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This is to certify that the
dissertation entitled

Exploring the Direct Versus Indirect Linkages among
Operations Practices, Operations Capabilities and
Operations Performance: Does Competitive Context
Moderate the Key Relationships?

presented by

Sarah Jinhui Wu

has been accepted towards fulfillment
of the requirements for the

Ph. D degree in Marketing and Supply Chain
Management

 

 

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Exploring the Direct versus Indirect Linkages among
Operations Practices, Operations Capabilities and Operations
Performance: Does Competitive Context Moderate the Key
Relationships?

By

Sarah Jinhui Wu

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTORAL OF PHILOSOPHY
Department of Marketing and Supply Chain Management

2007

ABSTRACT
Exploring the Direct versus Indirect Linkages among Operations Practices, Operations
Capabilities and Operations Performance: Does Competitive Context Moderate the Key
Relationships?
By
Sarah Jinhui Wu
Past research in operations strategy has identiﬁed several elements when establishing the
linkages between operations decisions and business performance. However, the nature of
the relationships among these elements has not been fully understood. This dissertation
research focuses on exploring the impact of two inter-related but different elements —
operations practice initiatives (i.e., operations practices viewed at the middle level of
aggregation) and operations capabilities — on operations performance. The goal is to find
out the foci of practice initiatives implementation and operations capabilities
development, and the focus of operations strategy under different business environments.

The overall objective of this study is accomplished through the exploration of four

primary research questions.

(1) What are operations practices and operations capabilities? What are the critical
diﬂerentiators?

(2) Are operations practices initiatives compensatory or additive in enhancing
operations performance? Are operations capabilities compensatory or additive in
enhancing operations performance?

(3) What are the relationships between operations practice initiatives, operations
capabilities, and operations performance?

(4) To what extent are the key relationships among operations practice initiatives,
operations capabilities, and operations performance inﬂuenced by competitive
environment?

This is primarily a theory building research that follows Wacker’s (1998) procedure. In

stead of separating theory building with theory validation, the study integrated both

activities in one cycle. At the first stage, the grounded theory method was used to derive
and extract theory from a focus group study. At the end of this stage a tentative model
was proposed for validation. The second stage was to collect data from a large
professional organization, test the tentative model, and further refine the model based on

the feedback of the data.

After analyzing the data, the study came to the following conclusions. First, with refined
definitions, operations practice initiatives and operations capabilities can be clearly
distinguished from one another and their validities were conﬁrmed. Second, the nature of
the intra—relationships among operations practices was quite different from that among
operations capabilities. Third, the importance of operations practice initiatives and
operations capabilities in improving operations performance depended on the
performance goals. Lastly, the findings established the moderating roles of market
competitiveness and market dynamism in the relationships among operations practices,

operations capabilities, and operations performance.

Overall, the results emphasized the value of cultivating operations capabilities in the
process of implementing operations practices initiatives, and the significant influence of
the business environment on choosing the focus of operations strategy. In addition, it
showed that firms should be more focused on strategic decisions, on what practices
initiatives to implement, and on what operations capabilities to develop since they exhibit
a more compensatory than additive nature. The research is one of the few to explore these
issues. The findings of this study not only enriched theory of operations strategy, but also

motivated future research by proposing, testing and reﬁning general hypotheses.

Copyright by
SanﬂrﬁnhuiVVu

2007

DEDICATED TO MY PARENTS, JUN WU AND XIUZHEN LI

ACKNOWLEDGEMENTS

Every step towards the completion of this dissertation was a struggle. I could not
have completed the work without the help, input, and encouragement from a number of
people. First and foremost, my dissertation chairman, Dr. Steven A. Melnyk deserves
special thanks as he devoted tremendous amount of time and energy to make sure that I
not only did interesting work, but also did it well. The research would not have been
possible without his active participation and guidance.

The comments and suggestions from my other dissertation committee - Dr.
Cornelia Droge, Dr. Barbara B. Flynn, Dr. Morgan Swink, and Dr. Shawnee K. Vickery ——
were invaluable in keeping the dissertation concise, crispy, and rigorous. I feel blessed to
have the opportunity to work with all these distinguished scholars.

I would also like to thank MASCO Corporation and The American Production
and Inventory Control Society for their involvement in and contribution to the study. I
appreciate their understanding of the value of the dissertation research and the access
they provided to their management team and membership database. Those managers who
contributed their time and thoughts in all the stages of the dissertation project made the
research questions more relevant and empirical validation possible. I do hope that the
results of this work provide them insights and beneﬁts in the ﬁeld.

Finally, to all my family members, thank you for supporting me all the way to the
completion of my doctoral study. Especially during the most difficult moments, you gave
me hope, you cheered me up, and you pushed me forward. Without all these, I could not

have been where I am today. I would like to share all my achievements with you.

vi

TABLE OF CONTENT

LIST OF TABLES .............................................................................................................. x
LIST OF FIGURES ........................................................................................................... xi
Chapter 1
OVERVIEW OF THE RESEARCH .................................................................................. 1
1.1 Introduction ............................................................................................................... 1
1.2 Research Questions ................................................................................................... 2
1.3 Research Model ........................................................................................................ 6
1.4 The Motivation of the Research ................................................................................ 9
1.5 Research Methodology ........................................................................................... 12
1.5.1 Qualitative Study — Grounded Theory Method ................................................ 12
1.5.2 Quantitative Study — Survey Method ................................................................ 14
1.5.3 Data Analysis Methodology ............................................................................. 15
1.6 Contribution of the Research .................................................................................. 17
1.7 Structure of the Dissertation ................................................................................... 18
1.8 Summary ................................................................................................................. 19
Chapter 2
LITERATURE REVIEW ................................................................................................. 2O
2.] Operations Practices ................................................................................................ 20
2.1.1 Current Status of Studies on Operations Practices ......................................... 21
2.1.2 Operations Practices in This Study .................................................................. 25
2.2 Operations Capabilities ........................................................................................... 30
2.2.1 Construct Conceptualization ........................................................................... 30
2.2.2 lntra-relationships among Operations Capabilities ........................................ 34
2.2.3 Relationships with Business Performance ....................................................... 36
2.3 Competitive Context ............................................................................................... 37
2.4 Gaps and Opportunities ........................................................................................... 40
2.5 Summary ................................................................................................................. 42
Chapter 3
CONCEPTUAL FRAMEWORK ..................................................................................... 43
3.1 Wacker’s Theory Building Approach ..................................................................... 43
3.2 Definitions and Measurements of the Constructs ................................................... 45
3.2.1 Operations Practices ....................................................................................... 45
3.2.2 Operations Capabilities ................................................................................... 49
3.2.3 Operations Performance .................................................................................. 55
3.2.4 Competitive Context ......................................................................................... 56
3.3 Domain of the Theory ............................................................................................. 57
3.4 Tentative Conceptual Framework ........................................................................... 58
3.4 Chapter Summary ................................................................................................... 61

vii

Chapter 4

RESEARCH DESIGN AND METHODOLOGY ............................................................ 62
4.1 Theory Development - Grounded Theory Method ................................................. 62
4.1.1 Grounded Theory Method ................................................................................ 63
4.1.2 Focus Group Study .......................................................................................... 65
4.2 Survey Development and Validation ...................................................................... 69
4.2.1 Q-sort ............................................................................................................... 70
4.2.2 Pre-test the Survey Instrument ......................................................................... 71
43 Survey Administration ............................................................................................ 72
4.3.1 Data Quality ..................................................................................................... 72
4.3.2 Data Quantity and Response Rate ................................................................... 74
4.3.3 Survey Delivery Method ................................................................................... 76
4.4 Limitations of Methodology ................................................................................... 78
4.4.1 Common Method Bias ...................................................................................... 78
4.4.2 Response Rate and Sample Size ....................................................................... 80
4.5 Data Analysis Methodology ................................................................................... 82
4.6 Chapter Summary ................................................................................................... 86
Chapter 5
DATA ANALYSIS AND DISCUSSION ........................................................................ 87
This chapter uses this flow of activities to structure the presentation of the results ..... 87
5.1 Results from the Focus Group Study ...................................................................... 87
5.2 Q-sort Results .......................................................................................................... 92
5.3 Data Collection ....................................................................................................... 94
5.3.1 Survey Responses ............................................................................................. 94
5.3.2 Demographic Information of the Sample ......................................................... 96
5.3.3 Sample Representativeness .............................................................................. 97
5.4 Data Analysis and Discussion ............................................................................... 100
5. 4.1 Analysis and Discussion for Research Question I ........................................ 100
5.4.2 Analysis Results and Discussion for Research Question 2 ............................ 110
5.4.2.1 Analysis Results ...................................................................................... 110
5.4.2.2 Discussions ............................................................................................. 112
5.4.3 Analysis Results and Discussion on Research Question 3 ............................. 114
5.4.3.1 Analysis Results ...................................................................................... 115
5.4.3.2 Discussions ............................................................................................. 1 18
5.4.4 Analysis and Discussion on Research Question 4 ......................................... 122
5.4.4.1 Analysis Results ...................................................................................... 122
5.4.4.2 Discussions ............................................................................................. 129
5.5 Reﬁned Framework .............................................................................................. 131
5.6 Chapter Summary............. .................................................................................... 133
Chapter 6
CONCLUSION AND FUTURE RESEARCH ............................................................... 134
6.1 Conclusions ........................................................................................................... 134
6.2 Theoretical Contributions ..................................................................................... 136

viii

6.3 Contributions for Management Practice ............................................................... 139

6.4 Limitations ............................................................................................................ 140
6.5 Directions of Future Research .............................................................................. 142
6.5 Chapter Summary ................................................................................................. 146
APPENDICES ................................................................................................................ 147
Appendix A. Protocol of Focus Group Study ............................................................. 148
Appendix B. Protocol of Q-sort .................................................................................. 150
Appendix C. Questionnaire in the Survey Study ........................................................ 152
BIBLIOGRAPHY ........................................................................................................... 161

ix

LIST OF TABLES

Table 3.1 Definitions and Measurements of Operations Practices Initiatives .................. 47
Table 3.2 Definitions and Suggested Measurements for Operations Capabilities ............ 53
Table 5.1 Core Operations Practices and Operations Capabilities ................................... 90
Table 5.2 Q-sort Results ................................................................................................... 93
Table 5.3 Summary of Respondents’ Demographic Information ..................................... 96
Table 5.4 Respondents’ Representativeness ..................................................................... 99
Table 5.5 Measurement Model for Operations Practice Initiatives ................................ 101
Table 5.6 Measurement Model for Operations Capabilities ........................................... 104
Table 5.7 Correlation Matrix of Core Operations Practices and Core Operations

Capabilities ..................................................................................................................... 109
Table 5.8 Measurement Model for Operations Performance .......................................... 111

Table 5.9 Regression Results of the Nature of the Relationship between Operations
Practice Initiatives and Performance .............................................................................. 112

Table 5.10 Regression Results of the Nature of the Relationships between Operations

Capabilities and Performance ......................................................................................... 112
Table 5.11 Mediating Effect in the Compensatory Model ............................................. 117
Table 5.12 Mediating Effect in the Additive Model ....................................................... 117

Table 5.13 Relationships among Operations Practices, Operations Capabilities, and
Operations Performance in More Competitive Markets ................................................. 124

Table 5.14 Relationships among Operations Practices, Operations Capabilities, and
Operations Performance in Less Competitive Markets .................................................. 125

Table 5.15 Relationships among Operations Practices, Operations Capabilities, and
Operations Performance in More Dynamic Markets ...................................................... 127

Table 5.16 Relationships among Operations Practices, Operations Capabilities, and
Operations Performance in More Stable Markets ........................................................... 128

LIST OF FIGURES

Figure 1.1 The Proposed Model .......................................................................................... 7
Figure 2.1 Operations Practices Hierarchy ....................................................................... 27
Figure 2.2 The Boundary of This Study ........................................................................... 36
Figure 4.1 Theory Development — Theory Validation Cycle ........................................... 62
Figure 5.1 Two Competing Second-order Construct Measurement Models .................. 108

xi

Chapter 1

OVERVIEW OF THE RESEARCH

1.1 Introduction

In the field of operations strategy, two concepts continue to play a critical role: operations
practices and operations capabilities. For example, when laying out their framework for
operations strategy, Hayes and Wheelwright (1984) identiﬁed the critical role that
operations capabilities play and recognized a collective pattern of decisions that shape
operations capabilities of a firm: capacity, facilities, technology, vertical integration,
workforce, quality, production planning/materials control, and organization. Similarly,
operations practices have been found to significantly inﬂuence ﬁrms’ abilities to compete
in the market (Flynn, Schroeder and Sakakibara, 1995a; Fullerton, McWatters and

Fawson, 2003; Giffi, Roth and Seal, 1990; Schonberger, 1996; Ward and Zhou, 2006).

In all of these studies, two critical observations are very evident. First, operations
practices and operations capabilities have been viewed as having a strong impact on the
ability of a firm to use its operations management system as a competitive weapon.
Second, the relationships between operations practices and operations capabilities have
not yet been addressed explicitly and modeled collectively in respect to their contribution

to operations performance.

This dissertation focuses on achieving three major goals. First, it defines and
differentiates operations practices and operations capabilities which serve as the building
blocks for theory construction. Second, it examines the intra-relationships among a set of

operations practices and a set of operations capabilities individually. Third, it explores the

inter-relationships among operations practices, operations capabilities, and operations
performance and to what extent the key relationships are inﬂuenced by competitive

environment.

This is primarily a theory building research as it clarifies the distinction between
operations practices and operations capabilities and evaluates their direct and indirect
impact on operations performance. In scope, the study reported in this dissertation
transcends the typical theory-building/theory validation dichotomy, as presented by
researchers such as Hunt (1991). Rather the study follows Wacker’s (1998) approach that
integrates both of the two activities. His approach consists of four stages — (1) deﬁne
variables, (2) limit the domain, (3) build relationships, and (4) seek empirical support.
This approach allows us to develop and refine the dual constructs of operations practices
and operations capabilities through a qualitative study and then validate the relationships

of the resulting constructs using quantitative data generated from survey method.
1.2 Research Questions

The overall objective of this study is accomplished through the exploration of four

primary research questions.

1. What are operations practices and operations capabilities? What are the critical

ditferentiators between them?

Given the centrality of these two concepts in operations strategy and the lack of rigorous
deﬁnitions on both in the literature, a very ﬁrst step in theory building is to clarify the

definitions and make a clear distinction between them. If the two constructs could not be

distinguished from one another, it would be impossible to evaluate their individual

impact on operations performance.

In this study, operations practices are defined as task-specific ways of organizing
resources1 with an aim to maintain and/or improve operations performance. For example,
early supplier involvement can be best described as an operations practice in that
manufacturers involve suppliers at an early stage (generally at the level of concept and
design) in the product development process in order to achieve new product success

(Bidault, Despres and Bulter, 1998; LaBahn and Krapfel, 2000).

Operations practices can be viewed from different levels of aggregation, and this study
examines them from the middle level. That is, each “operations practices” (for instance,
integrated product development practices) is actually an operations practice initiative
supported by a cluster of concrete practices (e.g., early supplier involvement, cross-

functional collaboration). Detailed discussion is provided in Chapter Two.

Operations capabilities are defined as demonstrated potentials to execute a speciﬁed
course of action in operations in a unique and proﬁcient wayz. This study identiﬁes
operations capabilities from a process perspective, not from an outcome perspective.
Take Wal Mart for example. Its competitiveness in cost is not considered as an operations
capability (cost reflects an outcome of a process). Rather, crossdocking can be viewed as
an operations capability. Wal Mart uses real-time demand data and its own ﬂeet to

rapidly consolidate shipments from disparate sources at its distribution centers and move

 

' Resources refer to people, technology, equipments, anything necessary for production.
2 This is the ﬁnalized deﬁnition of operations capabilities after the focus group study.

them to outgoing trailers without storing them in between. Crossdocking allows Wal
Mart to realize economies of scale in both inbound and outbound transportation, and
eliminate much of inventory in the distribution centers. This results in Wal Mart’s

competitive advantage in cost.

