 7

Hypothesis 7 asserts that the consonance between the technical systems and
people systems in lean production are positively and signiﬁcantly related to perceived
performance. In other words, departments in which the technical systems and people
systems that are congruent are more likely to perceive enhanced department
effectiveness. Therefore, perceived effectiveness was regressed on the independent
variables and the cross-products. Table 4.12 shows the results of the regression analysis.
The results indicate a signiﬁcant relationship between technical systems (B = 1.58,
p<.01), and lean training (B = 1.04, p<.05) with perceived effectiveness at the supervisor
and superintendent level (MGT). Also, a signiﬁcant and negative association with the
interaction between technical systems and lean training (tech. x training) (B = -1.27,
p.01) with perceived effectiveness as rated by supervisors and superintendents (MGT).
No interaction effect was found for perceived performance as assessed by employees
(PTP). Hypothesis 7 was not supported.

One viable explanation for the negative relationship between the interaction of
technical systems and lean training as assessed by the management level is that
management may perceive investment in training as having a negative impact on
production goals. That is, if employees are Spending time in training during working
hours, these employees are not available to perform production activities. However, as
indicated in hypothesis 2, once the initial investment is made in training, management
(i.e., supervisors and superintendents) value employee increased capabilities in terms of

perceived department performance.

111

Hypothesis 8

Hypothesis 8 states that the consonance between the technical systems and people
systems in lean production are positively and signiﬁcantly related to work-related
attitudes. In other words, departments in which the technical systems and pe0ple systems
are congruent are more likely to have a positive impact on work-related attitudes.
Therefore, each of the work-related attitudes was regressed on the independent variables
and the cross-products. The results of this analysis are presented in Table 4.12. A
signiﬁcant interaction effect was found for job satisfaction (1 S) and technical systems and
people systems composite (tech. x composite) (B = 1.60, p<.05) as well as a signiﬁcant
negative interaction effect for technical systems and lean training (tech. x training) (B = -
1.32, p<.05).

The results in Table 4.12 also show a signiﬁcant negative interaction effect for
learning environment (LE) and technical systems and lean training (tech. x training) (B =
-1.57, p<.01). A positive and signiﬁcant interaction effect was found for team efﬁcacy
(TE) and technical systems and lean training (tech. x training) (B = 1.76, p<.05).

The signiﬁcant interaction effect for job satisfaction and technical systems and
people system composite supportsthe proposition that work organizations will perform
effectively only to the extent that their people systems are compatible with the technical
systems. This ﬁnding suggests that people systems consistent with a lean production
context provides increased job satisfaction for employees when compared with
departments that have not made similar changes in people systems. In contrast,
signiﬁcant negative interaction effect for job satisfaction and technical systems and lean

training suggests that training without requisite changes in people systems may frustrate

112

employees resulting in decreased job satisfaction. These ﬁndings provide additional
support for the proposition that training can provide understanding, but change occurs
when behaviors change - not good intentions promoted in lean training.

In addition, the signiﬁcant interaction effect for team efﬁcacy and lean training
also supports the consonance hypothesis. This ﬁnding appears to support the basic
concept of team efﬁcacy. Team efﬁcacy is a measure of a team’s belief that they can
positively impact their performance. The interaction of lean training and technical
systems indicates that lean training impacts a team’s belief in their ability to have a
positive impact on team performance.

The signiﬁcant negative interaction of learning environment and technical
systems and lean training is counter to hypothesis 8. While this ﬁnding in-part
challenges hypothesis 8, a discussion of this ﬁnding coupled with the discussion of the
results of hypotheses 9 through 11 may help explain this ﬁnding, which will be discussed
later in this chapter.

The ﬁnding in support of signiﬁcant moderation was limited in this study. A
plausible explanation is the limited power given the number of observations decreased to
26 for the archival performance data. As stated earlier the limited performance measures
(i.e., suggestion rate and uptime) and the reduced variance caused by only one plant
supplying performance data also decreased the opportunity to ﬁnd signiﬁcant results. In
addition, reduced reliability is inherent in the calculation of an interaction effect. For
example, if two variables have a reliability of .80, the reliability of the interaction
variable is .64 (i.e., .80 x .80 = .64). Yet, given these limitations a moderation effect was

found for job satisfaction and team efﬁcacy.

113

Table 4.11

Regression Results of the Test for Moderation of Technical and People Systems on
Suggestion Participation Rate and Uptime

 

 

 

Independent Variables Suggestion Participation Uptime
Step 1: Control
Team Size (Beta) .16 .10
R square .00 02
Step 2: Main Effect
Technical Systems (Beta) .69 -.71
People Systems:

Composite (Beta) .03 -.73

Training (Beta) .69 -.46
R square change .1 7 -.09
Step 3: Interactions
Tech. x composite -.36 1.21
Tech. x training -.37 .58
R square change .00 01
Overall adjusted R square -.02 -. 18
Overall model F .92 .41
Standard error of the .26 14.57
estimate
N = 26

*p<.10, "p<.05, and ***p<.01

114

Table 4.12

Regression Results of the Test for Moderation of Technical and People
Systems on Perceived Effectiveness and Work-Related Attitudes

 

 

Dependent Variables
Independent Perceived Work-Related Attitudes
Variables Effectiveness
MGT PTP CLS J S LE TE
Step 1: Control
Team Size
Beta -.16 .05 .05 -.04 -.85 .14
R square change .07* 10" .10M .10M .19*** .09**
Step 2: Main effects
Technical Systems
Beta 158*“ -.58 -.15 -.89 .48 -1.57
People Systems:
Com osite eta
Trainlin (Ega) ) .07 .19 .05 .03 .61 * 1.19
g 1.04" -.04 .30 .93" 1.10*** -1.12
R square change .64*** 25*" .19" 46*" .45*** .17"
Step 3: Integgtions
TCCh- X COHIPOSite 0.26 .78 .90 1.60" .48 1.03
Tech. x Training -l.27 -.14 -.68 -l.32** -l.57 1.76"”I
*** *t*
R 5‘1“” e Change .04" .01 .02 .07" .06" .19"
' r R
Overall adj“ ed .73 .28 .22 .58 .66 .27
square
Overall model F 2360*" 43*" 34*" 12.76"" 1860*“ 42*"
Stand?“ error Of .82 .46 .37 .34 .25 .76
the estimate

115

Mediation Hypothesis Testing

This third set of hypothesis testing that follows (hypotheses 9-11), involves
mediation hypotheses. The model hypothesizes that integration mediates the relationship
between technical systems and department performance (archival and perceived) as well
as work-related attitudes (commitment to lean strategy, job satisfaction, learning
environment, and team efﬁcacy). Likewise, the model hypothesizes that integration
mediates the relationship between people systems (people systems composite and lean
training) and department performance (archival and perceived) as well as work-related
attitudes (commitment to lean strategy, job satisfaction, learning environment, and team
efﬁcacy). The existence of a mediation effect requires that the mediation variable (in this
case integration is the mediating variable) adds to the prediction of the dependent
variables over and above the independent variables. This is determined by evaluating the
beta weights of the independent variables to observe if the independent variables become
non-signiﬁcant or become lower. That is, the beta weights are compared when the main
effect is entered before the mediator (equation 1, Table 4.13), and when the mediator is
entered before the main effect (equation 2, Table 4.13).
Hypothesis 9

Hypothesis 9 states that the relationship of the technical systems and people
systems with department performance will be partially mediated by integration practices.
In other words, the effects of the mediating variable (i.e., integration) will explain
variance above and beyond the variance explained by the technical and people systems of
lean production. As stated above, department performance measures in this analysis

include the number of employees to make at least one suggestion per department and

116

shift annually calculated as suggestion participation rate and uptime by department and
shift as a percent of uptime over an eight-month period.

The results in Table 4.13 show that that lean training (training) was positively
related to suggestion participation (B = .43, p<.05). However, the data also shows that
integration did not explain a signiﬁcant increase in variance above technical systems or
people systems. That is, integration explained all of the variance explained in suggestion
participation. In addition, integration was positively related to uptime performance (B =
.59, p<.10). The results show that integration accounted for all of the variance explained
in uptime performance (change in R square in equation 1 = .13, p<.01 and change in R
square in equation 2 = .18, p<.05). Thus, the results do not support the mediated

relationship proposed in hypothesis 9.

117

Regression Results of the Test of Integration as a Mediator for Technical and People

Table 4.13

Systems Effect on Suggestion Participation Rate and Uptime

 

 

Independent Variables Suggestion Participation Uptime
Equation 1
Step 1: Control
Team Size (Beta) .35 .26
R square .00 .02
Step 2: Main Effect
Technical Systems (Beta) -. 16 -. 14
People Systems:
Composite (Beta) .04 .16
Training (Beta) .43** . 16
R square change .1 7 .09
Step 3: Mediator
Integration (Beta) .63** .59*
R square change .12** .13*
Equation 2
Step 1: Control
Team Size (Beta) .35 .26
R square .00 .02
Step 2: Mediator
Integration (Beta) ~63” .59*
R square change .14** .18M
Step 3: Main Effect
Technical Systems (Beta) -.16 -. 14
People Systems:
Composite (Beta) -.04 .16
Training (Beta) .43" .16
R square change .16 .04

 

N=26.

118

Hypothesis 10

Hypothesis 10 states that the relationship of the technical systems and people
systems with perceived performance will be partially mediated by the integration
practices. That is, it was predicted technical and people systems will be positively related
to perceived effectiveness. Second, that the positive relationship between these
independent variables and perceived performance measures would be partially mediated
by the studies integration measures. The results of this test can be found in Table 4.14.
The results for supervisor and superintendent rating of perceived effectiveness (MGT)
was related technical systems (B = .93, p<.01). People systems composite (composite)
and lean training (training) are not positively related to perceived effectiveness at the
supervisor or superintendent level. The test for mediation was not supported.

In the case of perceived department performance (PTP) as rated by employees,
both technical systems (B = .53, p.<.10) and people systems composite (B = .45, p.<.01)
are positively related to employee ratings of effectiveness. However, the test of
mediation was not supported. Hypothesis 10 is not supported.

Hypothesis 11

Hypothesis 11 states that the relationship of the technical systems and people
systems with work-related attitudes will be partially mediated by the integration
practices. That is, it was predicted that technical systems and people systems are
positively associated with work-related attitudes. Also, that the positive relationship
between these independent variables and work-related attitudes would be partially
mediated by integration. The results are presented in Table 4.14. The results show that

technical systems are not statistically associated any of the work-related attitudes. Also,

119

lean training (training) is not statistically related to any of the work-related attitudes.
Table 4.14 shows that people systems composite is positively related to commitment to
lean strategy (CLS) (B = .45, p<.01), job satisfaction (IS) (B = .70, p<.01), learning
environment (LE) (B = .77, p<.01), and team efﬁcacy (TE) (B = .38, p<.05). None of the
tests of mediation were supported. That is, people systems composite explained all of the
variance in work-related attitudes. Hypothesis 11 is not supported.

While none of the integration hypotheses were supported in this study, a further
discussion of hypothesis 8 coupled with the integration hypotheses, and a discussion of
the measurement issues related to integration may in part explain these results.

The key measurement constraint in this aspect of this study was that the
assessment of integration was folded into the larger technical assessment instrument. The
speciﬁc items measured by the integration items (i.e., items 3, 10, 11, and 16 in Appendix
B), where signiﬁcantly different from the technical systems in terms of face validity. In
fact, these survey items were created by lean experts and a review of the integration items
compared with the technical systems assessment items reveals a clear focus on changed
behaviors by employees. However, all of these items on the assessment instrument was
assessed solely by the superintendent level. A total of 12 superintendents completed all
of the technical assessment, which included the integration assessment instrument. As a
result, the variance between departments and between the technical systems and
integration items may have been signiﬁcantly reduced. This is supported by the high
correlation between the technical and integration items on the survey instrument (r = .87).

(See Appendix F.) While alternative methods to collect integration data were discussed

120

and recommended by the researcher, the approached used in this study was the only
option available.

As found in hypothesis 8, the signiﬁcant negative interaction of learning
environment and technical systems and lean training appears to be counter intuitive, yet,
consistent with the concept of integration proposed in this study. The key components of
integration as deﬁned in this study include: 1) A suggestion system in which team
members use and understand, a suggestion system that documents activities,
improvements are tracked, and suggestion data is a key measure for the team, department
and plant; 2) Teams use speciﬁc lean tools for continues improvement, teams determine
the best work methods and adjust these methods to match takt time; 3) Disciplined
problem solving is in place and is consistently followed, these problem solving activities
have become a change management process achieving a continuous incremental
improvement process, and problem solving processes are folded into future product and
processes; and, 4) Operational availability concepts are understood and accurate, data
collection and analysis are tied directly to operational availability, and this data and
analysis drives continuous improvement efforts.

The negative interaction of learning environment and technical systems and lean
training would appear consistent with this ﬁnding. If department employees are provided
the technical systems and trained in lean production without the opportunity to use the
lean technical systems and their capabilities through training, the integration activities
described above will likely result in a decrease in a learning environment, which is
consistent with this ﬁnding. The measures for perceived learning environment are based

on speciﬁc behaviors and actions. For example, people in my department are encouraged

121

to continuously improve. And, successful people at my company continually try new
things. The implementation of the technical elements of lean production coupled with
lean training without the opportunity to actively participate in improvements (i.e., as
contained within the integration variable) will result in lower ratings of the perceived
learning environment as assessed by employees, which is consistent will this ﬁnding.

In addition, this position is supported further by the ﬁndings in hypothesis 9.
Integration was the only independent variable to be positively related to department
performance (i.e., suggestion rate and uptime). As stated earlier, the measures provided
for department performance and the fact that only one of the two sites in this study was
able to provide performance data at the department and shift level substantially reduced
the opportunity ﬁnd signiﬁcant data. Yet, integration still was signiﬁcantly related
department performance.

In short, these ﬁndings suggest that integration is dependent upon the technical
systems and lean training as a fundamental foundation for integration to occur. People
systems create the climate that allows employees at the department level to have a direct

impact on department performance.

122

Table 4.14

Regression Results of the Test of Integration as a Mediator for Technical and
People Systems Effect on Perceived Effectiveness and Work-Related Attitudes

 

 

 

Dependent Variables
Independent Perceived Work-Related Attitudes
mugs W
MGT PTP CLS JS LE TE

Equation 1
Step 1: Control

Team Size (Beta) -.12 .03 .02 .06 .00 .11
R square .07* .10** .10** .10** .19*** .09**
Step 2: Main effect
Tech. Sys. (Beta) .93*** .53* .24 -.11 .15 -.04
Training (Beta) .11 -.18 -.20 -.05 -.07 .23
Composite (Beta) -.12 .45*** .45*** .70*** .77*** .38"
quuare change .65*** .25*** .18*** .46*** .45*** .17**
Step 3: Mediator
Integration (Beta) -.09 -.57** .03 -.08 .07 .00
R square change .00 .06** .00 .00 .00 .00
Equation 2
Stg) 1: Control
Team Size (Beta) -. 12 .03 .02 .06 .00 .1 1
quuare .07* .10** .10** .10** .19*** .09**
Step 2 : Mediator -.09
Integration (Beta) -.57** .03 -.08 07 .00

.47**

R square change .08** .00 .11** .00 .00
Step 3: Main effect

Tech. Sys. (Beta) .93*** .53*** .24 -.11 .15 -.04
Training (Beta) .1 1 -. 18 -.20 -.05 —.07 .22
Composite (Beta) -.12 .45*** .45*** .70*** .77*** .38"
R square change .19*** .23*** .18** .35*** .45*** .17"

 

Summary of Hypotheses

Table 4.15 summarizes the signiﬁcant relationships between technical systems
and people systems as well as signiﬁcant relationships of integration as a mediating
variable and the cross-products or interaction variables with perceived performance,
archival data, and work-related attitudes. It was assessed whether technical systems and
people systems predicted department performance and work-related attitudes. It was also
assessed whether the congruence between the technical systems and pe0ple systems in
lean production moderates the effects of these systems resulting in improved department
performance and work-related attitudes. Lastly, this study examined whether technical
systems and people systems are mediated by integration in its effect on department
performance and work-related attitudes.