Nevertheless, the difference between operations practices and operations capabilities is
not resolved merely from the definitions; the fundamental difference is further discussed

from their nature in Chapter Three.

2. Are operations practices compensatory or additive in enhancing operations
performance? Are operations capabilities compensatory or additive in enhancing

operations performance ?

It is not unusual for a firm to implement more than one strategic initiative and develop
multiple capabilities, especially world class manufacturing firms have shown evidence of
developing capabilities that reinforced each other (Corbett and Van Wassehnove, 1993).
Accordingly, a ﬁrm faces a resource allocation decision. That is, is it better off to spread
out resources in adopting various operations practice initiatives or to focus on a few?
Therefore, there is a need to understand the intra-relationships among the set of
operations practice initiatives and the set of operations capabilities because it provides

guidance on how to split resource investment within each set.

This study incorporates a set of core operations practices (i.e., a group of operations
practice initiatives) and a set of core operations capabilities. It speciﬁcally answers the

question of whether a ﬁrm needs to invest in all the core practice initiatives to enhance

performance. If operations practice initiatives can be compensated by each other, a firm
that is good at some practices but poor at others can still run successfully. In stark
contrast, poor performance in any dimension could generate an adverse consequence if it
can not be compensated by other excellent dimensions. If this assumption turns out to be
true, a firm can not be better off unless it makes a minimum investment in all the core
practice initiatives. Similarly, the question can be applied to the set of core operations

capabilities: are they compensatory or additive?

3. What are the relationships between operations practices, operations capabilities, and

operations performance ?

This research question focuses on the inter—relationships among the core constructs —
operations practices, operations capabilities, and operations performance. Particularly,
what is the role of operations capabilities in the linkage between operations practices and
performance? Grounded on the literature and the resource-based View of the ﬁrm
(Schroeder, Bates and J unttila, 2002), operations capabilities are hypothesized to mediate
the impact of operations practices on performance. This perspective serves as a starting

point for theory development and reﬁnement.

The inter-relationships among the key constructs can be studied under the concept of
“ﬁt”. Among the six frameworks of “fit” proposed by Venkatraman (1989), “fit as
mediation” is appropriate because the criterion variable (i.e., operations performance) is
specific and the functional form of fit is relatively precise (i.e., viewed as an indirect
effect). The goal here is to ﬁnd out whether the mediator variable (i.e., operations

capabilities) accounts for a signiﬁcant proportion of the relationship between the

predictor (i.e., operations practices) and criterion. The results could help a firm weigh the
importance of operations practices and operations capabilities in setting up the focus of

operations strategy.

4. To what extent are the key relationships among operations practices, operations

capabilities. and operations performance influenced by competitive environment?

Every organization exists in an open system in which several factors can not be
controlled (for instance, changes in technology, government regulation, or competitor’s
action). The best a firm can do is to build and maintain the ﬁt with its environment.
Among various potential influencing factors, this study concentrates on the impact of

competitive environment on the key relationships discussed in research question (3).

Competitive environment has been widely proposed in the configuration framework of
operations strategy (Miller, 1988; Ward, Bickford and Leong, 1996) and tested in
empirical studies (Dean and Snell, 1996; Ward and Duray, 2000). This research question
is designed to specifically examine the role of environment in operations strategy and

increase the explanation power of the derived theory under various contexts.

1.3 Research Model

Whenever doing theory-driven empirical research, one does not have to go to the field
blind. Theory-building and validation is an iterative process from data to theory and then
from theory to data (Handﬁeld and Melnyk, 1998; Meredith, 1993). Consequently, the
study begins with the following initial conceptual model (Figure 1.1), which serves as a

starting point of the iteration. The model is grounded on the existing evidence in the

literature and captures the area of interest in this study. However, it is important to
emphasize that this model is tentative and revisions are expected in light of the actual

data.

      
   
 

Competitive
environment

 

 

 

 

 

 

/ I \
/ \
/ l \
/ I \
/ ' \
Operations 1 / $ .
practices Operations
performance
Operations
capabilities

   

 

Figure 1.1 The Proposed Model
The model portrays the direct impact of operations practices on performance and the
potential indirect impact through operations capabilities. In other words, the model
hypothesizes that operations capabilities mediate the relationship between operations
practices and operations performance. In addition, the relative impact of operations

practices and operations capabilities could be inﬂuenced by competitive environment.

The basic model in Figure 1.1 is similar to some recent studies (Rosenzweig, Roth and
Dean, 2003; Swink, Narasimhan and Kim, 2005) that have focused on competitive
capabilities. Operations capabilities and competitive capabilities should not be perceived
as being identical. Rather, they can be clearly distinguished from one another in a manner

similar to that used by Vickery (1991) to differentiate production competence from

manufacturing competence. Competitive capabilities are more observable given the
dimensions typically used to measure them (for instance, cost, quality, and lead time).
When customers buy products, they can recognize all the competitive dimensions
directly. However. operations capabilities are more embedded in the production process
where customers in general could not notice. In essence, operations capabilities are
process capabilities whereas competitive capabilities are outcome capabilities. Process

capabilities can affect outcome capabilities.

The research model is studied in a sequence of four steps. First, in order to operationalize
the model, operations practices and operations capabilities need to be clearly deﬁned and
properly measured. These definitions are drawn upon the literature, but not limited to it.
The measurements of operations practices are synthesized and structured based on the
extant research in order to capture multiple core operations practice initiatives.
Guidelines for measuring operations capabilities can be found in the work of Swink and
Hegarty (1998). Detailed information on the measurements of operations practices and

operations capabilities are presented in Chapter Three.

Second, the compensatory versus additive nature of core operations practices and
operations capabilities is assessed. That is, can weaknesses in certain dimensions of
operations capabilitiesbe compensated by strengths in other dimensions to improve
operations performance? Alternatively, is a certain threshold level required for all the
dimensions of operations capabilities? Similarly, are operations practices compensated
with each other to enhance operations performance? These questions are very critical to

operations strategy, yet little research has been done so far. The answers to these

questions give operations managers clear suggestions on how to set up the portfolio of
operations practice initiatives and operations capabilities to improve Operations

performance.

Third, the study explores and evaluates the relationships among operations practices,
operations capabilities, and operations performance. As can be seen in Figure 1.1,
operations practices are hypothesized to have not only a direct relationship with
operations performance, but an indirect one through operations capabilities. The interest
is to investigate whether operations capabilities are the intermediate stage between
operations practices and operations performance and what roles operations capabilities

play in the proposed framework.

Lastly, from a contingency perspective, it is interesting to re-evaluate the relationships
while taking into consideration the potential impact of competitive environment. The
results indicate the robustness of the base model and how significant the pattern

difference is under various environments.

1.4 The Motivation of the Research

This research is motivated from both the conceptual development of operations strategy

and empirical findings in this area.

Conceptually, there are basically two perspectives of operations strategy. From a content
perspective, operations strategy involves a sequence of decisions that enable a business
unit to develop a set of speciﬁc capabilities to implement a chosen competitive strategy

over time (Hayes and Wheelwright, 1984). From a process perspective, operations

strategy encompasses the identification and weighing of manufacturing’s competitive
priorities, strategic manufacturing decision-making, implementation, and manufacturing
performance measurement (Vickery, 1991). Thus, both perspectives implicitly assume
that operations strategy comprise several interrelated decisions/stages. However, how

these decisions are related has not been specified in the existing conceptual frameworks.

When researchers tried to answer whether operations strategy matters, they used different
“lenses” (for instance, operations practices, operations capabilities, core competence, or
production competence). Though these lenses are different in the sense that they may not
be at the same level or at the same stage, they are complementary dimensions of
operations strategy. For example, manufacturing competitiveness was often examined as
the outcome of operations processes which included a series of operations practices
(Flynn, Sakakibara and Schroeder, 1995b). Production competence considered the fit
between manufacturing competences and the strategic objectives of the ﬁrm (Vickery,
1991). Competence, sometimes used interchangeably with capability, can be viewed as a

high order construct of capabilities (Mills, Platts and Boume, 2003).

Using these different “lenses”, various empirical endeavors have been made to test the
direct relationships between each of these elements and operations/business performance.
The goal is to seek evidence to conﬁrm/disconfirm the operation strategy framework
proposed by Skinner (1969), that is, operations decisions have an impact on ﬁrms’
performance. However, the findings are mixed. Take research in operations practices for
example. Empirical studies that examined the direct relationship between Just-In-Time

(JIT) implementation and financial performance have reported mixed results

10

(Balakrishnan, Linsmeier and Venkatachalam, 1996; Callcn, Fader and Krinsky, 2000;
Huson and Nanda, 1995; lnman and Mehra, 1993). There also seemed to be no distinctive
patterns of Total Quality Management (TQM) factors that affect performance. For
instance, some authors (Flynn, Schroeder and Sakakibara, 1994; Parzinger and Nath,
2000; Powell, 1995) found that top management commitment or leadership is positively
correlated with many ﬁrm performance measures such as financial and operational results
as well as customer satisfaction. However, Wilson and Collier (2000) reported that top
management commitment is not related to financial results and Li (1997) reported similar
results with service quality perfomiance. Therefore, even the “best” practices are
contextual: it is “best” in the context of a certain business, company culture, and

competitive strategy (Heibeler, Kelly and Ketteman, 1998).

These mixed results imply that there may exist omitted variables, or the relationship may
not be completely specified, or the mechanism may be more complex than speculated.
Essentially, prior studies focused more on the direct linkage between operations practices
and performance (Balakrishnan et al., 1996; Challis, Samson and Lawson, 2005; Cua,
McKone and Schroeder, 2001; Dean and Snell, 1996; Flynn et al., 1995b) but neglected
the critical process/path necessary for operations practices to generate a positive
performance effect via operations capabilities. As operations strategy research becomes
mature, it is desirable, though challenging, to examine these mechanisms and processes
that explain how or why the elements of operations strategy combine together to generate

high operations performance.

1]

Inspired by the interaction of conceptual framework and empirical evidence, this study
aims to expand upon prior research by (1) exploring the potential mechanisms of how
implementing various operations practice initiatives could enhance operations
performance; (2) examining the roles of operations practices and operations capabilities
to improve operations perfomiance; and (3) studying the interactions between internal

decision-makings and external business environment on operations performance.
1.5 Research Methodology

Wacker’s (1998) theory building approach, which incorporates theory development and
theory validation, is adopted in this study. At the first stage, the grouped theory method is
exploited to revise the tentative model obtained from the literature review. The second
stage is to collect data from a large sample, test the revised tentative model, and further
refine the model based on the feedback of the data. This section addresses the

methodology issues on both stages and data analysis techniques.
1.5.1 Qualitative Study — Grounded Theory Method

This study takes a grounded theory method to generate theory through the interplay with
data. The grounded theory method, according to Strauss and Corbin (1990, p. 24), is “a
qualitative research method that uses a systematic set of procedures to develop an
inductively derived grounded theory about a phenomenon”. The primary objective of this
method is to expand upon an explanation of a phenomenon by identifying the key
elements of that phenomenon, and then categorizing the relationships of those elements

(Strauss and Corbin, 1990). Therefore, the grounded theory is developed from the data,

12

rather than the other way around. That makes the grounded theory method inductive in

that it moves from specific to general.

The grounded theory method is attractive in at least two ways. First, it does not require
researchers to suspend or ignore all pre-existing theoretical knowledge. Instead, it
encourages developing and enriching theories by drawing upon broad theoretical
approaches (Glaser, 1978). Second, the method fits in the research situation where
research questions are open and general rather than formed as specific hypotheses, and
where the emergent theory accounts for a phenomenon that is relevant to participants

(Strauss and Corbin, 1990).

In this study, the grounded theory method started from a tentative framework and
corresponding general research questions after literature was reviewed and gaps and
concerns were identified. The method was executed through a focus group study3. The
focus group consisted of eight knowledgeable, experienced, and capable middle level
operations managers from a large manufacturing corporation headquartered in Michigan
who met our recruiting requirements. Two researchers from Michigan State University
were present during the focus group study, one leading the discussion and the other tape
recording, taking notes, and clarifying questions. The discussion followed a protocol and
lasted for one and a half hours. Written notes and tape were reviewed and used for

analyzing the content. At the end of this phase, the key constructs were sharpened; the

 

3The focus group study and the survey study have been approved by Michigan State University Institutional
Review Board (IRB# X06-270). The human subjects were not exposed to any physical, emotional, and
psychological risk in this study.

13

tentative framework was refined; and additional insights were obtained for the

quantitative study at the next phase.
1.5.2 Quantitative Study — Survey Method

The second phase involves the implementation of a large scale survey designed to
confimr the validity of the measurement scales of the underlying constructs and verify the
presence of the relationships. The survey instrument was designed to incorporate
demographic information and the key constructs identified from the literature review and
the focus group study. As the measurements for operations capabilities have not been
well documented in the literature, a Q-sort was done to pre-assess initial construct
validity and reliability for operations capabilities (Moore and Benbasat, 1991). After the
scale refinement of operations capabilities, a pre-test was conducted for the whole
questionnaire. The primary purpose of the pre-test was to ensure that all the questions
were clearly articulated and appropriately understood. Once changes were made based on
the feedback from the pre-test group, the survey instrument was transformed and
mounted onto Michigan State University server using PERSEUS — an online survey

design software.

Considering the key research questions, the unit of analysis is appropriate at the plant
level. Consequently, the proper sampling frame is operations managers in plants. The
American Society for Production and Inventory Control - The Association for Operations
Management (APICS) was selected as the most appropriate organization to work with.
The majority of APICS members work in the area of operations and planning, which fits

well with our target population. A survey announcement with the link to the survey

14

website was sent out by the association to its members’ email accounts through its semi-

monthly e-newsletter in June, 2006.

In order to improve the response rate and obtain enough responses, the study used
multiple tactics that complement and reinforce each other, for instance, support from a
professional organization, multiple venues of incentives, follow up, multiple ways of
delivery, and moderate length of the survey. In addition, the survey study was conducted
in June, 2006, in advance of typical vacation time for employees in American firms. Last
but not the least, using APICS as a vehicle to administrate the survey enhanced the
quality of the data. In the announcement, operations managers were invited to participate
in the study, which limited the potential respondents to those directly involved in
producing a product or in providing a service. The survey itself also screened out those
whose main responsibility was not operations with the first question. All these procedures

ensured that the survey was delivered directly to the qualiﬁed respondents.

1.5.3 Data Analysis Methodology

Data analysis had two steps. The first step addressed the first research question with the
purpose of validating the dual constructs (i.e., convergent validity and discriminant
validity) — operations practices and operations capabilities. Confirmatory Factor Analysis
(CFA) has been carried out to show that all these constructs were valid, related, but
distinctive. Particularly, the difference between operations practices and operations

capabilities was conﬁrmed by empirical evidence.

15

The second step addressed the rest three research questions regarding the relationships
among the constructs. First, regression analysis was employed to test the competing
models between the compensatory and additive nature of core operations practice
initiatives and core operations capabilities respectively. The setup of regression analysis
followed the human judgment model, which were originally developed for individuals to
make decisions among competing alternatives when facing complex multi—attribute
information (Patton and King, 1992). In this context, customers decide to buy a product
from an organization because it carries certain desirable performance outcomes. In
another word, customers determine business success by voting for products of different
organizations. Each organization has a heterogeneous combination of operations practices
and operations capabilities, which can be viewed as multi-attributes from customers’

perspective.

Models of human judgment basically fall into two classes: the linear compensatory
models and the non-liner noncompensatory models (Bettman, 1979; Peter and Olson,
1987). Among the three types of noncompensatory models, the study was particularly
interested in the conjunctive (i.e., additive) model as it was relevant to the research
question (2) — does an organization need to implement all the core practice initiatives or
develop all the core operations capabilities to a certain extent to be successful in the
market? The additive model concerns whether a minimum level of each attribute is met
for a decision making. Therefore, the relative importance of all the attributes does not

matter in the decision-making process.

16

Regression analysis was carried out to investigate the inter-relationships among
operations practices, operations capabilities. and operations performance as the focus of
the question was the relative importance of operations practices and operations
capabilities in enhancing operations performance. The relative importance of them can be
revealed from the significance and magnitude of the regression coefficients. To test the
potential mediating effect of operations capabilities, the standard three-step regression

approach was used (Baron and Kenny. 1986; Venkatraman, 1989).