The main ﬁndings of this research is that there is a positive and signiﬁcant
relationship between technical systems and perceived work unit effectiveness as assessed
by supervisors and superintendents. The ﬁndings support that people systems lean
training is positive and signiﬁcantly associated with perceived performance as rated by
supervisors and superintendents and that people systems composite is positive and
signiﬁcantly related to perceived performance as assessed by employees. There is also a
positive and signiﬁcant relationship between people systems composite and each of the
work-related attitudes (commitment to lean strategy, job satisfaction, learning
environment, and team efﬁcacy).

A positive and signiﬁcant interaction effect was found for technical systems and
people systems composite with job satisfaction. Also, a positive and signiﬁcant

interaction effect was found for technical systems and people systems lean training with

124

team efﬁcacy. These ﬁndings support the proposition that the consonance between the
technical systems and people systems in lean production is positively and signiﬁcantly
related to work-related attitudes.

Support was not found for the proposition that integration mediates the
relationship between technical systems and people systems. Integration accounted for a
signiﬁcant increase in explained variance in terms of suggestion participation rate and
uptime performance at the department level. However, the variance explained by
integration was a direct effect on the dependent variables and no integration effect was
found.

Table 4.15 will now be presented. It summarizes the ﬁndings shown in this

 

 

chapter.
Table 4.15
Comparison of Hypothesized Versus Actual Research Findings
Variable Hypothesized Actual
Hypothesis 1: Technical systems, department + 0
performance

Hypothesis 2: Technical systems, perceived
department performance
Rated by supervisors and superintendents + 4-

Rated by department employees 4. o

Hypothesis 3: People systems, department
performance + 0

125

Hypothesis 4: People systems, perceived
department performance

Management
Lean production composite
Lean training

Employee
Lean production composite
Lean training

Hypothesis 5: People systems, work-related
attitudes

People Systems Composite

Commitment to lean strategy
Job Satisfaction

Learning environment
Team efﬁcacy
Lean Training
Commitment to lean strategy

Job Satisfaction

Learning environment

Team efﬁcacy

126

Hypothesis 6: People systems moderate technical
systems, department performance

Technical systems x People systems
composite

Technical systems x Lean training

Hypothesis 7: People systems moderate technical
systems, perceived performance

Technical systems x People systems composite
Management
Employee

Technical systems x Lean training

Management
Employee

Hypothesis 8: People systems moderate technical
systems, work-related attitudes

Technical systems x People systems composite

Commitment to lean strategy
Job Satisfaction

Learning environment
Team efﬁcacy
Technical systems x Lean training

Commitment to lean strategy
Job Satisfaction

Learning environment

Team efﬁcacy

127

Hypothesis 9: Integration mediates technical and
people systems, department performance

Technical systems x people systems + 0
composite
Technical systems x lean training + 0

Hypothesis 10: Integration mediates technical and
people systems, perceived performance

Management . + 0
Employee 4. o

Hypothesis ll: Integration mediates technical and
people systems, work-related attitudes

Commitment to lean strategy

+ 0
Job Satisfaction
. . + 0
Learning envrronment
+ 0
Team efﬁcacy
+ 0

 

Where:

- represents a signiﬁcant inverse relationship;

+ represents a signiﬁcant positive relationship;

0 represents no signiﬁcant relationship.
Conclusion

Although many hypotheses were fully or partially supported, several were not.
While the most plausible explanation is that the archival performance data variable was
inadequate in scope and variance due to archival data made available by the Midwest
plant and the inability by the Southeast plant to supply any useful archival data, a more

thorough discussion of these results is warranted. The discussion of the ﬁndings and

their implications will be the focus of chapter ﬁve.

128

CHAPTER FIVE
DISCUSSION
This chapter includes a discussion of the results of the dissertation research. This
discussion contains an overall summary of the ﬁndings presented in Chapter Four
compared to the problem as stated in Chapter One. The implications for research and
theory, the implications for practice, and suggestions for future research are also

presented.

Summary

Manufacturers around the world continue to expend immense effort to implement
lean production. However, despite efforts by many manufacturers to implement lean
production, few have successfully imitated the Toyota Production System (TPS) (Spear,
1999). The implications in implementing lean production are dramatic for these
manufacturers in both short term and long term consequences. Those ﬁrms that fail to
adequately implement lean production will face signiﬁcant performance gaps with those
manufacturers that achieve a fully integrated implementation of lean production.

Many of these efforts as described by practitioners and the academic community
have focused on the technical aspects of lean production. In fact, many organizations
have begun to make the shift from mass production to a technically centered approach of
lean production (see Figure 2.1). Yet, a key challenge facing manufacturers is to
implement people systems consistent with the technical aspects of lean production and to
integrate these systems in ways that encourage problem solving at every level of the
organization. While the technical aspects of lean production have been extensively

studied, few empirical studies have directly studied the people aspects of lean production.

129

The purpose of this study was to increase understanding of the people elements
that foster and support the technical systems in the diffusion of lean production. This
study examined the relationship between these elements of lean production as well as the
integration of these elements in the implementation of lean production. Also, this study
investigated people systems as a moderator of the relationship between technical systems
with department performance and work-related attitudes.

Speciﬁcally, this study sought to provide empirical evidence that investing in the
people aspects of lean production has a positive impact on performance and work-related
attitudes beyond the technical systems of lean production. This study examined the
relationship between the technical and people systems of lean production as well as the
integration of these elements. This study identiﬁed key characteristics of lean production
and linked these characteristics with effectiveness data and work-related attitudes at the
department level. In this study, a set of hypotheses was presented about the relationships
between technical systems, people systems, their interaction, and integration as a
mediator of these systems as determinants of department performance and work-related
attitudes.

The key ﬁndings of this study are as follows:

1. The correlation analysis and the factor analysis of the people systems indicate that
key people aspects of lean production at the department level can be identiﬁed and
measured. Then, I found that these measures of people systems can be analyzed in
terms of department performance and work-related attitudes. Moreover, the research
indicated that the people systems of lean production can be used to provide a more

thorough understanding of lean production as well as differentiate between

130

4.

departments that have adopted a technology centered approach versus an integrated
approach in implemented lean production.

The technical and people systems of lean production were not a signiﬁcant predictor
of department performance. As stated in Chapter Four, this may be a reﬂection of the
reduced opportunity for variance in the archival data provided by the plant located in
the Midwest and the inability of the plant in the Southeast to provide useful data at
the department level.

Consistent with the hypothesis. 2, technical systems of lean production was a
signiﬁcant predictor of perceived department performance as assessed by
management. However, technical systems were not a signiﬁcant predictor of
department performance as evaluated by department level employees. This suggests
that management and employees at the department level use different factors in
assessing perceived performance. This difference in perceptions may also reﬂect the
challenge of brownﬁeld transformation to lean production. Speciﬁcally, the
unfreezing or unleaming of old routines may occur differently for managers and
workers.

The people systems composite measure was a signiﬁcant predictor of perceived
performance as assessed by department level employees (hypothesis 4). People
systems lean training was not a signiﬁcant predictor. The reverse was found for
management assessment of performance. That is, people systems lean training was a
signiﬁcant predictor and people systems composite was not signiﬁcant as rated by
management. This suggests that department employees associated people systems

(i.e., supervisory behavior, management support, etc.) as a composite with improved

131

7.

perceived department performance. In contrast, management associated lean training
efforts with improved department performance. These ﬁndings reinforce difference in
perceptions by management level and department level employees in the brownﬁeld
conversion to lean production.

Consistent with hypothesis 4, the people systems composite measure was a signiﬁcant
predictor of work-related attitudes at the department level. This suggests that the
investment in people systems have a signiﬁcant and positive impact on each of the
work-related attitudes, which include commitment to lean strategy, job satisfaction,
learning environment, and team efﬁcacy, at the department level.

Consistent with the “consonance hypothesis,” people systems was a moderator of
technical systems and work-related attitudes (i.e., job satisfaction and team efﬁcacy).
In other words, these ﬁndings provide support for the proposition that organizations
will perform effectively when their structures (i.e., people systems) are compatible
with the requirements and dictates of the technical system.

While integration as a mediator of technical systems and people systems was not
supported in this study, the operationalization of integration at the shop ﬂoor level
provides support for two previous studies of integration in a lean production context
(Dean, 1991; MacDufﬁe, 1992). This study provides support for Dean and Snell
(1991) by deﬁning integration activities at a fourth level. In addition, this study
extends the MacDufﬁe and Krafcik (1992) ﬁndings by providing theoretical support
for their argument that problem solving at the shop ﬂoor level is where integration
occurs. Also, while the integration variable did not demonstrate a mediation effect, it

was the sole independent variable to predict department performance. This ﬁnding

132

reinforces the importance of the integration variable as a predictor of department

performance.

Implications for Research and Theory

Chapter two described many reasons why people systems and the integration of
people and technical systems are important for organizations adopting lean production,
and offered two theoretical perspectives as a foundation for this study. The results of this
study suggest that people systems predict work-related attitudes and also inﬂuence
perceptions of department performance by employees. Speciﬁcally, the people systems
variable was signiﬁcantly related to commitment to lean strategy, job satisfaction,
learning environment, and team efﬁcacy. In support of the STS perspective, these
ﬁndings support the proposition that work organizations are composed of two
interdependent systems. That both the technical (i.e., technical systems) and social
factors (i.e., people systems) must be optimized to obtain high organizational
performance.

Technical systems were strongly related to management perception of department
performance. The people systems composite was found signiﬁcantly related to employee
perceptions of department performance, but not people systems lean training. In contrast,
the reverse relationship was shown for management perception of department
performance. These ﬁndings suggest strong differences in perceptions between managers
and employees in the transformation to lean production. These ﬁndings suggest that
manager’s view the transformation to lean production as largely a technological
phenomenon supported by changes in employees reinforced through lean training. In

contrast, department employees associate the transformation to lean production as a

133

 

people systems shift demonstrated by changes in behaviors of managers as well as others.
In short, the ﬁndings that people systems inﬂuence perceptions of performance and work-
related attitudes are consistent with the sociotechnical systems (STS) and high
performance work practices (HPWP) literatures.

People systems composite was found to moderate the relationship between
technical systems and work-related attitudes (i.e., job satisfaction). And, people systems
lean training moderated the relationship between technical systems and work-related
attitudes (i.e., team efﬁcacy). As such these key ﬁndings provide empirical support for
the “consonance hypothesis,” a key aspect of the STS theory from early in its formation.
That is, these: ﬁndings provide support for the proposition that organizations will perform
effectively when their structures are compatible with the requirements and dictates of the
technical system. Consistent with Rice (1953), Trist (1951), and Walton (1972) and in
contrast with Mohr (1971), people systems moderated the relationship between the
technical systems and job satisfaction and team efﬁcacy. While Mohr did not ﬁnd
support for the “consonance hypothesis,” the author included only supervisory style as a
single measure of social structure. In contrast, this study provided several measures of
people systems. Mohr’s limited measures of social structure might explain his results.

This study also provides theoretical support for the Dean and Snell (1991) study
in which the authors argued that a core concept in integrated manufacturing (i.e., lean
production) is the elimination of barriers between different facets of a manufacturing
operation. This study provides support for this paradigm that “integration mechanisms”
suggested by Snell and Dean converge at the shop ﬂoor. The authors provided three

integration mechanisms, which included integrating functional departments, integrating

134

the stages of production, and by integrating manufacturing goals across the organization.
This study provided evidence for a fourth “integration mechanism,” which is the
integration of the technical systems and people systems at the level of the shop ﬂoor.

In addition, this study extends the MacDufﬁe and Krafcik (1992) ﬁndings by
providing theoretical support for their argument by suggesting that the interaction of
people systems and technical systems at the shop ﬂoor level is where integration occurs.
MacDufﬁe and Krafcik describe integration as “incremental problem solving” at the shop
ﬂoor level and provide data at the ﬁrm level. This study extends MacDufﬁe and Krafcik
by identifying key activities that are related to the lean production system that are driven
by “incremental problem solving” by department level employees. That is, this study
provides support for integration at the level of the department where incremental problem
solving occurs in manufacturing facilities and as such closes the gap between the
rationale provide by the authors and the level of their analysis.

MacDufﬁe and Krafcik argued that that the “organizational logic” of lean
production is premised on reduced buffers, which provide a direct effect (e. g., reducing
the carrying cost of inventories) and a more signiﬁcant indirect effect of providing
valuable information for focused problem solving. That is, the system creates a creative
tension between the need to run at reduced inventories and the need for people to solve
problems. The authors continue their argument by suggesting that only when human
resources put in place the necessary skills and commitment in the work force can
incremental improvements be fully harnessed. This study provides the logic and some
empirical evidence that the integration of the technical and people systems occurs where

value is added to the product through integration activities at the department level.

135

In additional this study provides speciﬁc integration activities as the shop ﬂoor
level. The key components of integration identiﬁed is in this study include: 1) A
suggestion system in which team members use and understand, a suggestion system that
documents activities, improvements are tracked, and suggestion data is a key measure for
the team, department and plant; 2) Teams use speciﬁc lean tools for continues
improvement, teams determine the best work methods and adjust these methods to match
takt time; 3) Disciplined problem solving is in place and is consistently followed, these
problem solving activities have become a change management process achieving a
continuous incremental improvement process, and problem solving processes are folded
into future product and processes; and, 4) Operational availability concepts are
understood and accurate, data collection and analysis are tied directly to operational
availability, and this data and analysis drives continuous improvement efforts.

While this study did not ﬁnd a mediating effect of integration as proposed, a
direct effect was discovered in the empirical ﬁndings. In fact, integration was the only
variable found to directly impact the performance data at the department level. This

ﬁnding argues for further study of integration as proposed in this research.

Implications for Practice

This dissertation provided support for the idea that people systems and technical
systems must be integrated to fully capture the full potential of lean production. There
are three key implications for practitioners that can be drawn from these ﬁndings.

The model for this research entitled A Framework for a Comparative Analysis of
Alternative Approaches to the Diffusion of Lean Production in Brownﬁeld Sites (see

Figure 2.1) provides a conceptual framework to more fully understand factors inﬂuencing

136

the successful diffusion of lean production. For many practitioners the transition to lean
production is solely a technical systems transformation. The interviews associated with
this research suggest that many practitioners do not consider people systems in the
diffusion of lean production. However, those practitioners who have worked in a full
lean production system - that is a fully integrated model - accept the technical aspects of
lean production as a necessary fundamental ingredient, but focus most of their attention
on the people aspects of lean production. As such, this model will assist practitioners by
augmenting their current understanding of lean production.