Finally, the impact of competitive environment on the key relationships among
operations practices, operations capabilities, and operations performance was further
tested by dividing the total sample into sub—samples and observing the pattern difference

derived from the regression results.
1.6 Contribution of the Research

The results of the research are interesting to both academia and practitioners. From an
academic perspective, the study clarifies the deﬁnitions of two critical but poorly deﬁned
constructs in operations strategy — operations practices and operations capabilities.
Second, the study develops a better understanding of the two constructs in terms of the
intra-relationships among the set of operations practice initiatives and the set of
operations capabilities. Third, instead of focusing merely on identifying the critical
elements of operations strategy, the study examines the operations strategy from multiple
dimensions and underscores potential mechanisms to improve operations performance.
The study explores the total impact of operations practices, operations capabilities, and

other contingent factors on operations performance. Moreover, the study provides a

17

picture of how operations practices and operations capabilities are interrelated and what
should be the foci of operations strategy. It addresses the critical question in operation
strategy — how operations can be used as a competitive weapon. The research enriches
the theory of operations strategy, and motivates future research by proposing, testing and
refining general hypotheses. The multi-method approach of qualitative and quantitative
studies lends itself well to (1) the development of valid and reliable scales of the latent
constructs and (2) the buildup and refinement of relationships among the latent

COIISII'UCIS.

The results also deliver important messages to managers facing investment decisions in
building operations capabilities and launching various practice initiatives to improve
operations performance. Business resources are limited, and therefore how to optimally
allocate them is critical. The answers to whether firms need to implement all core
practice initiatives and develop all core capabilities have a direct impact on resource
deployment. The answers to whether firms need to focus on capability development
indicate what managers should care most. In addition, competitive context has been
found to play a role in determining the effectiveness of operations strategy. For instance,
investing in operations capabilities was critical in competitive markets but investing in
operations practices worked well in less competitive markets. All these ﬁndings create

more venues for managers to shape their operation strategies.

1.7 Structure of the Dissertation

Chapter Two offers a comprehensive review of literature on the major concepts such as

operations practices, operations capabilities, and competitive environment. Chapter Three

18

contains the conceptual framework. As the research is a theory building exercise, the
procedures of theory building are introduced first. Chapter Four describes research
design, data collection. and data analysis methods. Chapter Five presents the results and
discussion of the data analysis. Chapter Six concludes the research, addresses the
contributions of the research, recognizes the limitations of the dissertation, and points out

directions for further studies.

1.8 Summary

The study explores the mechanisms of how operations practices generate a positive
impact on operations performance. It links the critical elements identiﬁed in operation
strategy research and examines the nature of the relationships between operations
practices and operations capabilities. The study enriches the theory of operations strategy
by incorporating the potential interactions between these elements and other inﬂuencing
factors. The results of the study are also intriguing to practitioners as it offers more
options to differentiate their operations strategies via the diverse combinations of
operations practices and operations capabilities given their specific business

environments and the desired performance goals.

19

Chapter 2

LITERATURE REVIEW

The study is to contribute to the theory building of operations strategy. To do this, the
study deals with two separate but interrelated constructs — operations practices and
operations capabilities. The situation is similar to what Venkatraman (1989) encountered
in examining “fit” in operations strategy. That is, there are many central and commonly
used terms that tend to be poorly, imprecisely, and inconsistently deﬁned. Operations
practices and operations capabilities, while central to the operations strategy research,

have been often poorly and inconsistently defined.

In this chapter, the literature pertaining to the major constructs is reviewed and the key
issues and concerns to be addressed in Chapter Three are summarized. The deﬁnitions
and measurements of the key constructs, the domain of the theory, and the relationships
among the constructs are elaborated in Chapter Three to keep Wacker’s (1998) approach

as a whole piece in one chapter.
2.1 Operations Practices

There has been a great deal of research devoted to manufacturing/operation practices4
(Cua et al., 2001; Dean and Snell, 1996; Flynn et al., 1995b; Samson and Ford, 2000;
Swink et al., 2005) and best/world class practices (Davies and Kochhar, 2002; Laugen,
Boer, Boer and Frick, 2005; Schonberger, 1996). Underlying these studies is the

argument that operations practices are drivers to enhance performance at the operations

 

4 . . . . . .
In thrs study. manufacturing practices and operations practices (a broader construct) are Viewed as same.

20

and corporate level. Given the potential importance of operations practices, it is important
to develop a good understanding of it. The review focuses on three issues: deﬁnition,

level of aggregation, relationships with performance.

2.1.] Current Status of Studies on Operations Practices

First, even though the construct of operations practices has been frequently used in
literature and there has been a long term awareness of its importance in operations
strategy research (particularly since attention shifted to world class manufacturing
practices in the early 1980s); the construct has not been consistently deﬁned. It was often
treated as something clearly well known, and some literature omitted the need to deﬁne it
(Bolden, Waterson, Warr, Clegg and Wall, 1997; Christiansen, Berry, Bruun and Ward,
2003; Clegg, Axtell, Damodaran, Farbey, Hull, Lloyd-Jones, Nicholls, Sell, Tomlinson,
Ainger and Sewart, 1996; Yusuff, 2004). Alternatively, operations practices were deﬁned
through examples (Guisinger and Ghorashi, 2004; Nahm, Vonderembse and Koufteros,
2004; Sakakibara, Flynn, Schroeder and Morris, 1997). For instance:
“T ime-based manufacturing practices are employed to achieve fast response to
customer needs; and such practices include cellular manufacturing, reengineering
setups, quality improvement efforts, preventive maintenance, and pull production.”
— Nahm et al., 2004
“An agile company can be defined as. . The ﬁve most prevalent agile practices can
be summarized as improving relationships with suppliers; formation of strategic
partnerships, adaptation of advanced technology/research. . .”
— Guisinger and Ghorashi, 2004

The definitions of “best practices” were even problematic because they were prescriptive

and tautological. For example, best practices have been deﬁned as those “that will lead to

21

the superior performance of a company” (Camp, 1989), or “the best ways to performance
a business process” (Heibeler et al., 1998). These kinds of definitions describe best
practices in an explicit positive relationship with performance, which makes it impossible
to identify them ex ante. Alternatively, best practices can only be identiﬁed ex post when
it shows evidence of enhancing performance. However, there are plenty of factors that
could make an inﬂuence on best practices. Given that the best practices for one firm
could be different from those for another firm, it is difﬁcult to give prescriptions to other

firms in investment decisions.

Second, the domain (i.e., the boundary) of operations practices was not clear in that
practices have been treated in different ways and viewed from different levels of
aggregation. Operations practices have been viewed at the low level of aggregation — a
relatively independent activity to achieve a specific and concrete goal. For instance,
statistical process control uses statistical tools to observe the performance of a production
line, to predict significant deviations that may result in rejected products, and to detect
whether the current process is under control. It is one of the building blocks to achieve

quality control in operations processes.

Operations practices have been viewed at the middle level of aggregation, which include
a set of mutually consistent practices at the low level. For instance, I IT practices studied
by Flynn et al. (1995b) have four dimensions such as Kanban controls, lot size reduction,
setup time reduction, and J IT scheduling. Operations practices have also been viewed at
the high level of aggregation. For instance, lean production and agile manufacturing

practices were referred as “systems of practices” or “collection of practices”

22

(Narasimhan, Swink and Kim, 2006; Shah and Ward, 2003). These systems include many

of the practices that other researchers studied at the middle level of aggregation.

As a result of varying levels of aggregation as well as different contents, it is difficult to
compare findings across studies. For example, total quality management was referred at
the middle level of aggregation in one study and at the high level of aggregation in the
other study. Some authors studied specific practices at the detailed level (Swadmidass,
1992; Swadmidass, 1994) whereas others focused on general practices (Hanson, Voss,

Blackmon and Claxton, 1994).

Third, much attention has been given to verify the relationships between operations
practices and operations/business performance. For instance, a great deal of work has
been done in examining the individual impact of JIT practices (Fullerton et al., 2003;
Sakakibara et al., 1997; White, Pearson and Wilson, 1999), TQM practices (Flynn et al.,
1995a; Powell, 1995), Advanced Manufacturing Technology (AMT) practices on
performance (Gupta and Whitehouse, 2001; Kotha and Swamidass, 2000), but only a few
investigated the integrated impact of multiple operations practices. For instance, Cua et
a1. (2001) bundled the JIT, TQM and Total Preventive Maintenance (TPM) practices
together and studied the overall impact on performance while Challis et a1. (2005)
examined the impact of integrated manufacturing (comprising AMT, J IT, and TQM) on
manufacturing performance. Given the reality that ﬁrms implement multiple practice
initiatives to improve performance, it is interesting to examine the interaction among

them in order to enhance operations performance.

23

Underlying much research, there has been the premise that adopting operations practices
is associated with improved performance or maintaining performance. However, the
research has yielded inconsistent results. For instance, unique J IT practices were found to
positively impact both TQM and JIT outcomes (Flynn et al., 1995b). However, JIT
practices alone were not found as a substantial factor to inﬂuence operations performance
and they generated an indirect effect that worked through improvement of manufacturing
infrastructure (Sakakibara et al., 1997). Large-sample empirical studies on AMT-
performance have also suggested mixed results. AMT has been found to link to higher
performance (Boyer, Leong, Ward and Krajewski, 1997; Kotha and Swamidass, 2000;
Ward, Leong and Boyer, 1994), but not in all situations (Beaumont and Schroder, 1997;

Jaikumar, 1986).

The failure to obtain consistent results could be explained by several factors. Most

notably:

(1) Focus of the study. Some focused on the impact of operations practices on
speciﬁc performance dimensions (Flynn et al., 1995b; Nahm et al., 2004) while others
studied their impact on general operations performance (Challis et al., 2005;

Sakakibara et al., 1997).

(2) The measures used to capture operations practices and performance. As Kaynak
(2003) pointed out, the inconsistency could be the result of using single construct to
measure practices or performance. For instance, TQM was operationalized as a single
construct in some studies (Douglas and Judge Jr., 2001) while in other studies TQM

was operationalized as a multidimensional construct (Samson and Terziovski, 1999).

24

Some authors (Dean and Snell, 1996) included only the core practices while others

(Cua et al., 2001; Sakakibara et al., 1997) examined infrastructure practices as well.

(3) Other omitted variables. The other suspicious issue in the majority of the extant
studies was that they focused only on the direct causal relationship. Some recent
studies (Boyer et al., 1997; Kotha and Swamidass, 2000; White et al., 1999) used
more sophisticated designs and considered the potential interaction between
operations practices and variables such as organization size, strategy, and
infrastructure. Yet, very few examined the indirect impact of operations practices on
performance through other variables. Particularly, operations capabilities have hardly

been considered as intermediate variables.

(4) Nature of the relationships. This is the question of what exactly the relationship is
between operations practices and performance. York and Miree (2004) specifically
posed this kind of question in studying TQM and financial performance: does TQM
lead to improved performance or better performing firms may be more likely to adopt

TQM? Put alternatively, is this a causal relationship or a covariant relationship?

All these studies indicated several venues to further understand the relationships between

Operations practices and performance.

2.1.2 Operations Practices in This Study

This study made three changes in the way it handled operations practices. First, it stayed
away from the notion of “best practices”. The reason is that “best practices” is highly

normative and implies one best way to operate the business. The deﬁnition of “operations

25

practices” in this study is similar to that in the study of Flynn et al. (1995b), which

emphasizes that operations practices are specific activities with objectives to achieve.

Second, as noted in the prior section, operations practices can be examined from different
levels of aggregation. The different levels of aggregation can be viewed as a hierarchy of
operations practices. An example is demonstrated in Figure 2.1. The first tier of the
hierarchy (lean production practices) is a practice system which covers many operations
practice initiatives with a goal of eliminating waste in operations processes and making
more by using less. Operations practice initiatives are on the second tier, each of which
contains a group of detailed practices at the third tier. The group of detailed practices is a
set of mutually inclusive, supportive activities that tend to be used together to achieve a
specific goal. For instance, all the practices listed under JIT share the same goal of

eliminating/reducing inventory in the production system.

In this study, operations practices were studied at the middle level of aggregation (i.e.,
second tier in Figure 2.1). That is, operations practices were seen as practice initiatives.
Each of the core operations practices identiﬁed and tested was a practice cluster. This
level of aggregation is appropriate and adequate for general managers to understand the

implication of operations strategy.

26

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

L d . . + Practice
can pro uctron practices system
T M .1 IT TPM ......... Practice
pracuces practices practices initiatives
on m "o
5 91 E.
a: :: ~—
= 'U '3
5:2. 3- o
s 5 B
T, g g I Detailed
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Figure 2.1 Operations Practices Hierarchy

Lastly, the inconsistent findings in the relationships between operations practices and
performance indicate that the nature of the relationships has not yet been completely
understood and opened up many research opportunities. Though it is possible to explore

many ways to resolve the inconsistency, this study only focused on three aspects.

(1) The study enlarged the scope of operations practices by studying a core set of
operation practices (i.e., operations practice initiatives) at the same time. Empirical
research on operations practices has been inundated with many articles focusing on
one or a few types of practice initiatives. In reality, firms tend to implement several of

them simultaneously to imitate world class manufacturing ﬁrms. Thus,

27

operations/business performance is the result of multiple practice initiatives rather
than only one or a few implemented. Consequently, the linkage between operations
practices and operations performance could be affected by other practice initiatives
not incorporated in the models if their contribution to performance could not be
decomposed. When Collins, Cordon, and Julien (1996) systematically examined the
relationship between multiple core practice initiatives and performance and they
found that companies are unlikely to build long-term sustained performance if they
choose to concentrate on only one or two areas. Therefore, they suggested that
operations practice initiatives have to be put in place in all areas to gain best

performance outcomes.

The core set of operations practices in this study included “hard” practices as well as
“soft” practices. Operations practices were traditionally interpreted as tools and
techniques. Yet, “soft” aspects (i.e., human resource management practices that are
based on belief, philosophy, and organizational culture) have also caught researchers’
attention. For instance, Flynn et al. (1995b) emphasized the infrastructure practices
(e.g., workforce development) shared by both JIT and TQM are crucial to JIT
performance. Clegg et a1. (1996) found that 80% of information technology
investments fail to achieve their performance goals not because of the technology
itself but due to the lack of attention given to the crucial role played by human and
organizational factors. Sakakibara et al. (1997) also mentioned that activities that
provide support for the use of JIT practices are neglected. Jayaram et al. (1999)
identiﬁed ten key dimensions of human resource practices that are associated with

manufacturing competitive dimensions. Gagnon (1999) called for a new paradigm of

28

operations strategy that underscores supportive managerial and organizational
practices that are not tied directly to operations processes per se. All these evidences
reinforced each other and suggested that “soft” practices be considered in conjunction

with “hard” practices.

(2) Operations practices should not be viewed as being independent. However, as
pointed out by Laugen et al. (2005), too little attention was paid to examine the
relationships among different operations practices (i.e., the relative effects of
individual practice and their interactions on performance). Facing limited amount of
resources in a ﬁrm, managers have to select a portfolio of practice initiatives. The
interesting question is whether they need investment in all of them or focus on a few.
The question is essentially about the intra-relationship among various operations
practices. Specifically, are they compensatory or additive? Operations practices are
compensatory if weakness of some practices can be offset by strength of others. So
far, there has not been literature explicitly addressing the question, and it has been left
at best in the discussion of future studies. Yet, the answer to this question has a
strategic implication to investment decisions. If the compensatory assumption is
proven to be valid, a firm can concentrate on a small set of practice initiatives
intensively. Otherwise, the ﬁrm has to spread out their investment to all major

initiatives to improve performance.

(3) The linkage between operations practices and performance was explored by
introducing an intermediate variable (i.e., operations capabilities). The idea is largely

grounded on the Resource-Based View of firm (RBV) which emphasizes the

29

importance of inimitable, non-substitutable, path dependent abilities that a firm has
developed over time, combined with other assets, and merged with organizational
culture (Schroeder et al., 2002). The study differentiated operations practices from
operations capabilities from their nature and proposed the logic connections between
them with respect to operations performance. The focus of this research is to explore
the potential mechanisms of how operations practices effectively impact operations

performance.