This dissertation provided support for the proposition that people systems must be
addressed in the implementation of lean production. Work-related attitudes are shaped
by the people systems of lean production. People systems also shape employee
perceptions of department performance. This study provides an assessment instrument
for practitioners to assess the current state of their organizations and measure progress
over time as these organizations convert to lean production as well as assess whether
these organizations are maintaining people systems consistent with lean production.

This study also provides support for the proposition that integration activities at
the shop ﬂoor positively inﬂuence department performance. In other words,
implementing the technical systems and people systems is not sufﬁcient in the diffusion
of lean production. Practitioners need to consider integration activities at the department
level that will encourage and support practices that fully capture the full potential of the
technical and people systems of lean production. This study provides some ideas on

these integration activities for manufacturers.

137

Suggestions for Future Research

An important assumption in the study of the brownﬁeld conversion of mass
production plants into lean production facilities is that change occurs person-by-person,
department-by-department, and plant-by-plant. A key suggestion for future research is to
conduct construct validation of the people systems assessment instrument. This study
suggests that people systems at the individual and department can be measured. A key
task for future research is to solidify the key construct that should be considered in
measuring people systems in a lean production context.

A second and related item for future research is to compare and contrast mass and
lean production facilities using these constructs of people systems. That is, many of the
current industrial and organizational constructs were developed in a mass production
paradigm. A question for future researchers is whether the current constructs retain their
validity in a lean production context.

Another suggestion for future research is to expand the research design across
industrial sectors. While this research was conducted in the automotive supplier industry,
it included only two facilities within one large corporation. This study could be revised
and expanded to include a much larger number and different types of automotive
suppliers. Also, this study could be expanded to include other industrial sectors, such as,
the steel industry, electronic, and apparel industry. Clearly the technical assessment
instrument would need to be adjusted to ﬁt the industrial sector, yet these industries as
well as others, are under pressure to become lean producers. For example, the apparel
industry from a technical process perspective is driven more by U-shaped cells than the

andon systems used in automotive assembly operations. However, each of these

138

 

industries could be studied in terms of the integration of the people and technical systems
of lean production.

Further research will need to address the performance data weaknesses in this
study. Two immediate steps could be taken to address this weakness. First, the
researcher could develop speciﬁc measures related to cost, quality, delivery, suggestions,
and productivity independent of any organization’s current practices. Second, these data
could be tracked over time to address the inherent limitation of a cross-sectional study.
However, any such changes must be approved by the participating organizations.

This study provided an important foundation block in terms of understanding
human resources and psychological aspects of shifting from mass to lean production
environment. It showed that the identiﬁcation and measurement of the people aspects of
lean production is possible. That these measures of people systems are useful in
providing increased understanding in terms of the transformation to lean production.
This study provided “additional support for the proposition that the technical and social
systems of lean production needs to be in harmony (i.e., the “consonance hypothesis) to
optimize organizational performance. This research identiﬁed speciﬁc “incremental
problem solving” activities at the department level that integrate the technical and people
systems of lean production and that these activities directly impact department

performance.

139

List of References

Dean, J. W., and Snell, Scott A. (1991). Integrated manufacturing and job design:
moderating effects of organizational inertia. Academy of Management Journal,

34(4), 776-804.

MacDufﬁe, J. P., and John F. Krafcik,. (1992). Integrating technology and human
resources for high-performance manufacturing: Evidence from the world auto
industry. In a. M. U. Thomas A. Kochan (Ed.), Transforming Organizations .
New York: Oxford Press.

Mohr, L. B. (1971). Organizational technology and organizational structure.
Administrative Science Quarterly, 16, 444-459.

Rice, A. K. (1953). Productivity and social organization in an Indian weaving shed: An
examination of the socio-technical system of an experimental loomshed. Human
Relations, 6, 297-329.

Spear, S., and Bowen, H. Kent. (1999). Decoding the DNA of the Toyota Production
System. Harvard Business Review, 77(5), 96-106.

Trist, E. L., and Bamforth, K. W. (1951). Some social and psychological consequences of
the long wall method of coal-getting. Human Relations, 4, 3-38.

Walton, R. E. (1972). How to counter alienation in the plant. Harvard Business Review,
50(6), 70—81.

140

APPENDICES

141

APPENDIX A

PERCEPTIONS REGARDING THE IMPLEMENTATION OF
LEAN PRODUCTION

QUESTIONNAIRE

142

PERCEPTIONS REGARDING THE IMPLENIENTATION OF THE
LEAN PRODUCTION

QUESTIONNAIRE
INTRODUCTION

The purpose of this questionnaire is to collect input from employees regarding the effects
of a number of changes in the automotive industry. These changes have come about
through numerous efforts by management and union to improve the competitive position
of your plant and company. Through this questionnaire, we hope to learn more about
your opinions regarding these changes. Please answer all of the questions.

Participation in this survey is entirely voluntar_'y. You may discontinue
participation in the survey at any time. You indicate your voluntary agreement to
participate by completing and returning this questionnaire. All responses to the
questionnaires will be kept strictly confidential. Further, you should not identify
yourself on the questionnaire. No one in the Plant will see the completed surveys.
Only group statistics and aggregate results will be disclosed as feedback to your
plant and all interested employees. The questionnaires will be processed by the

researcher alone. Moreover, the questionnaires will be destroyed once the analysis
is complete.

The survey data is being collected and analyzed at the School of Labor and Industrial
Relations, Michigan State University. Michigan State University will not permit any
responses to be traced back to any individuals or allow individuals to be identiﬁed in any
other way.

Please take the time to complete the survey. It should take approximately 20-25
minutes to read and complete the survey.

If you have any questions, please call the researcher Bill Mothersell at (517) 432-
0188.

Thank you for your participation.

143

Organizational Survey

Department Number: Work Group/Cell:

 

Please answer each of the following items by circling the appropriate response.

Please answer the extent to which you agree or disagree with each of the

statements listed below using the following scale:

1 Strongly Disagree SD
2 Disagree D
3 Neutral N
4 Agree A

' 5 Strongly Agree SA

If an item does not apply to your job please leave it blank.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Supervisory Behaviors: SD D N A SA

1. My advisor/supervisor encourages better relations with other 1 2 3 4 5
natural work groups/departments.

2. My advisor/supervisor supports the implementation of the 1 2 3 4 5
Lean Production.

3. My advisor/supervisor is willing to adapt his/her supervisory l 2 3 4 5
style in response to changes resulting from the move to the
Lean Production.

4. My advisor/supervisor communicates his/her expectations 1 2 3 4 5
clearly and effectively.

5. My advisor/supervisor provides timely and beneﬁcial 1 2 3 4 5
performance feedback.

6. My advisor/supervisor encourages continuous improvement 1 2 3 4 5
and innovation.

7. My advisor/supervisor encourages ideas and suggestions. 1 2 3 4 5

8. My advisor/supervisor encourges teamwork. 1 2 3 4 5

9. My advisor/supervisor goes directly to the work area to l 2 3 4 5
observe and discuss problems.

10. My advisor/supervisor encourages subordinates to participate 1 2 3 4 5
in important decisions.

11. My advisor/supervisor encourages people to speak up when l 2 3 4 5
they disagree with a decision.

12. My advisor/supervisor encourages people to try different 1 2 3 4 5
approaches to solve problems.

13. My advisor/supervisor believes that people can learn from 1 2 3 4 5
their mistakes.

14. My advisor/supervisor views problems and work challenges 1 2 3 4 5

as opportunities to develop peoples’ skills.