2.2 Operations Capabilities

The review on operations capabilities focuses on its definition, the intra-relationships of

operations capabilities, and their impact on performance.

2.2.] Construct Corzceptualization

Skinner (1969; 1974) was the first one to observe that a company’s operations function
could do more than simply produce and deliver products: operations offer certain
“capabilities” that could be used as competitive weapons for an organization. Much
research following Skinner’s seminal work, has specified operations capabilities as cost,
quality, delivery speed, delivery dependability and ﬂexibility (Boyer and Lewis, 2002;
Cleveland, Schroeder and Anderson, 1989; Ferdows and De Meyer, 1990; Flynn and
Flynn, 2004; Noble, 1995; Safizadeh, Ritzman and Mallick, 2000; Swink et al., 2005;
Ward, McCreery, Ritzman and Sharma, 1998). This illustrated that operations capabilities
have been defined in a fairly consistent manner, but there are further opportunities to

enhance the deﬁnition. This section identifies three of them.

30

First, operations capabilities are so closely related to other constructs such as operations
objectives/competitive priorities that it is difficult to differentiate among them.
Competitive priorities are the foci of decision-making in manufacturing strategy planning
framework, which include cost, quality, dependability, ﬂexibility, and service (Boyer and
Lewis, 2002; Van Dierdonck and Miller, 1980; Ward and Duray, 2000; Youndt, Snell,
Dean and Lepak, 1996). On the other hand, the same dimensions are used to describe
manufacturing capabilities (Noble, 1995; Roth and Miller, 1992; White, 1996). The study
viewed competitive priorities as goals at the strategy level and operations capabilities as

actual abilities at the operations level.

Second, there is no clear discrimination between operations process capabilities and
operations outcomes capabilities (Swink and Hegarty, 1998). Rosenzweig et al. (2003)
called cost, quality, reliability, and dependability as “manufacturing-based competitive
capabilities” mainly because they recognized that these dimensions are the outcomes of
operations processes. They represented a manufacturer’s actual competitive strengths
relative to primary competitors in its target market. While operations outcomes are
visible to and appreciated by customers (e.g., cost/prices, quality, and delivery time),
operations capabilities are invisible to customers (McGrath, Tsai, Venkataraman and
MacMillan, 1996; Penrose, 1959). Customers do not need to figure out and appreciate
how ﬂexible/agile/lean the operations process is when making purchasing decisions, but
firms achieve desired outcomes by deploying their capabilities. Simply put, operations

capabilities are a means to the end.

 

5 Customers are aware of prices rather than costs. However, prices are closely related with costs from the
economics perspective. The price of a product is usually set as the total of the cost and a markup.
Therefore, customers can sense of costs from prices.

31

Third, the treatment of operations capabilities is inconsistent with that of organizational
capabilities in the strategy literature. Organizational capabilities have been developed
largely based on the RBV and other frameworks extended from RBV. RBV builds on the
premise that what a firm can and can not do is greatly inﬂuenced by the
resources/capabilities available to it. In the framework, “resources” refers to tangible and
intangible assets firms used to develop and implement business strategies (Ray, Barney
and Muhanna, 2004). A resource has to exhibit certain attributes (such as rare, valuable,
inimitable, non-substitutable) to be the source of competitive advantage (Collis and
Montgomery, .1995). Apparently, tangible physical resources that are tradable in the
market can rarely satisfy the criteria. Therefore, several scholars have shifted from the
general resource perspective to the more specific capability perspective (Leonard-Barron,
1992; Nelson, 1991). This shift highlighted the importance of a variety of organizational
mechanisms, such as an organization's ability to coordinate specialized units,
organizational culture, and communication channels as sources of competitive advantage
and key determinants of organizational performance. Capabilities aim at deploying and
coordinating organizational resources. The ability to control over resources is path
dependent, difficult to be identified and decoded (Amit and Schoemaker, 1993; Prahalad

and Hamel, 1990), which meets the requirements to be sources of competitive advantage.

A Knowledge-Based View of the firm (KBV) is an extension of RBV (Grant, 1996). By
extension, knowledge becomes a resource critical for product, process, material and
organizational innovation, as well as a resource for the application, acquisition and
calibration of other resources for a firm’s objectives. More recently, scholars (Teece,

Pisano and Shuen, 1997) have extended RBV to dynamic markets and proposed dynamic

32

capabilities that rest on distinctive processes, specific asset positions and the path of
adoption. Though RBV, KBV, and dynamic capability emphasize different critical assets,
all of them attempt to identify the source(s) of competitive advantage from the process
perspective. Moreover, it is apparent that the source(s) of competitive advantage has/have
been specified more clearly, from general and vague “resources” to speciﬁc “capabilities”
and “knowledge”. This becomes a motivation to identify operations capabilities from

operations processes.

In contrast, operations capabilities tend to be stated from operations outcomes perspective
rather than from operations processes perspective. If cost, quality and time are the
dimensions upon which a firm wants to outperform the competition, a question remains
unanswered — what kind of capabilities need to be developed in production processes to

achieve these outcomes?

Some recent studies have provided greater insights of the definition of operations
capabilities. Hayes and Pisano (1996) suggested that capabilities are activities that a ﬁrm
can do better than its competitors. However, their definition needs to be further refined to
distinguish from operations practices. As noted in the prior section, operations practices
are activities, but operations capabilities are more latent than concrete activities. Swink
and Hegarty (1998) referred operations capabilities to fundamental proﬁciencies in
operations processes. Both papers recognized that capabilities exist at a different level
from outcomes and capabilities are associated with operations processes. Though still

quite vague, those definitions tended to converge with capabilities defined in the strategy

33

area and pointed out an important direction that should be considered when crafting the

definition of operations capabilities in this study.

2.2.2 Intra-relationships among Operations Capabilities

Research on operations capabilities deals with intra-relationships among themselves and
relationships between capabilities and business performance. This subsection reviews the

literature on the former relationships and the next subsection addresses the latter one.

There has been a persistent and on-going debate on the relationships among operations
capabilities — tradeoff, simultaneous, or cumulative. The trade-off perspective, originated
from Skinner’s (1969) operations strategy framework, is based on the premise that in the
absence of slack resources the achievement of a higher level of performance on one
capability can only be obtained at the expense of performance on other capabilities.
Therefore, plants need to prioritize their strategic objectives and focus on specific

capabilities.

In recent years the existence of trade-offs has been challenged. Global competition has
intensiﬁed the pressure on plants to improve along all dimensions. World class
manufacturing ﬁrms set the standard, developing capabilities that reinforce one another.
The most quoted example was that high quality enables plants to become more
responsive to customer needs, more reliable, and more cost efficient (Schonberger, 1996;
Szwejczewski, Mapes and New, 1997). Additionally, AMT allowed plants to develop

multiple capabilities simultaneously (Corbett and Van Wassehnove, 1993). Roth and

34

Miller (1992) provided evidence that business performance is positively related to a

company’s performance on a set of operations capabilities.

Pursuing the idea that multiple capabilities are desirable, the question becomes which
capability a company should develop first. Ferdows and De Meyer (1990) proposed the
“sand cone” model based on the proposition that competences are cumulative rather than
mutually exclusive. They specified a particular sequence in which a company’s
operations capabilities should be developed: quality — reliability — ﬂexibility — cost
efficiency. Empirical efforts to validate such sequences have been inconclusive with
unsuccessful studies (Flynn and Flynn, 2004; Wood, 1991) and successful cases (Lapre

and Scudder, 2004; Noble, 1995; Rosenzweig and Roth, 2004).

Even though operations capabilities do not mean cost, quality, reliability, and ﬂexibility
in this study, the nature of the relationships among them is worth further examination.
While the trade-offs, simultaneous, and cumulative discussion is theoretically interesting,
it was not central to the major thrust of this study. Rather, this study took a different
perspective to examine the intra-relationships among operations capabilities. That is, are
these operations capabilities compensatory or additive? If they are compensatory,
strengths in certain operations capabilities would counteract weaknesses in other
dimensions. Therefore, ﬁrms are free to conﬁgure their operations capabilities. They can
either continue developing those they are good at or focus on those poor operations
capabilities. However, if the nature of the relationships is additive, then firms may be
forced to compete on all dimensions and the overall performance is largely determined by

the poorest operations capability. A more thorough investigation of such relationships

35

offers a way of better understanding the diverse strategy formulations consisting of
different sets of operations capabilities. These kinds of questions were addressed in this

study with refined definitions and measurements of operations capabilities.

2.2.3 Relationships with Performance

Skinner (1.969) argued that operations has the potential to strengthen or weaken a
company’s competitive advantage. Hayes and Wheelwright (1984) also indicated that
operations capabilities can play a major role in helping a company achieve a desired
competitive advantage. Theoretically, it can be argued that there is a chain effect, that is,
operations capabilities improve operations performance which enhances competitive
advantage (Figure 2.2). Thanks to theory of production competence, the relationship
between operations performance and business performance has been validated (Cleveland

et al., 1989; Vickery, 1991; Vickery, Droge and Markland, 1993).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

l' ———————————— _|
Operations l Operations Business |
capabilities ———h performance } performance l
I ______ __T————l
Purpose of this study Theory of production
competence

 

 

 

Figure 2.2 The Boundary of This Study

Production competence captures two pieces of information — dimensions of operations
performance and the strategic importance of each dimension (Vickery et al., 1993). It is

the latter one that helps connect operations performance with business performance.

36

Given that the interest of this study is to explain how and why variables affect operations
performance, the linkage between operations performance and business performance goes
beyond the scope of this study. Yet, the findings of this study can be easily expanded to

the corporate level using the theory of production competence.

To sum up, this section suggests that the definition of operations capabilities can be
improved in three dimensions. First, it needs to be distinguished from other related but
different constructs such as competitive dimensions/priorities. Second, it needs to focus
on capabilities along operations processes. Third, its definition needs to be consistent
with organizational capabilities in the strategy literature. Therefore, to further advance
the study on operations capabilities, this research gives a deﬁnition addressing all these
problems in the next chapter. With a new definition, the relationships among operations

capabilities and their impact on operations performance are investigated.

2.3 Competitive Context

As has been argued in the section 2.1, mixed results of the linkage between operations
practices and performance could be the result of omitted variables. Accordingly, the
scope of operations practices is enlarged to include “soft” practices and the possible
misspeciﬁcation of relationships is suggested. However, those discussions are based on
the generic situation where firm characteristics and the competitive environment are
largely neglected. In fact, a business organization exists in an open system in which some
factors can not be controlled (for instance, changes in technology, government regulation,
or competitors’ action). All it can do is to build and maintain the ﬁt with the

environmental context.

37

The notion of “fit” has long been recognized in the operations strategy literature (Hayes
and Wheelwright, 1984; Venkatraman, 1989). An effective operations strategy not only
fits the environment by differentiating a company (or its products and services) from the
competition. but also fits the way the company conﬁgures, equips and manages
operations functions (Hayes and Wheelwright, 1984). Thus, competitive environment is a
factor inﬂuencing operations strategy and is particularly considered in this study because

of at least three reasons.

First, business environment has been captured as one of the dimensions in the
configuration approach in studying operations strategy. Miller (1988) argued that
environment (environmental uncertainty measured by unpredictability, dynamism, and
heterogeneity) and strategy are interdependent and firm performance results from the fit
between environment, strategy, and organizational structure. Similarly, Ward et al.
(1996) conceptualized four types of operations strategy based on the congruence between

the environment, competitive strategy, manufacturing strategy, and structure.

However, configuration is more like a framework than a complete theory (Miller, 1996)
because it lacks predicative power. To move toward theory, frameworks need to become
more precise, detail the mechanisms that explain the phenomena, and suggest some
implications that can be tested (Schmenner and Swink, 1998). Ward and Duray (2000)
went a step forward from the conﬁguration approach by depicting the relationships
among environment, competitive strategy, and operations strategy in their framework.
They found that environmental dynamism affects product differentiation strategy but not

cost leadership strategy, quality and ﬂexibility manufacturing strategy but not cost and

38

delivery. The empirical findings not only confirmed the critical role of environment in the
conceptual operations strategy framework, but also implied that its role could vary in
different operations strategy. That is the primary reason to bring an environmental factor

into the model.

Second, market competition has an impact on the effectiveness of operations practices
implementation. Dean and Snell (1996) specifically examined how the utilization of
integrated manufacturing relates to performance as a function of industry
competitiveness. They found that the impact of the relationship is magniﬁed or
diminished by competitive environment. Integrated manufacturing seemed to ﬁt better in
quality-oriented strategies and environments with limited competition. Their ﬁndings
suggest competitive context could be a moderator in operations strategy, which helps

specify its role in the proposed model.

Third, market competition has also inﬂuenced the pattern of capabilities. For instance,
Flynn and Flynn (2004) argued that industry competitiveness inﬂuences the relationships
between cumulative capabilities and plant performance. That is, plants in more
competitive industries are likely to gain less advantage from the law of cumulative
capabilities. Eisenhardt and Martin (2000) also observed that effective patterns of
dynamic capabilities vary with market dynamism. The dynamic capability framework
complemented the RBV by specifically considering market dynamism because RBV has
not adequately explained how and why certain ﬁrms have competitive advantage in
situations of rapid and unpredictable change. All the evidence together indicates that

market competitiveness could inﬂuence firms’ decisions in developing certain operations

39

capabilities. As operations capabilities is a key construct in the model, it is reasonable to

take into consideration the potential impact of competitive context.

To sum up, competitive environment is a relevant construct that has been considered in
the operations strategy research. Though it was well developed in conceptual
frameworks, little empirical research explored its role in implementation choices of
operations practices and development of operations capabilities. Therefore, its

inﬂuencing role was explicitly considered in this study.
2.4 Gaps and Opportunities

The review of literature centers on the major constructs in the area of operations strategy,
reveals several gaps and concerns, and becomes the starting point of this dissertation

research.

First, the constructs of operations practices and operations capabilities are important to
studies of operations strategy. Yet, they have not been well-defined and subject to
varying levels of disagreement. Rigorous definitions and solid measurements are needed
as the first step towards theory building. The review provides a guidance of how to reﬁne

their definitions.

Second, operations practices leave unanswered question of what relationships exist
among them. Similarly, operations capabilities leave unanswered questions regarding the
composite nature of capabilities — are they compensatory or additive? Assuming a set of
operations practice initiatives and operations capabilities is identified, what kinds of

operations practice initiatives need to be adopted to foster performance? Is a subset of the

40

initiatives enough? Do certain initiatives need to be present? Does a plant need to
implement all the operations practice initiatives to a certain extent? In the same vein,
similar questions can be asked about operations capabilities. Answers to these questions
could offer valuable information of how to develop a portfolio of operations practice

initiatives and operations capabilities to improve operations performance.

Third, as operations practices and operations capabilities are two central elements to
improve operations performance, do they interact one way or another? Current operations
strategy research has focused on the individual contribution of critical elements such as
operations practices or operations capabilities, while relationships among the elements
were largely neglected. The empirical ﬁndings challenged the simple direct relationship
and indicated that studying only one element at a time may not be sufﬁcient to bridge the
missing linkage between operations decisions and performance. Some recent studies have
started to explore the relationships between operations practices and capabilities (Swink
et al., 2005; Tu, Vonderembse, Ragu-Nathan and Ragu-Nathan, 2004), but still treated
capabilities as an outcome variables very much like operations performance.
Consequently, there is a need to improve the understanding of operations strategy by
simultaneously studying multiple elements and seeking intermediate relationships among

them.

Finally, competitive environment has not only been considered in the operations strategy
framework, but also showed as an inﬂuencing factor to operations practices and
operations capabilities. Therefore, it needs to be incorporated in the study. Particularly, it

is interesting to ﬁnd how competitive environment could inﬂuence the role of operations

41

practices and operations capabilities in improving performance. The results could help
plant managers delicately modify operations strategies in light of their business

environments.