 

 

144

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Management Support: SD D N A SA

15. Management displays a commitment to Lean Production. 1 2 3 4 5

16. Management establishes policies and procedures that are 1 2 3 4 5
consistent with Lean Production.

17. Management is responsive to my suggestions and concerns 1 2 3 4 5
regarding the implementation of Lean Production.

18. Management “walks-the-talk” when implementing Lean l 2 3 4 5
Production.

19. Management demonstrates relentless effort to continuously l 2 3 4 5
improve.

20. Management allows decisions to be made at the lowest 1 2 3 4 5
appropriate level.

21. Management listens to ideas employees have for making 1 2 3 4 5
improvements.

22. I get the information I need from management. 1 2 3 4 5

Cooperative Union Management Relations: SD D N A SA

23. Union objectives and goals are consistent with the objectives 1 2 4 5
and goals of Lean Production in my natural work
group/department.

24. The collective bargaining agreement is structured to 1 2 3 4 5
facilitate the transition to the Lean Production in my natural
work group/department.

25. Union representatives have had input in the design, 1 2 3 4 5
monitoring, and evaluation of Production in my natural work
group/department.

26. Union representatives should have input in the design, 1 2 3 4 5
monitoring, and evaluation of Lean Production in my natural
work group/department.

27. Adversarial relations between union and management 1 2 3 4 5
prevent my natural work group/department from solving
problems.

28. In my natural work group/department there is little status 1 2 3 4 5
differential between union and management.

29. In my natural work group/department union and l 2 3 4 5
management representatives work together cooperatively to
solve problems.

Commitment to the Lean Production Strategy: SD D N A SA

30. I am committed to the ideas of the Lean Production. 1 2 3 4 5

31. I think we should increase the emphasis placed on the Lean l 2 3 4 5

Production.

 

 

145

 

 

Commitment to the Lean Production Strategy (continued):

SDDN ASA

 

32. There is no way that my department can make Lean
Production effective.

12345

 

 

 

 

 

33. I understand the key elements of Lean Production. 1 2 3 4 5
34. Lean Production is just a move by management to cut costs. 1 2 3 4 5
Job Satisfaction: SD D N A SA
35. Generally speaking, I am very satisﬁed with this job. 1 2 3 4 5
36. I am generally satisﬁed with the kind of work 1 do in this 1 2 3 4 5

job.

 

37. In general the implementation of Lean Production in my
natural Work group/department has resulted in increased job
satisfaction.

 

38. I am satisﬁed with the physical working conditions in my
natural work group/department.

 

39. I am satisﬁed with the recognition my natural work
group/department receives for good work.

 

Perceived Learning Environment:

 

40. People in my natural work group/department are encouraged
to continuously improve.

 

41. People in my natural work group/department are provided
with the opportunity to learn new things.

 

42. People in my natural work group/department are encouraged
to assume difﬁcult assignments.

 

43. In my natural work group/department it is acceptable to
question others about why things are done a certain way.

 

44. The successful people at my company continually try new
things.

 

45. In my natural work group/department it is better to ignore
problems than to suggest improvements.

 

46. In my natural work group/department maintaining the status
quo is more important than learning new things.

 

47. New ideas are highly valued in my natural work
group/department.

 

48. In my natural work group/department employees are
responsible for demonstrating on the job what they have
learned in training.

 

49. In my natural work group/department I have some input into
the type of training I attend.

 

Developmental Focus:

 

50. I receive the training I need to perform my job well.

 

 

51. I receive training in teamwork skills.

 

 

146

 

 

Developmental Focus (continued):

SDDN ASA

 

 

 

 

 

52. My advisor/supervisor is good at coaching me through on- 1 2 3 4 5
the-job problems.

53. I am learning how to solve problems better. 1 2 3 4 5

54. I eg'oy work chages that require me to learn new things. 1 2 3 4 5

Perceived Team Performance: SD D N A SA

55. The implementation of the Lean Production in my natural 1 2 3 4 5

work group/department will better enable us to serve our
customers.

 

56.

The implementation of Lean Production in my natural work
group/department will allow us to make continuous
improvements.

 

57.

The implementation of Lean Production in my natural work
grow/department will pit employees against each other.

 

58.

The implementation of Lean Production in my natural work
group/department will assist us in improving quality.

 

59.

The implementation of Lean Production in my natural work
group/department will help us reduce costs.

 

60.

The implementation of Lean Production in my natural work
group/department will assist us in the elimination of waste.

 

 

 

 

 

 

 

 

 

 

 

61. The implementation of Lean Production in my natural work 1 2 3 4 5
grgp/department will help us in our overall performance.

62. The implementation of Lean Production in my natural work 1 2 3 4 5
group/department will allow us to better solve problems.

63. The drive to become a lean producer is a passing fad. 1 2 3 4 5

64. The implementation of Lean Production is the right thing to l 2 3 4 5
do for our long term success.

Managing Change: SD D N A SA

65. My advisor/supervisor seeks input from employees 1 2 3 5
regarding how to better. Lean Production activities.

66. My natural work group/department takes steps to remove l 2 3 4 5
barriers that prohibit the effective implementation of Lean
Production activities.

67. My natural work group/department takes steps to modify 1 2 3 4 5
procedures to improve the effective implementation of Lean
Production activities.

68. Our management and union have worked together to explain 1 2 3 4 5
how the change to Lean Production will beneﬁt the
individual worker

69. Our management and union have worked together to explain 1 2 3 4 5

how the change to Lean Production will improve our
competitive position.

 

 

147

 

Please circle the appropriate response for each of the statements listed below using

the following scale:
1 To very little extent = VL
2 To a little extent = L
3 To some extent = S
4 To a great extent = G
5 To a very great extent = VG

If an item does not apply to your job please leave it blank.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Teamwork: VL L G VG

70. To what extent does your natural work group/department l 2 4 5
plan together and coordinate its efforts?

71. To what extent does your natural work group/department 1 2 4 5
make good decisions and solve problems well?

72. To what extent do persons in your natural work 1 2 4 5
group/department know what their jobs are and know how to
do them well?

73. To what extent is information about important events and 1 2 4 5
situations shared within your natural work
gum/department?

74. To what extent is your natural work group/department l 2 4 5
committed to its objectives?

75. To what extent is your natural work group/department able 1 2 4 5
to respond to unusual natural work group/department
demands placed upon it?

76. To what extent do you have conﬁdence and trust in the l 2 4 5
persons in your natural work group/department?

Involvement/Psychological Participation: VL L G VG

77. To what extent can you inﬂuence what goes on in your 1 2 4 5
natural work grow/department?

78. To what extent can you inﬂuence the decisions of your 1 2 4 5
advisor/supervisor regarding things about which you are
concerned?

79. To what extent does your advisor/supervisor ask your 1 2 4 5
opinion when a problem comes up that involves your work?

80. To what extent can you make suggestions for improving 1 2 4 5

your jpb or changing the setup in some way?

 

 

148

 

 

Process Focus:

 

81.

To what extent do you understand how your job relates to
other jobs in your natural work group/department?

 

82.

To what extent do you understand how your natural work
group/department contributes to the goals of the plant?

 

83.

To what extent does your natural work group/department
continuously strive to make improvements in its key work
processes?

 

To what extent does your natural work group/department
monitor key processes to maintain high performance
standards?

 

85.

To what extent does your natural work group/department
view its work processes as critical in serving your customer
needs?

 

86.

To what extent do you understand how your natural work
group/department relates to your suppliers (e. g.,
parts/materials suppliers)?

 

87.

88.

To what extent do you understand how your natural work

, gIoup/department relates to your customers?

 

To what extent does your advisor/supervisor stress the
importance of maintaining and improving work processes?

 

Proactive Problem Solving:

 

89.

To what extent do persons in your natural work
group/department take preventive action by focusing on the
root causes of problems?

 

90.

To what extent do persons in your natural work
group/department focus on ﬁnding long-term solutions to
problems?

 

 

To what extent do persons in your natural work
group/department monitor and evaluate the effectiveness of
problem solving strategy implementation?

 

To what extent do persons in your natural work
group/department take calculated risks when engaged in
problem solving?

 

 

To what extent have persons in your natural work
group/department been provided with training in problem
solving approaches?

 

 

149

 

 

Proactive Problem Solving (continued):

VLLSGVG

 

94.

To what extent does you natural work group/department
consistently use a particular model of problem solving (e.g.,
5-Whys)?

12345

 

95.

To what extent do persons in your natural work
group/department _r_r_o_t engage in innovation for fear of
getting in trouble should the innovation fail?

 

Workplace Trust:

 

96.

To what extent do you trust that your coworkers would try to
help you out if you got into difﬁculties at work?

 

97.

To what extent can most of your coworkers be relied upon to
do as they say they will do?

 

98.

To what extent is management at your plant sincere in its
attempt to meet the workers’ point of view?

 

99.

To what extent do you feel conﬁdent that the company will
always try to treat you fairly?

 

100.

To what extent do you feel management would be quite
prepared to gain advantage by deceivipg the workers?

 

101.

To what extent can your management be trusted to make
sensible decisions regarding the future of the workers?

 

102.

To what extent are you conﬁdent that your union will always
try to treat you fairly?

 

103.

To what extent can your union be trusted to make sensible
decisions regarding the future of the workers?

 

Workplace Bonding:

 

104.

To what extent do you feel you are part of your natural work
group/department?

 

 

 

group/department help you ﬁnd ways to perform a job
better?

105. To what extent are persons in your natural work 1 2 3 4 5
group/department willing to pay attention to what you are
saying?

106. To what extent do persons in your natural work 1 2 3 4 5

 

 

107.

To what extent are you satisﬁed that in your natural work
group/department everyone’s opinion gets listened to?

 

108.

To what extent do you interact with other members of your
natural work group/department in social and recreational
activities after work?

 

\:09.

When you talk with your advisor/supervisor, to what extent
does he/she pay attention to your ideas?

 

 

150

 

 

Workplace Bridging:

VG

 

110.

To what extent does your natural work group/department
plan together and coordinate its efforts with other natural
work group/departments to get the job done?

 

111.

To what extent are you and your natural work
group/department conﬁdent that you will be provided all the
necessary resources and help to get the job done in a timely
fashion?

 

112.

To what extent is information about important events and
situations in your natural work group/department shared
with other natural work groups/departments and vice versa?

 

113.

To what extent do your internal suppliers provide the help
your natural work group/department needs?

 

114.

To what extent are you satisﬁed with your interactions with
your management at work?

 

115.

To what extent are you satisﬁed with the interactions with
your union at work?

 

116.

To what extent are you satisﬁed with the way the union and
management interacts to resolve your natural work

group’s/department’s problems in the workplace?

 

117.

To what extent is your company involved in volunteer
efforts within your community?

 

118.

How satisﬁed are you with your company’s efforts to help
save ancipresewe the environment in your community?

 

119.

How satisﬁed are you with your union’s efforts in helping
your community?

 

 

l 20.

How satisﬁed are you with your plant’s overall relationship
with your community?

 

 

151

 

When conﬂict occurs between you and your advisor/supervisor, to what extent
does your advisor/supervisor use each of the following behaviors to resolve
conflict? Remember you are rating how your advisor/supervisor behaves when
resolving conﬂict, not how you behave or what you think is desirable.

Describes behavior which never occurs = 1
Describes untypical behavior which seldom occurs = 2
Describes behavior which sometimes occurs = 3
Describes behavior which occurs frequently_= 4
Describes very typical behavior which usually occurs = 5

 

 

 

 

 

 

 

 

 

 

 

 

Conﬂict Resolution Climate (Forcing/Fostering): My

advisor/supervisor. . .

121. Brings the problem clearly into the open and carries it out to 1 2 3 4 5
resolution.

122. Forces acceptance of his/her point of view. 1 2 3 4 5

123. Emphasizes common interests. 1 2 3 4 5

124. Stresses that our differences are less important than our 2 3 4 5
common goals.

125. Demands to get his/her way. 1 2 3 4 5

126. Will not take no for an answer. 1 2 3 4 5

127. Irnposes his/her solution. 1 2 3 4 5

128. Acts as though our common goals are of prime importance. 1 2 3 4 5

129. Takes both sides of the issue into account. 1 2 3 4 5

 

Please answer the extent to which you are not at all confident to completely
conﬁdent using the following scale:

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Not at all Completely
conﬁdent gym
1 2 3 4 5 6 7
Team Efﬁcacy: How conﬁdent are you that your natural work
goup/department
130. Can solve performance problems? 1 2 3 4 5 6 7
131. Can come up with better ways to do its job? 1 2 3 4 5 6 7
132. Can assess what it does right and wrong? 1 2 3 4 5 6 7
133. Will work hard to meet its responsibilities? 1 2 3 4 5 6 7
134. Can organize itself to maximize its resources? 1 2 3 4 5 6 7
135. Can pull itself out of a slump? 1 2 3 4 5 6 7
136. Can do its job without wasting time? 1 2 3 4 5 6 7
137. Will jump in to solve problems? 1 2 3 4 5 6 7
138. Will go above and beyond its responsibilities? 1 2 3 4 5 6 7
139.15 committed to high levels of production? 1 2 3 4 5 6 7
140. Is committed to the implementation of Lean Production? 1 2 3 4 5 6 7

 

 

152

 

Demographic Information
Please circle the appropriate response to each of the following items.

Shift:
Day’s/First Shift/A Shift 1
Aftemoon’s/Second Shift/B Shift 2
Midnight’s/Third Shift/C Shift 3

Gender:
0 Male
0 Female 2

y—e

Years of Service at Plant 6:
0 — 2 years

3 -5 years

6 - 10 years

11 - 20 years

21 —- 30 years
More than 30 years

QMAMNt—t

Your age:

18 years - 25years
26 years — 30 years
31 years — 35 years
36 years - 40 years
41 years - 45 years
46 years - 50 years
51 years — 55 years
Over 55 years

OO\IO\LII-§UJNr-‘

Race:
African American
Asian
Caucasian
Hispanic
Native American
0 Other
Employment Status:
0 Hourly Employee
0 Salaried Employee 2

O\Lll-l>nUJN'—t

t—I

Do you supervise people?
0 Yes 1
e No 2

153

Work Area/Function:

0 Assembly ............................................................. l
0 Components (feeder groups) ...................................... 2
0 Quality Assurance ................................................... 3
0 Support (Skilled Trades, Janitor, Tool Crib, etc.) ............... 4
0 Production Control & Logistics (PC&L)/Materials Management 5
0 Engineering .............................................................. 6
0 Appointed or Elected Union Ofﬁcial ............................... 7
o All Others ............................................................. 8

Have you received any training in the Lean Production?

Yes 1
No 2

If you have received training, what was the training you received? Circle as many
as apply.

 

 

 

 

5S 1
Seven Forrns of Waste 2
Introduction to Lean Production 3
People Focused Practices (PFP) 4
Factory Simulation (One Piece Flow,

Level Scheduling & Pull System) 5
Team Building 6
Problem Solving 7
Other Related Training (please identify):

.1:
3.
4.
Additional Comments:

 

 

 

Thank You For Your Participation

154

APPENDIX B

IMPLEMENTATION OF LEAN PRODUCTION

ASSESSMENT INSTRUMENT

155

IMPLENIENTATION OF LEAN PRODUCTION

These questions relate to efforts to implement the key elements of lean production at the
department level. This assessment instrument is an abbreviated Lean Production Gap
Assessment document and has had been modiﬁed to focus on lean implementation
activities at the department level. Please identify the department number for which the
following information is being collected.