2.5 Summary

Chapter Two reviews the literature regarding the critical constructs relevant to the
purpose of the study. This literature review has established the constructs/elements but
not many of their relationships. Establishing and refining the constructs and the
relationships are the foci of this study. Overall speaking, the study positions itself in the
operations strategy area as a step further in theory building by examining the
relationships among operations practices, operations capabilities, competitive context,

and operations performance.

42

Chapter 3

CONCEPTUAL FRAMEWORK

In the prior chapter, the constructs and elements that could be viewed as the building
blocks of this study were examined. In this chapter, the conceptual model that forms the
foundations of this study is proposed. However, to facilitate the development, reﬁnement,
and validation of this conceptual framework, this study draws extensively on the process
and guidelines in theory-building approach presented by Wacker (1998). Consequently,

this chapter begins by presenting an overview of his process.

3.1 Wacker’s Theory Building Approach

This study aims to extend operations theory. It expands the literature by first making
reﬁnement in constructs’ definitions and measurements and then introducing operations
capabilities as potential mediators between operations practices and performance. Since
there has been no prior well-developed theoretical foundation for the proposed
relationships and opportunities were explored to better explain operations performance,
this research is primarily a theory building work. The other reason to call it a theory
building research is that the general research hypotheses were tested, rather than specific
hypotheses derived from a well-developed body of relevant literature, the same argument

used by Vickery et al. (1993) in developing the theory of production competence.

Though the academic literature suggested many different theory building procedures for
specific types of research projects (Bacharach, 1989; Eisenhardt, 1989; Swadmidass,

1986), Wacker (1998) proposed a generic procedure that ensures all guidelines for

43

“good” theory—building are met regardless of the types of research projects. His approach
was adopted because unlike other procedures that separate theory development and
theory validation (Hunt, 1991), his approach integrates the two. That is to say, it is not

enough to propose a theory; the theory is not useful and rigorous unless it is tested.

Wacker’s (1998) four-step procedure includes conceptual definitions, domain limitation,
relationship building, and theory prediction/empirical support. First, clear, precise, and
concise definitions of constructs are required to limit the area of investigation by deﬁning
“who” and “what”. A literature review generally provides a base for defining constructs.

New definitions are proposed only if the current ones are inadequate.

After precise definitions of constructs are established, the domain needs to be specified to
limit “when” and “where” the theory holds. The domain of the theory directly limits its

generalizability. The more specific the domain, the lower the generalizability is.

The third step is to build logic relationships among constructs/variables. The relationship
between any two constructs/variables must be explicitly stated, or else the theory can not
be shown to be internally consistent. Wacker (1998) emphasized the importance of
academic literature in suggesting the potential relationships and raising the abstraction
level for theory development. The goal is to address the common questions of “why” and

“how” through logical reasoning.

The last stage is theory validation and prediction. To be useful, a theory has to pass the
empirical test. Therefore, empirical evidence needs to be presented to verify that a

proposed theory can be applied in the real world. Different methodologies (e.g.,

44

experimental research, survey research, or case research) use different types of empirical

evidence to verify the predictive validity of a theory.

This chapter covers the first three steps, leaving the last step in Chapter Four and Chapter

Five.
3.2 Definitions and Measurements of the Constructs

The section contains the detailed information of how to define and operationalize the

major constructs in this research.
3 .2. 1 Operations Practices

Based on the discussion in Chapter Two, the study defined “operations practices” as task-
specific ways of organizing resources with an aim to maintain and/or improve operations
performance. This includes not only specific activities but also general practices that can

be applied to the operations management context.

As pointed out in Chapter Two and noted by other researchers (Bolden et al., 1997),
operations practices have been addressed in different content and at varying levels of
aggregation, which made comparison across studies difﬁcult. This study focused on
identifying multiple core operations practices at the middle level of aggregation (i.e.,
practice initiatives). The scope of the core practice initiative was expanded to contain
both practices aiming to achieve speciﬁc strategic objectives and those supporting

general strategic objectives. It comprised both “hard” practices and “soft” practices. The

45

improvement was in line with Davies and Kochhar’s (2002) suggestion that operations

practices should be approached holistically.

Table 3.1 contains the definitions and measurements of seven core operations practice
initiatives generated by synthesizing the existing studies. They are quality management
practices, .1 IT ﬂow practices, customer orientation practices, supplier relationship
management practices, integrated product development practices, workforce development
practices, and leadership practices. It should be noted that this is not a complete list of
operations practice initiatives, but represents those widely used and tested in the literature
and the foci of this study. Being at the middle level of aggregation, those operations
practice initiatives consist of detailed practices at the low level of aggregation. Therefore,

low level practices are used to measure middle level practice initiatives.

46

 

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3.2.2 Operations Capabilities

Insights from organizational capabilities are valuable input to derive the definition for
operations capabilities. Organizational capabilities have long been treated as firm-specific
assets from RBV perspective (Ray et al., 2004). Sometimes they were deﬁned in wide
latitude as “anything which could be thought of as a strength or weakness of a given
firm” (Wernerfelt, 1984), while other times defined as a high-level routine (or collection
of routines) that is highly patterned, repetitious, and founded in part in tacit knowledge
(Winter, 2003). Brieﬂy, capabilities are institutionalized routines embedded in the
processes and demonstrated in the organization’s abilities to do something (Subramaniam

and Youndt, 2005; Teece et al., 1997).

The focus in this study is operations capabilities, which limits its domain to only
operations function. However, as operations is a functional area of an organization, it
intrinsically carries certain characteristics of the organization. In line with this logic,
some general organizational capabilities are also evident to a certain extent at the
functional level. Taking this into consideration and emphasizing the strengths and
weaknesses of an operation process, “operations capabilities” were deﬁned as
demonstrated potentials to execute a specified course of action in operations in a unique

and proficient way.

This definition of operations capabilities circumvents the tautological criticism.
Operations capabilities are desirable in generating positive intermediate outcomes in
terms of the way that a firm carries out an action or a series of actions. However, they do

not automatically link to performance. The line of reasoning is similar to those arguing

49

that dynamic capabilities are not tautological as the definition underlines the ability to
integrate/reconfigure resources rather than the ability to create value in business

performance (Eisenhardt and Martin, 2000).

In reviewing the definitions, operations practices are clearly different from operations
capabilities. The former refers to specific, task/goal oriented, and contextual bounded
activities, while the latter is broad-based, context free routines/mechanisms that enable
the most efficient use of a firm’s assets (Day, 1994). If operations practices can be
articulated in the ways things are done, the elusive nature of capabilities makes it difficult

to be specific enough to itemize and imitate.

“Statistical process control” is an example of quality management practices. This practice
can be learned as a method for achieving quality control in operations processes and
adopted by firms that care about quality. However, operations capabilities are path
dependent, difficult to be identified and decoded. Take “development of proprietary
processes” for example. Every organization is endowed with various kinds of assets, and
it can even exchange assets with the market and use assets in different ways. But how it
extends, customizes, and combines the use of assets is evolved inside an organization
over time, contingent upon such factors as business strategy and organizational culture. In
addition, this capability is also formulated upon a combination of unique organizational
actions, learning, and cumulated knowledge. Therefore, it is a very complicated process
to develop a proprietary process. Consequently, it is difficult to find a one to one
correspondence between what an organization does and what capabilities it possesses

(Dierickx and Cool, 1989; Hart, 1995). That is to say, it is extremely hard to decipher a

50

capability because there is no such standard path an organization can take and develop it

over night.

As RBV framework provides the guidelines/criteria to identify organizational
capabilities, a similar counterpart is necessary at operations level. White’s meta-analysis
(1996) on operations capabilities showed that the enormous amount of research done so
far had identified cost, quality, delivery speed, and delivery dependability as operations
capabilities. Swink and Hegarty (1998) proposed the ﬁrst operations capabilities
framework which put capabilities in the context of operations processes and also
categorized them into static and dynamic capabilities. Even though it has not been
empirically tested. the Swink and Hegarty framework serves as a starting point for this

research.

Since then a lot of work has taken place (Escrig—Tena and Bou-Llusar, 2005;
Subramaniam and Youndt, 2005). Insights from those works were used to augment
Swink and Hegarty’s (1998) static-dynamic capabilities framework. Static capabilities are
demonstrated potentials to perform an operations action in a unique and proﬁcient way at
the steady state, for instance, cooperation skills (Escrig-Tena and Bou-Llusar, 2005),
development of proprietary processes (Escrig-Tena and Bou-Llusar, 2005; Schroeder et

al., 2002), and responsiveness (Swink and Hegarty, 1998).

Dynamic capabilities are demonstrated potentials to execute changes that affect resources

or routines by develOping new capabilities to adapt to the environment. They include

incremental process improvement (Swink and Hegarty. 1998), radical process innovation

5]

(Subramaniam and Youndt. 2005: Swink and Hegarty, 1998), and process

reconfiguration (Teece et al., 1997).

Detailed definitions and suggested measurements are summarized in Table 3.2. As the
goal is to identify a list of core operations capabilities (not a comprehensive list), the
difference between static capabilities and dynamic capabilities is not emphasized. In light
of the new deﬁnitions of operations capabilities, scale development must also take place.

Measurements are suggested based on the available and relevant sources.

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3.2.3 Operations Performance

Given that the study focuses on explaining what causes the difference in operations
performance, the analysis should be carried out at the plant level. There are many ways to
measure operations performance, depending on the focus of the study. For instance, the
impact of J IT practices on operations performance was measured in terms of inventory
tumover, on-time delivery, lead time and cycle time (Sakakibara et al., 1997). Lean
manufacturing’s impact on operational performance was evaluated by manufacturing
cycle time, scrap and rework costs, labor productivity, unit manufacturing cost, ﬁrst pass

yield, and customer lead time (Shah and Ward, 2003).

Even though different operations practices emphasized distinctive sets of performance
measurements, the predominant approach in the literature was to use cost, quality,
delivery, and ﬂexibility as the four basic indicators of overall operations performance.
The use of these indicators can be traced back to Skinner (1969) who proposed the
operations performance measurements in his seminal article. These measurements have
been widely used by many other researchers (Cua et al., 2001; Miller and Roth, 1994;

Schroeder et al., 2002; Ward, Duray, Leong and Sum, 1995).

This study focused on the same elements of cost, quality, and delivery in capturing
operations performance. The only exception was that ﬂexibility was excluded, and this
decision was made based on two reasons. From the conceptual perspective, flexibility is
generally defined as the ability of an operations management system to respond quickly
to changes at low cost (Gerwin, 1993; Swink et al., 2005). Therefore, it can be viewed as

a combined/derived measure. Even though ﬂexibility portrays an indispensable area of

55

competition, it in fact reﬂects the interaction between cost and time/range. From the
statistical analysis perspective, there should be discriminant validity among constructs.
The constructs are expected to be not only correlated, but also clearly differentiated.
Flexibility is closely related to “responsiveness” — one of the operations capabilities in
this study. In order to minimize the possibility of item cross—loading and avoid
redundancy in items of operations performance, this study only considered cost (e.g., unit
cost of production, manufacturing overhead cost, total cost), quality (e.g., conformance
quality, product reliability, product features) and delivery (e.g., delivery accuracy,
delivery dependability, delivery quality, delivery availability) as the three dimensions of

operations performance.

3.2.4 Competitive Context

Competitive environment is also a complex and multifaceted construct that can be
conceptualized in a number of different ways. This research limited itself to two critical

dimensions: market competitiveness and market dynamism.

The treatment of market competitiveness is based on Porter’s (1980) ﬁve forces model.
Specifically, “intensity of rivalry among existing competitors” has been identified as a
driver of business strategy. As noted, industry concentration is an appropriate
measurement for market competitiveness in organization theory literature and therefore
became a proxy measure in this study. Industries marked by high concentration are less
competitive than those that are not (Dean and Snell, 1996). The more competitive market

carries such features as (1) more major competitors in the market, (2) narrow price

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difference among competitors (Brynjolfsson and Smith, 2000), and (3) small

growth/decline in sales (Flynn and Flynn, 2004).

The second dimension is market dynamism that underscores how rapidly the industry is
changed by new products/processes. This aspect is worth examining in today’s
environment and has been addressed in the dynamic capability framework. The
measurements for market dynamism, largely indicated by the rate of innovation and

change of customers’ preference, follow the work of Anand and Ward (2004).

3.3 Domain of the Theory

The framework developed describes a mid-range theory and certain boundaries have to
be placed in this study. First of all, the resulting theory is limited to organizations whose
main responsibility is operations (e.g., plants). Second, the resulting theory is built upon
the current perspective and knowledge, and therefore limiting itself to the current
business environment. It has been observed that operations practices change with time.
Therefore, what has been captured as major practice initiatives in the current framework

may not hold years later.

Lastly, the resulting theory can be generalized across a certain range of environment
settings. Findings could be more applicable to moderately dynamic markets because
dynamic capabilities have different implications in different types of markets (Eisenhardt
and Martin, 2000). Dynamic capabilities can become a source of sustained competitive
advantage in moderately dynamic markets. However, in extremely dynamic markets, the

results become unpredictable.

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All these observations show that the constructs and relationships identified in this
research may not be universal. However, it goes beyond the scope of the study to test the

generalizability of the theory in other contexts and/or another point of time.

3.4 Tentative Conceptual Framework

Some researchers (Eisenhardt, 1989) argued that theory building research does not need a
conceptual framework to start with and should begin as close as possible to the idea of no
theory under consideration and no hypotheses to test. However, recognizing that
researchers do not need to capture all the data and it is impossible to capture all the data
in the field, an initial lens drawn from a theoretical foundation is critical. The chosen lens
is a way but not the only way to study the research questions; yet it helps the researchers

focus on the research questions and data collection.

Research questions and a prior specification of constructs were suggested to shape the
initial design of theory building research (Eisenhardt, 1989). Wacker (1998) further
recommended stating the relationships among the constructs explicitly and arguing the
connections logically through reviewing academic literature before any empirical support
is found. Although early identification of the research questions, possible constructs, and
relationship is helpful, it is equally important to recognize that the framework is tentative
and subject to change, based on the insights from the ﬁeld study. Without them, it is easy

to be overwhelmed by the volume of data in the ﬁeld.

Figure 1.] (in Chapter One) provides the initial framework for this research. It is only a

simpliﬁed graphic model. In fact, both operations practices and operations capabilities

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contain a list of items (as discussed in the previous sections). Research questions and
construct conceptualizations have been addressed before and are not repeated. The focus
of this section is to describe the relationships among the constructs based on some

insights from the relevant literature.

The literature review in Chapter Two indicated that the direct relationship between
operations practice initiatives and operations performance has been widely proposed and
tested (Cua et al., 2001; Flynn et al., 1995b; Sakakibara et al., 1997; Shah and Ward,
2003). However, Schroeder et al. (2002) argued that the direct relationship is not enough
to explain the phenomenon from the RBV perspective because research on operations
practices did not explicitly address the effects of competitors imitating a successful
innovation and failed to recognize the importance of proprietary processes that can not be
obtained from factor markets. That is, operations practices adopted by imitating world
class manufacturers may contribute to competitive parity but not to competitive
advantage. Accordingly, these researchers promoted further studies from the

resource/capability perspective.

There have also been other researchers calling for the resource-based research in
manufacturing plant setting (Amundson, 1998; Swadmidass, 1991), but Schroeder et al.
(2002) are among the few that empirically validated the applicability of the RBV to
operations capabilities. Following this line of research, operations capabilities were

introduced as another contributor to operations performance.

The strategy literature also argued that dynamic capabilities are the source of sustained

competitive advantage (at least in the moderately dynamic market). But dynamic

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capabilities exhibit commonalities across effective firms, which are called “best practice”
(Eisenhardt and Martin, 2000). For example, product innovation is an important dynamic
capability. Effective product development practices typically involve the participation of
cross-functional teams that bring together different sources of knowledge and expertise.
With these teams, coordination becomes more efficient among operations, marketing and
design people, and eventually accelerates the process of product development. Therefore,
operations capabilities are not born from vacuum; rather they are nurtured during the

implementation of operations practices.