Department Number

 

To answer the following questions, please identify the level at which each department has
progressed in implementing lean production. Please answer for just the criteria measure.
If a particular question is not applicable for a speciﬁc department, please mark as N/A.
Please answer all of the questions.

This assessment instrument is being collected and analyzed at the School of Labor and
Industrial Relations, Michigan State University. This assessment instrument is part of
research project focusing on the diffusion of lean production within manufacturing
organizations. Participation in this assessment instrument is entirely voluntary. You
may discontinue participation at any time. You indicate your voluntary agreement
to participate by completing and returning this instrument.

Please take the time to complete the assessment instrument. It should take approximately
10—15 minutes to read and complete the assessment instrument for each department.

If ou havean uesti ns lease call the researcher Bill Moth rsell at 517 43 -018 .

Thank you for your participation.

156

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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170

APPENDIX C

PERCEPTIONS REGARDING THE EFFECTS OF THE

INIPLEMENTATION OF LEAN PRODUCTION

Assessment Instrument

171

PERCEPTIONS REGARDING THE EFFECTS OF THE
IMPLEMENTATION OF LEAN PRODUCTION

QUESTIONNAIRE
INTRODUCTION

The purpose of this questionnaire is to collect input from advisors/supervisors and
superintendents regarding the effects of the implementation of lean production. Through
this questionnaire, we hope to learn more about your opinions regarding these changes
and its impact on department performance. Please answer all of the questions.

Participation in this survey is entirely voluntary. You may discontinue
participation in the survey at any time. You indicate your voluntary agreement to
participate by completing and returning this questionnaire. All responses to the
questionnaires will be kept strictly confidential. Further, you should not identify
yourself on the questionnaire. No one in the Plant will see the completed surveys.
Only group statistics and aggregate results will be disclosed as feedback to your
plant and all interested employees. The questionnaires will be processed by the

researcher alone. Moreover, the questionnaires will be destroyed once the analysis
is complete.

The survey data is being collected and analyzed at the School of Labor and Industrial
Relations, Michigan State University. Michigan State University will not permit any
responses to be traced back to any individuals or allow individuals to be identiﬁed in any
other way.

Please take the time to complete the survey. It should take approximately 5-10
minutes to read and complete the survey.

If you have any questions, please call the researcher Bill Mothersell at (517) 432-
0188.

Thank you for your participation.

172

Department Number:

 

Please answer each of the following items by circling the appropriate response.

Please answer how the implementation of lean production in your department has
improved department effectiveness using the following scale:

To a very To a very
little extent great extent
1 2 3 4 5 6 7

 

If an item does not apply to your department please leave it blank.

1. The implementation of Lean Production in my department has improved our
effectiveness in serving our customers.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

2. The implementation of Lean Production in my department has enhanced our
effectiveness in making continuous improvements.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

3. The implementation of Lean Production in my department has enhanced our
effectiveness in improving quality.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

4. The implementation of Lean Production in my department has improved our
effectiveness in reducing costs.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

173

5. The implementation of Lean Production in my department has improved our
effectiveness in the elimination of waste.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

6. The implementation of Lean Production in my department has enhanced our
effectiveness in improving safety.

To a very To a very
little extent ' great extent
~ 1 2 3 4 5 6 7

7. The implementation of Lean Production in my department has enhanced our
effectiveness in improving ergonomics.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

8. The implementation of Lean Production in my department has improved our
effectiveness in generating suggestions.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

9. The implementation of Lean Production in my department has improved our
effectiveness in implementing suggestions.

To a very To a very
little extent great extent
1 2 3 4 S 6 7

10. The implementation of Lean Production in my department has assisted us in
improving our overall performance.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

174

11. The implementation of Lean Production in my department has improved our
effectiveness in solving problems.

To a very To a very
little extent great extent
1 2 3 4 5 6 7

12. The implementation of Lean Production has resulted in our department becoming
more integrated and cohesive.

To a very To a very
little extent great extent
- l 2 3 4 5 6 7

Thank you for your participation.

175

APPENDIX D

INTERVIEW PROTOCAL

176

INTERVIEW PROTOCOL

Plant:

 

Name:

 

Title:

 

Date:

 

I am interested in ﬁnding out what lean production elements have. been
implemented in your plant. For each of the following items, please tell me whether
these elements of lean production have been implemented in your plant and your
assessment of your plant’s current position in adapting these lean production
elements.

1. Seven types of waste:

 

 

 

 

2. 5 S (Sort, straighten, sweep, sanitize, sustain):

 

 

 

 

3. Standardized work:

 

 

 

 

4. Quick set-up:

 

 

 

 

5. Small lot (containerization & transportation)

 

 

 

 

177

6. Machine layout (decoupling , buffers)

 

 

 

 

7. Level scheduling and one piece ﬂow:

 

 

 

 

8. Pull system (kanban):

 

 

 

 

9. What is your overall assessment of your plant’s current position in adopting these
lean production elements?

 

 

 

 

 

No Beginning Halfway Mostly Completely
Implementation Implementation Implemented Implemented Implemented
0% 25% 50% 75% 100%

 

 

 

Next, I would like to have you consider the plant’s activity in the following areas:

10. Involvement and participation (suggestion system, family activities & holiday
activities):

 

 

 

 

11. Teamwork (multifunctional activities, problem solving circles, roles of TM, TL, &
TM):

 

 

 

 

178

 

 

12. Training and development (standardized work, problem solving, TUTM training):

 

 

 

 

13. Recognition (attendance, safety, suggestions, etc.)

 

 

 

 

14. What is your overall assessment of your plant’s current position in adopting these
lean production elements?

 

 

 

 

 

 

 

No Beginning Halfway Mostly Completely
Implementation Implementation Implemented Implemented Implemented
0% 25% 50% 75% 100%

 

179

 

 

APPENDIX E

DEVELOPMENT OF SURVEY ITEMS AND SOURCE FOR PERCEPTIONS
REGARDING THE

IMPLEMENTATION OF LEAN PRODUCTION

180

 

Table 3.6: Development of Survey Items and Source for Perceptions Regarding the
Implementation of Lean Production

 

 

 

Construct Source
Supervisory behaviors Adapted from Ford, J. Kevin, and Adapted
from Cook, Hepworth, Wall & War ( 1981).
Management support Adapted from Cook, Hepworth, Wall &

War(1981).

 

Cooperative union-management relations

Adapted from Cook, Hepworth, Wall &
War (1981).

 

Commitment to lean strategy

Adapted from Ford, J. Kevin

 

' Job satisfaction

Adapted from Cook, Hepworth, Wall &
War (1981).

 

Perceived learning environment

Adapted from Tannenbaum, Scott I.

 

Developmental focus

Developed by William M. Mothersell.

 

Perceived team performance

Developed by William M. Mothersell

 

Managing change

Adapted from Ford, J. Kevin

 

Teamwork

Adapted from Cook, Hepworth, Wall &
War (1981).

 

Involvement/psychological participation

Adapted from Cook, Hepworth, Wall &
War (1981).

 

Process focus

Developed by William M. Mothersell

 

Proactive problem solving

Adapted from Ford, J. Kevin

 

 

 

Workplace trust Jointly developed by Ramanand, Moore &
Mothersell.
Workplace bonding Jointly developed by Ramanand, Moore &

 

Mothersell.

 

181

 

 

 

 

Workplace bridging

Jointly developed by Ramanand, Moore &
Mothersell

 

Conﬂict resolution climate

Adapted from Cook, Hepworth, Wall &
War (1981).

 

 

 

Team efficacy Adapted from Cook, Hepworth, Wall &
War (1981).
Lean training Developed by William M. Mothersell

based on training offered in the
participating organizations.

 

 

 

 

182

 

 

APPENDIX F

CORRELATION OF INDEPENDENT, MODERATING, MEDIATING, AND
DEPENDENT VARIABLES

183

 

 

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