These arguments and evidence indicate that operations practices and operations
capabilities may be interrelated and that operations capabilities may play an important
role in converting adopted practices into intrinsic and inimitable abilities to enhance
operations performance. Specifically, the following questions about the proposed model
are raised. How important is the indirect path through operations capabilities, compared
with the direct path between operations practices and performance? The indirect path
suggests the mechanism of how operations practices adoption enhances operations
performance, which has not been addressed in the literature. The proposed indirect path
and the well-researched direct path constitute the tentative model in Figure 1.1. Put
simply, operations capabilities are hypothesized to mediate the relationship between

operations practices and performance.

Besides the main frame of the model presented in Figure 1.1, market environment is
proposed as having an impact on the interrelationship among operations practices,

operations capabilities. and operations performance (dashed lines). As argued in the prior

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chapter, the competitive environment may inﬂuence decisions on practice
implementation and desirable capabilities. From the contingency perspective, it is

reasonable to argue that it could moderate the key relationships in the basic model.
3.4 Chapter Summary

The chapter begins by selecting a general procedure for our theory building research. At
the heart of theory building is the clear, precise and concise deﬁnitions of constructs.
Therefore, the research spends much time and effort in defining and differentiating
operations practices and operations capabilities, and introducing other inﬂuencing
factors. The theoretical framework that portrays the relationships among the key

constructs provides a foundation and driving vehicle for the methodology chapter.

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Chapter 4

RESEARCH DESIGN AND METHODOLOGY

Having the general theoretical model and approach laid out in the preceding chapters, this
chapter describes the research methodology used to implement the model. Since the
research follows the guidelines set out by Wacker (1998), theory and data are linked in
one complete cycle (Figure 4. 1). While most studies focused on one of these two arcs
(i.e., from theory to data or from data to theory), this study unifies both. Since the ﬁrst
step in this process is to build theory, the study begins with the link from data to theory.

To implement the link, the grounded theory method is adopted.

Theory development

/\

Theory validation

Figure 4.1 Theory Development — Theory Validation Cycle

4.1 Theory Development - Grounded Theory Method

As shown in Figure 4.1, theory-building is an iterative process: going from data to theory
through observation/description, empirical generalization and explanation, and then from
theory to data through hypotheses testing (Handfield and Melnyk, 1998; Meredith, 1993).
This study took a grounded theory method (Glaser and Strauss, 1967) to develop and

reﬁne theory through interaction with data collected in a focus group study.

4.1.1 Grounded Theory Method

The grounded theory method is largely based on a general method of comparison/contrast
analysis (Glaser and Strauss, 1967). Cases similar on many variables but with different
outcomes are compared to see where the key causal differences lie. On the other hand,
cases that have similar outcome are examined to see which conditions they have in

common, thereby revealing the possible causes.

The grounded theory method is appealing in theory building studies. The theory it
generates allows researchers not only to develop a theoretical description of the general
features of a topic but also to ground the explanation in empirical observations or data
(Glaser and Strauss, 1967). Therefore, it is usually not completely refuted by one set of
data or replaced by another theory, despite its inevitable modiﬁcation and reformulation.
This robustness reﬂects one great advantage of the grounded theory over those derived
deductively from a grand theory. Without the help of data, those theories could turn out

to fit no data set at all.

The grounded theory method is particularly desirable for this study because it helps
resolve the gaps and concerns pointed out in Chapter Two. First, the key constructs in
this study — operations practices and operations capabilities — have not been well-deﬁned
and subject to varying levels of disagreement. Before they can be validated, their
deﬁnitions and measurements have to be reﬁned using the grounded theory method.
Second, the study explores the nature of the relationships among the operations practice
initiatives set and operations capabilities set, which is an issue under researched in

existing literature yet important from the resource investment perspective. The grounded

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theory method could help gain insights on the nature of the relationships and establish an
initial framework for validation. Third, the tentative model proposes an alternative way to
explain operations performance by considering the total effect of operations practices,
operations capabilities, and competitive context. The grounded theory method could
provide evidence of the interrelationships or interactions among these constructs and

offer an opportunity to refine the model.

The data for a grounded theory can come from at least four sources: interviews, direct
observations, focus groups, and case studies — anything that may shed light on questions
under study (Corbin and Strauss, 1990; Strauss and Corbin, 1994). This study used a
semi-structured focus group interview design. A focus group study is to use a small group
of selected people from a wide population with the purpose of soliciting their opinions

about or emotional response to a particular subject (Stewart and Shamdasani, 1990).

A focus group was preferred in this study than a single case study because more
diversiﬁed information can be extracted from discussions with managers in different
organizations. It is also less costly and less time consuming, compared with a multiple
case study (Bonama, 1985). Each case study can be an extensive and expensive endeavor,
making the acquisition of such qualitative expertise arduous or slow. Instead of paying
many trips to different organizations, all the interviewees in a focus group were met at a

specific time and their opinions were collected all at once.

The focus group study was also preferred than a Delphi study because it allowed
participants to elaborate their views and to interact with others’ perspectives (Abbott and

Eubanks, 2005). In the process of expressing their views loud and having them discussed

64

by others, participants are exposed to other opinions, able to reﬂect and reassess their
own interpretations. That is a consequence of the dialectic process that focus groups
engender (Eubanks and Abbott, 2003). While a Delphi study emphasizes the achievement
of a reliable consensus of opinions among a group of people by a series of questionnaires
combined with controlled feedback from the study coordinator (McKenna, 1994), the
primary objective of a focus group study is to provide greater insights into how people
view a phenomenon and why they view it that way. The goal was not to reach consensus

but to listen to all the possibly different explanations.

4.1.2 Focus Group Study

The appropriate participants in the focus group are those in charge of operations at plant
level because they matched with the level of analysis in the study. They represent the
middle level operations managers in a corporation who are most likely interested in this
study and have the most relevant knowledge to offer insightful opinions. Second, it is
desirable to have participants working at different business environments (e.g., good
performance plants versus poor performance plants, plants that underscore product
innovation versus plants that emphasize process innovation). Consequently, they can
bring together diversified knowledge based on their different experiences and provide
alternative views for the same phenomenon. Third, participants who have common
understandings on the terms used in the study are highly preferred. Given the limited
amount of time of the on-site discussion, more time could be spent in collecting their

view points regarding the key research questions than explaining those terminologies.

65

A corporation (The name is not disclosed to protect its identity) was found that was
willing to sponsor this study and could provide the candidates that fit with the recruiting
requirements. The company has a unique relationship with Michigan State University.
Over the last ten years, an education program was jointly established to provide training
to the middle level management team for the corporation. The education program helped
improve the trust between the corporation and Michigan State University. As a result, the
corporation was willing to give access to its management team, and its managers were
more likely to participate in the study. Also, the managers who have gone through the
intensive training program were familiar with the terms used in the study and had less

confusion of them.

In addition, the corporation has a well-developed operations planning and execution
system committed to quality and operational excellence. The company has been aware of
various kinds of operations practices and implemented practices such as lean
manufacturing. value engineering, value analysis, formal product innovation,
collaboration in supply chain, and six sigma. The management team of the corporation
also recognizes and develops sufﬁcient operations capabilities to compete in the market.
Therefore, they have the skill set and knowledge to comment on the issues raised in the

study.

Apart from this, the corporation houses 30 major manufacturing divisions or companies
in the home decoration and construction industry. Therefore, its managers are exposed to
a wide variety of business environments. Some work under the environment with fast

clock speed while other work under the environment with slow clock speed (Fine, 1999).

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Some plants have experiences in product innovation while others in process innovation.
The data collected from the management team in such a corporation are comprehensive
and representative, which provides generality in that the theory includes extensive

variation and is abstract enough to be applicable to a wide variety of contexts.

Most focus groups consist of six to twelve people (Chan and Man, 2005; Jarvenpass and
Lang, 2005); however, the number of participants depends on the objectives of the
research (Stewart and Shamdasani, 1990). For example, smaller groups (four to six
people) are preferable when the participants have a great deal to share about the topic or
have intense or lengthy experiences with topic of discussion (Krueger, 1988). In general,
Merton, Fiske, and Kendall (1990) suggested that the size of the group should be
governed by two considerations. It should not be too large to preclude adequate
participation by most members, nor should it be so small that it fails to provide

substantially greater coverage than that of an interview with one individual.

In order to secure a certain number of participants, Krueger’s (1988) suggested inviting
two times the desirable size of people. In this study, 25 invitations were sent to managers
in the selected corporation in order to form a focus group of six to twelve people. Eight
managers responded and agreed to join the focus group at the designated time. Some of
the respondents were willing to participate, but could not make it due to prior travel

commitment.

Two researchers from Michigan State University acted as moderators in the study, one

leading the discussion and the other tape recording, taking notes, and clarifying

67

questions. Participants were encouraged to speak one at a time to avoid garbling the tape

(Krueger. I988). The discussion lasted for one and a half hours.

The focus group discussion was largely guided by a protocol (Appendix A). Stewart and
Shamdasani (1990) proposed that most interview guides consist of fewer than a dozen
questions. Krueger (1988) suggested that a focused interview include less than 10
questions and often around five or six. The protocol contained four major questions so

that the researchers can develop an in-depth discussion with the participants.

All the questions were arranged in a logical sequence and had a natural ﬂow to them.
“Yes” or “No” questions were avoided and open—ended questions were frequently used. It
started with the definitions of operations capabilities and operations practices. The goal
was to make a clear distinction between the two constructs so that the focus group could
list examples of core operations capabilities and operations practices without confusion.
Then they were asked to brainstorm the relationships among operations practices,

operations capabilities, and operations performance.

Field notes were taken and conversation was tape recorded during the discussion. The
written notes and the tape were used for analyzing the content of the discussion. The aim
was to seek trends and patterns that emerged from the focus group (Kreuger, 1988).
Particularly, the goal was to look for overall opinions of (a) categorization of operations
practices and operations capabilities, (b) intra-relationships of the operations practices set
and the operations capabilities set, (c) the role of operations capabilities in improving
operations performance, and ((1) comment on the tentative framework. At the end of this

phase, the constructs’ deﬁnitions were revised, the tentative framework was reﬁned, and

68

additional insights were collected. All of these became the necessary inputs for the next

phase of the study.

4.2 Survey Development and Validation

To accomplish theory validation (the are from theory to data), the survey method was
employed to collect perceptual data from people knowledgeable in the subject and then
evaluate the presence/nature of the relationships proposed in the tentative model. The
data collection process is broken down into two steps: Section 4.2 covers survey

development and validation, while Section 4.3 addresses survey administration.

Survey research has dominated various data collection methods in empirical research in
operations management (Flynn, Sukakibara, Schroeder, Bates and Flynn, 1990; Scudder
and Hill, 1998). Data collected from survey research are largely perceptual. Starbuck and
Mezias (1996) classified 249 articles published by the Journal of Organizational
Behavior from 1988-1992 and found that 210 present perceptual data. They further
argued that other journals have similar pattern — perceptual data are more frequently used
than archival data. Even though there may be a divergence between archival and
perceptual measures, the correlation between them has been argued to be stronger when
respondents are from higher positions in an organizational hierarchy and when data come
from the same level of analysis (Boyd, Dess and Rasheed, 1993). Therefore, the analysis
was kept consistent at the plant level and operations managers in charge of plant

operations were surveyed to increase the validity of the perceptual data.

69

A tentative survey instrument was developed based on the research model, existing
measures, and the additional information gathered through the focus group study. The
survey instrument can be largely divided into two parts: demographic information and
questions related to the major constructs in the model. The survey instrument was

validated by Q-sort and pre-testing.

4.2.1 Q-sort

While the literature is inundated with measurements for operations practices, operations
performance, and competitive context (discussed in Chapter Three), it has not laid a solid
foundation to scale development for operations capabilities defined in this dissertation.
Therefore, a Q-sort was conducted to pre-assess initial construct validity and reliability
for operations capabilities. The basic concept of Q-sort method is to have experts act as
judges and sort the items into several groups, with each group corresponding to a factor
or dimension based on a pre-determined agreement (Boon-itt and Paul, 2006; Moore and

Benbasat, 1991).

In the Q-sort method, two evaluation indices are normally used to measure inter-judge
agreement levels when observing or coding qualitative/categorical variables: (1) Cohen’s
Kappa (Cohen, 1960) and (2) Moore and Benbasat’s hit ratio (Moore and Benbasat,
1991). Cohen’s Kappa is a robust measure than simple percent agreement calculation
since it takes into account the agreement occurring by chance (Fleiss, 1981). Several
studies have considered a score greater than 0.65 to be acceptable (J arvenpass, 1999; Li,

Rao, Ragu-Nathan and Ragu-Nathan, 2005).

70

In this study, six senior operations management doctoral students in Michigan State
University were given the definitions of all the six operations capabilities constructs and
a list of measures as well. They were asked to assign each measure to a capability based
on the supplied definitions. Their rankings were assessed by Cohen’s Kappa in judging
the inter-rater agreement. Their feedback helped remove and/or reword some of the
confusing items. As a result, respondents are more likely to achieve consistent assessment
on the linkages between items and constructs and increase scale reliability and validity.

The Q-sort protocol is presented in Appendix B.

4.2.2 Pre-test the Survey Instrument

After validating the items for operations capability constructs using Q-sort, a pre-test was
conducted for the full survey instrument in a group of 15 managers. Those managers also
came from the selected corporation, but they were a different group from the focus group.
They were given the survey instruments with a cover letter. The cover letter directed their
attention to certain important areas: time to ﬁll out the survey, clarity of the questions,
necessity of the questions, and key missing questions. They were asked to submit the
completed surveys and written comments on the questions in the cover letter, and make

changes and comments on the survey.

This round of pre-test had three purposes. First, it ensured no ambiguity in the questions
and no different understanding between researchers and potential respondents. Second,
the pre-test served as a cross check of the clarity of measurements as they were proposed
by one group of managers and tested by another group. Third, this was another chance to

modify the survey instruments and the ﬂow of the questions, which helped minimize the

71

possible occurrence of common method bias (This point is further elaborated in Section

4.4.1).

Once the survey instrument was revised, it was transformed into an on-line version using
PERSEUS — an online survey development software. The survey was uploaded on the
server of Michigan State University, which is a secure website. The on-line survey was
the main means for collecting data while a downloadable version was also available
because some organizations’ firewalls may block employees from accessing certain

websites. The finalized survey instrument is presented in Appendix C.

4.3 Survey Administration

The survey administration process was designed with three goals: quantity of the data,
response rate, and quality of the data. That is, the aim was to obtain a large and quality

dataset with a decent response rate.

4. 3. 1 Data Quality

To ensure quality data, the most appropriate respondents must be identified first, and then
an access to them needs to be obtained. Accordingly, the unit of analysis and desirable
respondents are first discussed, followed by soliciting help from a professional

organization who can provide access to such kinds of respondents.

Implications of operations strategy can be discussed at the strategic business unit (SBU)
level, plant level, or functional level. Swink and Way (1995) pointed out a problem in

existing research, that is, the unit of analysis has not always been consistent with the

72

objectives of the research. For instance, operations strategy studies frequently assess
strategy at a functional or plant level while performance is measured at the SBU level. To

avoid this problem, the unit of analysis was kept consistent across all the variables.

A manufacturing plant was set as the unit of analysis in this study because of the
following reasons. First, this is consistent with operations practices literature (Flynn et
al., 1995b; New and Szwejczewski, 1995; Shah and Ward, 2003). Second, the majority of
operations capabilities come from operations processes (i.e., transformation processes
that convert input into output), which can be more easily observed in a simple context,
such as a plant. Third, managers at this level who oversee operations are knowledgeable

and appropriate to answer the research questions.

As a result, the desired respondents were operations managers at the plant level. In this
study, “operations managers” were broadly viewed as those who directly involved in
various activities that are necessary in producing a product or in providing a service (e. g.,
planning, scheduling, performance measurement, procurement/purchasing, or
logistics/warehousing, delegating and supervising the work and activities of others

involved in the operations process).

Based on the prior discussion of the traits of desired respondents, a professional
association was sought which could provide access to knowledgeable and competent
people that fit with the target population. Consequently, APICS was selected as the most
appropriate organization to work with. A detailed research project proposal was provided
to APICS and asked for their collaboration. With the approval, APICS granted the access

to its members who subscribed its semi-monthly e-newsletter. Considering that APICS

73

has members outside the target population (e.g., educators, consultants), a self-screening
question was designed at the beginning of the survey. This can be viewed as another step

to obtain quality data from the target population.

4.3.2 Data Quantity and Response Rate

The quantity of a dataset is affected by the number of potential respondents and the
response rate. A survey with a higher response rate often ends with a large dataset, and
vise versa. Due to the connection between quantity and response rate, the discussion on
these two dimensions is combined together. APICS has 30,000 members subscribe its e-
newsletter, and 60% of them work in the area of operations and planning. There exist a

great number of potential respondents, thus the key is to enhance the response rate.

It is well-known that survey research has been plagued by low response rates (Dennis Jr.,
2003; Larson, 2005; Sivo, Saunder, Chang and Jiang, 2006). Facing this challenge,
different tactics have been discussed to increase response rates in operations management
survey research (Frohlich, 2002). Several tactics have emerged as having a potentially
positive impact on response rates. The most important ones include pre-notiﬁcation/pre-
qualification (Lambert and Harrington, 1990; Yu and Copper, 1983), incentives (Greer,
Chuchinprakam and Seshadri, 2000), support (Larson, 2005), length of questionnaire
(Deutskens, de Ruyter, Wetzels and Oosterveld, 2004), and follow-up (Dillman, 2000). It
is important to recognize that these tactics are not mutually exclusive. Rather they
reinforce each other and are typically used as a set, as recommended by Dillman (2000).

Therefore, this study used a combination of support, multiple types of incentives,

74

multiple waves of delivery, multiple ways of delivery, and moderate length of survey to

improve the response rate to a maximum extent.

First, support can come from multiple sources: professional societies, governments, and
organizations. Larson (2005) found that professional organizations’ support is most
effective to enhance response rates. Therefore, APICS was solicited to support the
research and administer the survey distribution. APICS agreed to announce the survey
study in its semi-monthly e-newsletter delivered to its member subscribers through

emails.

Second, multiple incentives were designed to encourage APICS members’ participation
and to complete the survey. It has been made clear at the beginning of the survey how
important for the potential respondents to complete all the questions to their best
knowledge and even suggested them talking with colleagues for answers they were not
sure. All the respondents have been promised conﬁdentiality in which their individual
names and opinions were not disclosed. However, only the completed ones were
provided with a summary of the project findings and had the opportunity to win a lottery
(a grand prize of a $75 Visa Gift Certificate and ﬁve first prizes of Barnes and Noble Gift
Cards with $25 each). In order to preserve anonymous and be able to get in touch with
the winner, two datasets were created. After the original data were obtained, those
unﬁnished responses or responses with one half of the data missing were removed and
the rest was copied to a new data file. The email addresses in the new data file were taken
out for lottery drawing and sending out executive summary. The rest of the data (without

their contacts) was used for data analysis.

75

Third, multiple waves of survey were used to remind respondents of participation. Two
weeks after the first wave of survey announcement, a reminder was put in the APICS
semi-monthly e-newsletter and sent to its subscribers. Therefore, those who did not have
a chance to fill it out had another opportunity to do it. Additionally, both of these waves

occurred in June. in advance of vacation time for most employees in American firms.

Fourth, though the survey was mounted on the server, respondents were actually given
two options (i.e., fill it out online or download it). This combined use of delivery methods

gave respondents more ﬂexibility and made them feel comfortable.

Lastly, the questionnaire was designed as concise and short as possible. It turned out to
be 12 screen pages. From the pre-test results, the length of time to ﬁll out the survey was
18.35 minutes on average and with standard deviation 1.83 minute. 70 percent of the
respondents finished in less than 20 minutes. All these results showed that the length of

the questionnaire was reasonable.

4.3.3 Survey Delivery Method

Surveys can be delivered through different modes: mail, fax, telephone, personal
interview, and the Internet. There is a current debate regarding the relative effectiveness
and efficiency of online surveys compared with traditional mail surveys (Cook, Heath
and Thompson, 2000; Deutskens et al., 2004; Ilieva, Baron and Healey, 2002; Sheehan
and Hoy, 1999). The resulting evidence is mixed. While some have found that on-line

surveys are more cost-effective, others have found that they can discourage participation

76

by being perceived as “impersonal” and “cold” (Deutskens et al., 2004). The survey was

delivered through Internet for the following reasons.

First, web-based surveys offer appealing possibilities (Cook et al., 2000). People seem to
find the technology easy to use (Parker, 1992). Like a mail survey, electronic surveys can
be completed at the pace the respondents choose. Unlike a mail survey that could be
mislaid easily, an electronic contact remains in place until purposefully deleted (Sheehan
and Hoy, 1999). In a University of Colorado survey, 55 percent of the respondents cited
ease of use as one of reasons they liked most about answering a Web survey (University
of Colorado at Boulder, 1996). As long as people find out how easy it can be done online,

they are more willing to do it.

Second, on-line surveys are less costly, with data being obtained quickly and structured
(Ilieva et al., 2002). Coding errors are significantly reduced. At the same time,
disadvantages of on-line surveys (for example, the unfamiliarity of the new technology)

can somewhat be covered up by a paper version which can be downloaded.

Third, online survey has not been showed to be plagued by missing values. For instance,
a study showed that 69.4 percent of email respondents completed 95 percent of the
survey whereas only 56.6 percent of mail respondents completed 95 percent of the survey
(Schaefer and Dillman, 1998). Furthermore, the email participants provided answers to
open-ended questions with 40 words on average, whereas the mail respondents’ answers
were briefer, with 10 words on average. Data with many missing values have a direct
impact on the quality of the data and data analysis. If an observation is deleted because

some of the answers are missing, it reduces the number of observations used in the data

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analysis. Mean substitution or other techniques to make up for missing values are never

as good as real answers.
4.4 Limitations of Methodology

Even though every effort was made to make the research design a well-thought one,
every study was plagued by certain limitations. In this section, those limitations are

recognized and the techniques to counteract them are reviewed.

4. 4. 1 Common Method Bias

Using self-reporting measures as the primary or sole type of data collection method,
though common in survey type of research, is subject to common method biases.
Common method variance is the variance attributable to the measurement method rather
than to the constructs of interest. Method biases are one of the main sources of systematic
measurement error which threatens the validity of conclusions about relationships

between constructs (Bagozzi and Yi, 1991; Nunnally, 1978; Spector, 1987).

Podsakoff, Mackenzie, Lee, and Podsakoff (2003) provided a comprehensive summary of
the potential sources of method biases and techniques for controlling them. Common
method biases arise from having a common rater, a common measurement context, a
common item context, or from the characteristics of the items themselves. They argued
that method biases are likely to be a substantial problem in studies where the data from
predictor and criterion variables are collected from the same source in the same

measurement context using the same item context and with similar item characteristics.

78

Generally, there are two primary ways to control for method biases — one is through the
research design phase and the other is through statistical control (Podsakoff et al., 2003).
In the research design phase, the key is to minimize the connection between predictor and
criterion variables from contextual cues, specific wording, or question format. Measures
of predictors and criterion can be obtained from different sources. If this is impossible or
infeasible, another potential remedy is to introduce a temporal (e.g., a time lag),
psychological (e.g., cover story), or methodological (e.g., different scales) separation
between the measurement of predictor and criterion variables. Another important issue is
to improve the quality of the scale items to reduce the ambiguity, avoid vague concepts,
and keep questions simple and concise. If little can be done in the research design phase,
it is also helpful to use some statistical remedies to tackle the problem of common
method biases (for instance, Harman’s single—factor test, partial out “common” factors, or

multi-traits multi-methods).

Following the suggestions offered by Podsakoff et al. (2003), the issue of common
method biases was addressed up front in the research design phase. First of all, as
previously noted, the survey instrument was pre-tested by target respondents before it
was posted online. The purpose of the pre-test was to clean up the questionnaire, reduce
the ambiguity of the questions, mix the positively worded items with negatively worded
items properly, and condense the length of the questionnaire. Consequently, items were
presented to respondents without producing artifactual covariance in the observed

relationships.

79

Second, it can be argued that the design features of an on-line survey can help reduce the
exposure and emergence of common method bias. The respondents do not have any
chance to glance through the whole questionnaire and they could only see one or two
questions on each screen. It can be viewed as a distraction from their logic ﬂow when a
new page shows up in front of them. They are also unlikely to turn back to the previous
pages and change their answers. Therefore, they do not build the connection among

questions easily and consciously.

Third, although an effort has been made to minimize common method biases in the
research design, it is extremely difﬁcult to eliminate its unfavorable impact. Statistical
analysis (i.e., Harman’s single-factor test) was done later to evaluate how significant the

problem was.
4. 4.2 Response Rate and Sample Size

Response rate is a major concern of survey research. However, the response rate is hard
to estimate when the survey is delivered via Internet because the true sample size cannot
be accurately assessed. With the lntemet and emails, it is possible for surveys to be
redistributed from one person to another electronically when the initial respondent
believes someone else is more appropriate to ﬁll it out. It is also possible that respondents
skip these emails. In this case, the survey announcement was sent to APICS e-newsletter
subscribers’ email accounts as part of the e-newsletter. However, how many subscribers
read every issue of e-newsletter is unknown. Even those who read frequently may skip
this particular issue or skip the survey announcement part. Under all these situations, a

proportion of the subscribers did not know anything about the survey. Yet it is almost

80

impossible to estimate the real pool of the potential respondents and calculate the

accurate response rate accordingly.

This becomes a problematic issue of using online survey. However, considering its great
advantage (discussed in Section 4.3.3) and the fact that APICS no longer allows
researchers to access its current mailing list, this delivery method is still preferred.
Though APICS past mailing lists are managed by a third party (Infocus) and can be
purchased with a fee, it is appealing to use the most current one to have the correct

contact information.

Besides the response rate, the size of the dataset is another issue. In this study, the desired
number of responses is a minimum of 240 to run Confirmatory Factor Analysis (CFA).
This is based on the estimation of the product of the number of constructs (16), the
minimum number of items for measuring each construct (3), the minimum number of

observations to generate a reliable and convergent parameter estimate (5) (Bollen, 1989).

As previously noted, many tactics were introduced to obtain enough responses from
knowledgeable managers given the time and budget of the research. Yet, in the case of
falling below the minimum requirement, CFA could be conducted for the operations
practices set, the operations capabilities set, and operations performance respectively in
stead of taking all of them in one CFA model. If necessary (the dataset is too small to run
even small size CFA), bootstrap technique could be used to estimate the sampling
distributions of estimates by re-sampling from the original sample with replacement
(Bollen and Stine, 1993). The purpose of bootstrapping is to derive robust estimates of

standard errors and confidence intervals of a population parameter such as a mean or

81

regression coefficient. Therefore. bootstrap technique has been advocated as a method of
internal replication to assess the replicability of results of an individual study (Thompson,
1993). The application of bootstrap is most appropriate in situations where theoretical
assumptions are unlikely to be tenable, or the statistical theory is weak (Bone, Sharma
and Shirnp, 1989; Fan and Thompson, 1998). Once the validity and reliability of the
latent constructs are confirmed in CFA, techniques that are not data demanding could be
used for the statistical tests of research questions (2) to (4), for instance, regression

analysis rather than structural equation modeling.

4.5 Data Analysis Methodology

Data analysis has been divided into two steps. The first step addresses the first research
question with the purpose of validating the measurements of the key constructs. The
second step addresses the rest three research questions regarding the relationships among
the constructs. Significance level a was set at 0.05 to assess the signiﬁcant relationships

in all research questions except question (4).

For research question (1), CFA was employed in EQS to test construct validity (i.e.,
convergent validity and discriminant validity) for the set of operations practice initiatives,
the set of operations capabilities, and operations performance respectively. Another CFA
including the measurements of all operations practice initiatives and operations
capabilities was conducted with the purpose of showing that the two sets are valid and

different construct groups.

82

The rest of the analyses were can'ied out through regression analysis using SPSS. As to
research question (2). the human judgment model was borrowed to evaluate whether the
intra-relationships among the operations practice initiatives set and among the operations

capabilities set are compensatory or additive.

The basic model setup followed Patton and King’s work (1992). The original dataset was
recoded to make the test feasible. For example, are the operations practice initiatives
compensated with each other in improving performance? Two competing models were
established. In the compensatory model, the weakness of some practices can be cancelled
out by other practices. What matters is the average level of all the practice initiatives, and
the average usage of them becomes the independent variable in the model. The
respondent with the highest average usage would be predicted to develop a higher level

of the performance if the compensatory model is valid.

In the additive model, the minimum level (threshold value) use of any practice initiative
put a limitation on the effectiveness of other practices. Theoretically, the higher the
threshold value, the better the performance. Thus, what matters is the lowest evaluation
on all practice initiatives, which acts as the independent variable in the model. Each
respondent was assigned a score corresponding to the lowest evaluation received on all
the dimensions of operations practices. Significant positive relationship between the

score and operations performance would support the additive model.

Essentially, research question (3) concerns the potential mediating effect of operations
capabilities. The potential mediation effect was tested following the standard three-step

approach in regression analysis (Baron and Kenny, 1986; Judd and Kenny, 1981).

83

(1) Show that the initial variable is correlated with the outcome, so there is an effect
that may be mediated. The first step required using operations performance
(either cost, quality or delivery performance) as the criterion variable in a
regression equation and operations practices as predictors.

(2) Show that the initial variable is correlated with the mediator. This step essentially
involves treating the mediator as if it were an outcome variable. In this case,
operations capabilities were used as the criterion variable in the regression
equation and operations practices as predictors.

(3) Show that the mediator affects the outcome variable by using operations
performance as the criterion variable in a regression equation and operations
practices and operations capabilities as predictors. To establish that operations
capabilities completely mediated the operations practices — operations
performance relationship, the effect of operations practices on operations
performance controlling for operations capabilities should not be significantly

different from zero.

The goal of research question (4) is to study how competitive environment inﬂuences the
relationship among operations practices, operations capabilities, and operations
performance. In another word, are the results obtained from research question (3) robust

enough in different market environments?

Two dimensions of competitive environment were examined following the literature:
market competitiveness and market dynamism. The former was measured by the number

of competitors, growth/decline of sales, and price difference among competitors. Low

84

market concentration, slow growth/decline of sales, and marginal price difference among
competitors indicate that the market is more competitive. Market dynamism was
measured by the rate of change of product introduction, processes innovation, tastes and

preferences of customers. The faster the change, the more dynamic the market is.

The whole sample was divided into two sub-samples based on the market competitive
index and market dynamism index. Two sets of models (compensatory vs. additive) were
run and results were compared between different model structures and across sub-

samples.

At this stage, significance level a = 0.1 was used rather than a = 0.05 due to two reasons.
First, the probability of correctly rejecting null hypotheses is reduced as the sample size
decreases (Labovitz, 1968). The standard error varies inversely with sample size.
Consequently, a small difference is likely to be statistically signiﬁcant in a large
database, while with small sample size even large differences may not reach the
predetermined significance level. Therefore, small a (e.g., 0.01 or 0.001) should
accompany large sample size and large a (e.g., 0.10, 0.20) should be used for small
database. As the sample size became smaller due to the split, the signiﬁcance level was

relaxed to p < 0.1 so that few “true” hypotheses were rejected.

Second, Labovitz (1968) pointed out that the selection of signiﬁcance level also depends
on the research purpose — theory developing versus theory testing. If testing well-
reasoned and well-developed hypotheses (like in most conﬁrmatory research settings), it
is logical to select a small level of signiﬁcance so that researchers have a great

confidence of supporting one theory over the others. On the other hand, it is inappropriate

85

to set a stringent significance level in exploratory research where researchers explore a
set of interrelations for the purpose of developing hypotheses to be tested in other studies.
A large significance level is suggested so as to yield more hypotheses — any of which
may be subsequently validated. Having discussed in Chapter Three, the goal of this
dissertation study is to develop theory and generate more specific hypotheses for future
studies. Therefore, it is reasonable and acceptable to choose a relatively lenient

significance level.

4.6 Chapter Summary

This chapter details the methodology in order to carry out the study. The grounded theory
approach is used to develop theory through a focus group study. Survey method is
employed to collect data to validate the relationships among the key constructs. How to
conduct the focus group study, develop the survey instrument, and administrate the
survey study are described followed by the plan for data analysis. The next chapter

reports the results of data analysis.

86

Chapter 5

DATA ANALYSIS AND DISCUSSION

This chapter presents the qualitative and quantitative results generated in this study. It
also discusses the findings and their implications, as they pertain to the key research
questions. As noted previously, the research methodology employed by this study

consists of the following stages:

Focus Group — used to further refine the constructs of operations capabilities and
operations practices and to explore relationships between these constructs and
operations performance.

0 Development and refinement of the survey instrument.

0 Administration of the survey instrument.

0 Analysis of the data generated by the survey instrument.

0 Refined framework.

This chapter uses this ﬂow of activities to structure the presentation of the results.
5.1 Results from the Focus Group Study

After sending out invitation to 25 middle level operations managers from the selected
organization, eight responded and indicated that they were willing to participate in the
focus group. No further attempt to recruit additional members was made since the
member of participants was exactly within the range suggested by (Krueger, 1988). The
discussions followed the protocol, as set out in the preceding chapter, and the resulting

meeting lasted approximately 90 minutes. The focus group was held in December 2005 at

87

the corporate headquarters and took place during an event that allowed the participants to

amend.

The participants were asked to provide their inputs on the definitions of the two core
constructs — operations practices and operations capabilities. They agreed that operations
capabilities are unique ways to do something by extending and modifying ﬁrms’ assets.
For example, the company extended their technology in insulation, which efﬁciently
prevented transfer of heat and saved consumers’ energy bills. Insulation technology is a
physical asset available to every firm. However, it can be combined with other assets
and/or modified to fit with the special needs of a firm in different ways. The corporation
has developed a proprietary process of insulation installation and generated a strong
stream of business with homebuilders. This proprietary process was regarded as a

potential core capability.

Some participants pointed out that operations capabilities are not something that an
organization claims to possess. Rather they must be first demonstrated. So they suggested
adding one more level of speciﬁcity on the existing capability conceptualization, which
would provide a clear judgment rule for firms and their management to ascertain whether
they have a certain capability. Therefore, the definition of “operations capabilities” was
reﬁned as being the demonstrated potential to execute a speciﬁed course of action in

operations in a unique and proficient way.

After consistency on this definition was achieved, the discussion next turned to provide a
list of core operations practice initiatives and operations capabilities. Before that, the

difference between capabilities and practices was emphasized using examples in personal

88

life so that the participants could make the distinctions easily in operations settings. For
instance, “time management” is viewed as a capability for an individual — a person good
at using time (work time and leisure time) efficiently. This could be measured by how
much output (e.g., jobs) the person could generate or process in a given amount of time.
“Make schedules everyday and keep appointments in a palm pilot” are seen as examples
of practice since these activities are used to help people better manages their time. The
critical difference between practices and capabilities lies in that practices are specific,
task/goal oriented, and contextual bounded activities, whereas operations capabilities are
broad-based, context free routines/mechanisms that enable the most efﬁcient use of the
firm’s assets. The former can be observed and imitated easily; whereas the latter are

elusive, intangible, and hard to pin down.

The focus group was next asked to list core operations practice initiatives and operations
capabilities. They came up with 16 operations practice initiatives and 14 operations
capabilities (Table 5.1). In reviewing these two lists, it is interesting to note that there was
a signiﬁcant overlapping between these sets and those contained within the original
framework. Consequently, it was decided to combine some of the items found in the lists

generated by the focus group and to incorporate them into the original framework.

89

Table 5.1 Core Operations Practices and Operations Capabilities

 

Core Operations Practices

Core Operations Capabilities

 

H

A

OO\IO\LII

9

10
ll
12
I3
14

15
16

In terms of the nature of the operations practices set and operations capabilities set, the
majority of participants agreed that ﬁrms do not need to have all of them. More
importantly, deﬁciencies in one or more practices/capabilities can be offset by each other,
which supported the compensatory model. Alternatively, they suspected that some of
them are core and complemented by other practices/capabilities. Both views were against
the additive model where various operations practices/capabilities are equally important
and need to be implemented/developed. The primary reason given by the participants was
that ﬁrms have different competitive dimensions, and they choose different sets of
practice initiatives and operations capabilities to support those competitive dimensions.

Firms do not need to do well on all dimensions; they need to do well on those dimensions

Small lot size production
Information sharing with supplier

Standard work practices
Statistical process control

ISO 9000 and ISO 14000

Feedback collection from customers
Customer training to identify defects
Customer championship

Customers’ complaint analysis

Design for manufacturing

Collaborated new product development
Employee training

Talent development

Performance measurement and
evaluation (daily scorecard)

Supplier certification

Formal communication

Value creation for core customers
Sense of urgency to meet short lead
time

Fulfillment of customers’ orders
Process improvement to make price
competitive

Process standardization
Responsiveness

Dependability and reliability
Intellectual property and know-how
(specialized tooling, technology,
equipment)

Specialization (service experts)
Customization

New product testing facility
Product innovation

Control of the supply chain
Relationships and trust with partners

critical to the successful achievement of their strategic objectives.

90

As showed in Figured 1.1, there is a direct connection between operations practices and
operations performance with the former impacting the latter. There also exists an indirect
linkage between them that goes through operations capabilities. The focus group was
asked to suggest the inter—relationships among operations practices, operations

capabilities. and operations performance.

The focus group first pointed out the direct connection between operations practices and
performance as it was both intuitive and straight forward. It is natural to attribute what a
firm achieves to what it puts into action. Yet, the impact of operations capabilities on
performance could not be seen very easily and clearly. Though it is reasonable to believe
that a firm’s performance relies on its ability to execute activities in efﬁcient ways, the
linkage is not evident due to the subtle and elusive nature of operations capabilities.
However, they showed a great interest in ﬁnding out the exact role of operations

capabilities.

In terms of the relationships between operations practices and operations capabilities, the
focus group recognized that they reinforce each other over time. Capabilities can not
come from nothing in an organization; and they are knowledge gained or drawn from
everyday practices. With capabilities developed over time, an organization can quickly or
easily implement new practices or uses existing practices more efficiently. However, due
to the limitation of empirical analysis techniques, it was hard to incorporate the reciprocal
relationships into the conceptual model for testing. Therefore, only the linkage from

operations practices to operations capabilities was tested.

91

5.2 Q-sort Results

The focus group study helped refine the constructs and provide evidence of the tentative
framework. In order to validate the proposed framework, data were collected through a
large-scale survey. Before the survey was distributed, the theoretical linkages between
the measurement items and the constructs of operations capabilities were ensured through

a Q-sort analysis.

As noted from Chapter Fours, six senior operations management doctoral students in
Michigan State University were asked to assign a list of 25 measures to six capabilities
constructs based on the supplied definitions. The resulting assignment is summarized in
Table 5.2. To assess the degree of inter-rater reliability, the study used Cohen’s Kappa.
Cohen’s Kappa between any two researchers was 0.65 or higher, which was deemed to
indicate an acceptable level of agreement. As can be seen from Table 5.2, most of the
evaluations between raters were generally very consistent with the original design. The
only exception was item #15. Two third of the researchers assigned it to the “wrong”
construct. Consequently, item #15 was removed from the measurement model. Cohen’s
Kappa was computed again, with all of values improving to above 0.70. These results

demonstrated face validity of operations capability constructs.

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Table 5.2 Q-sort Results

 

 

Correct rate
Item # COS PPD RSP IPI RPI PRC IDEAL ( %)
l 4 4 4 4 4 4 4 100
2 2 2 2 2 2 2 2 100
3 3 3 3 3 3 3 3 100
4 6 6 6 3 3 6 6 67
5 5 5 5 5 5 5 5 100
6 4 4 4 4 4 4 4 100
7 2 2 2 2 2 6 2 84
8 3 3 3 3 3 3 3 100
9 6 3 6 6 3 6 6 67
10 2 6 2 2 2 6 2 67
l I 6 3/4/6 4 6 6 6 6 67
12 1 1 1 1 I 1 1 100
I3 3 3 3 3 3 3 3 100
I4 5 5 5 5 5 5 5 100
15 I 2 l 2 1 l 2 33
16 3 3 4 3 3 3 3 84
I7 I I 2 I I 1 l 84
I8 6 6 2 6 6 2 6 67
l9 1 1 I 1 I 1 1 100
20 5 5 5 5 5 5 5 100
21 4 4 4 4 4 4 4 100
22 2 2 2 2 6 6 2 67
23 2 2 6 6 2 2 2 67
24 l l 1 1 1 1 l 100
25 4 2 4 4 2 4 4 67

 

 

Note: COS — Cooperation skills; PPD — proprietary processes development;

RSP — responsiveness; IPI — incremental process improvement;
RPI — radical process innovation; PRC - process reconﬁguration.
IDEAL represents the construct number each item is supposed to measure in

the original design. Correct rate is the ratio of the number of correct answers
and the total number of participants.

Besides item #15, there still existed some level of inconsistency in less than half of the

items. The correction rates were not 100% for items such as #4, #7, #9, #10, #11, #16,

#17, #18, #22, #23, and #25. Consequently, the researchers were asked to explain the

rationale for their assignment on the inconsistent items. The feedback from this

discussion was then used to rephrase the wording of those items. The result was a set of

items that were linked to the underlying constructs and that were worded to minimize the

chance for misinterpretation and confusion.

5.3 Data Collection

An online survey was developed using PERSEUS software and pre-tested by the
managers in the selected corporation before it was uploaded on the university server. The
survey targeted APICS members who are primarily in charge of operations at the plant
level. To gain access to this target group, the survey announcement was sent out through
APICS semi-monthly e-newsletter. There, the potential respondents were invited to
participate in the study and provided the link to the survey webpage

(www.msu.edu/~wuiinhui).

 

5.3.1 Survey Responses

The survey announcement was first sent out on June 6, 2006, followed by another round
two weeks later. After the ﬁrst round, 103 responses were received; and 50 more were
received after the second round by June 30, 2006. According to the APICS membership
2003 directory, there are 2600 members whose primary area of responsibility was
operations. The 153 responses should represent 5.88% response rate. However, this is

only an estimate. As previously noted, the exact response rate was hard to estimate.

Among the 153 responses, 19 responses were considered invalid because they were
almost completely empty. The remaining 134 responses were used in the analysis. In
general, missing values were not a problem since respondents seemed to be very

committed to ﬁnish all the questions once they got started. For those cases where missing

94

values were present, the missing values were replaced using the mean values of those

variables.

If non-response bias can be viewed as a continuum, ranging from fast responders to slow
responders (with non—responders defining the end of the continuum), the comparison
between the first round and the second round respondents can infer the seriousness of the
non-response bias (Armstrong and Overton, 1977). Pair-wise t-tests were carried out
between the two groups on all the questions related to operations practices, operations
capabilities, and operations performance. Overall, 95 percent of the questions showed no
significant difference between the two groups. This means that non-response bias is not a

substantial problem in this study.

In a typical hypothesis-testing research, a researcher wants to control Type I error (or) but
also must be very concerned about Type 11 error ([3). Therefore, the research design must
ensure that power (1- [3) is reasonably high to detect reasonable departures from the null
hypothesis so that a researcher will not accept a hypothesis when it is false. Power is
positively related to sample size. The larger the sample, the more likely a false hypothesis
can be detected (i.e., the greater the power is). However, if there is too much power,
trivial effects become highly signiﬁcant. 80 percent power was suggested by Cohen
(1988) to estimate the required sample size: 196 observations for a small effect size (d =
0.2, null hypothesis is wrong by a small amount), 33 for a medium effect size (d = 0.5),
and 14 for a large effect size (d = 0.8) in one sample t-test. Considering regression was
employed in most of the analyses and t-tests were used to evaluate the signiﬁcance of the

coefﬁcients, Cohen’s suggestion was followed to evaluate the power of this study. Given

95

a usable sample size 134, the power was high (above 80%) for us to detect a medium

effect size but relatively high to uncover a small effect size.

5. 3.2 Demographic Information of the Sample

Respondents’ demographic information was demonstrated from the job title, the number
of years in the position, the number of years in the field, and confidence to assess plant
level issues. Detailed information is presented in the Table 5.3(a), (b), and (c)

respectively.

Table 5.3 Summary of Respondents’ Demographic Information

 

 

 

 

 

 

 

 

(3)
Respondents’ Job Title Frequency
Chief operations manager 4
VP operations 4
Plant manager 7
Director of operations 10
Production supervisor 2
Operations manager 31
Supply chain manager 13
Planning and inventory system manager 19
Others 44
(b)
Years in this Position Frequency Years in the Field of Operations Frequency
Less than one year 24 Less than one year 2
1-5 years 74 1-5 years 15
6-10 years 21 6-10 years 32
Over 10 years 15 Over 10 years 85
(C)
Confidence to Assess Plan Level Issue Frequency
Not at all 0
Somewhat confident 31
Confident 67
Extremely conﬁdent 36

 

96

Respondents were asked to identify their job title from a provided list. If they could not
find in the list, they checked “others” and specified their exact job titles. Among the 44
job titles they provided, 36 of them are entitled as “continuous improvement leader”,
“demand planning supervisor”, “director of quality assurance”, “director/manager of
logistics/supply chain”, “lean coordinator/leader”, “Master planning and operations
systems manager”, and “material control/coordination manager”. Therefore, the
majorities of the respondents work in the area of operations, and were appropriate

informants to answer the survey.

A typical respondent has been in the current position for more than five years and he/she
has worked in the general area of operations for more than eight years. Thus, they have
cumulated enough experience and knowledge to give feedback on the questions in the

survey.

The unit of analysis in this study is at the plant level, and most of the questions in the
survey are related to operations at this level. 77 percent of the respondents claimed that
they have good confidence of assessing plant issue. Overall speaking, the respondents
represented the target population and were capable to address the issues in the

questionnaire.

5. 3.3 Sample Representativeness

Considering the estimated response rate was relatively low (i.e., 5.88%), sample
representativeness was further analyzed from the organizational size and industry aspects.

The survey asked a number of questions to infer the industry each respondent’s business

97

competes, for instance, North American Industry Classification System (NAICS) code,
Standard Industry Classification (SIC) code, major products, and name of the ﬁrm to
which the plant belongs. Among all the options, the majority of the respondents ﬁlled out
the information about their major products. Based on the product description, their
industry information was generalized using three-digit NAICS code. Detailed breakdown
is provided in Table 5.4(a). Except a few in the sectors of utilities, construction,
transportation, wholesales, and professional services, the majority of the business are in
the manufacturing sectors. Among them, their businesses widely spread among
food/beverage, machinery, computer/electronic products, chemical/pharmaceutical

products, electronic equipment, and transportation equipment.

Apart from this, market competitiveness and market dynamism were used as proxies for
industry representativeness. The sample has a good span from more competitive markets
to less competitive markets, from dynamic markets to stable markets (Table 5.4b).

Therefore, the sample has good industry representativeness.

The survey had two questions to estimate the size of the organization: annual sales and
the number of employees. Since there was a lot of missing information on annual sales,
only the number of employees was reported in Table 5.4(c). Apparently, the sample

covers fairy small businesses as well as large businesses.

98

Table 5.4 Sample Representativeness

 

 

 

 

 

 

 

 

(a)
NAICS Code NAICS Title Frequency
22 Utilities 1
23 Constructions 2
31-33 Manufacturing 106
3 1 1 Food 4
312 Beverage and tobacco product 2
313 Textile mills I
314 Textile product mills 2
315 Apparel 1
316 Leather and allied product 1
321 Wood product -
322 Paper 4
323 Printing and related support activities 3
324 Petroleum and coal product -
325 Chemical manufacturing 12
326 Plastics and rubber products 4
327 Nonmetallic mineral product 2
331 Primary metal 4
332 Fabricated metal 11
333 Machinery 12
334 Computer and electronic product 6
335 Electrical equipment, appliance and components 10
336 Transportation equipment 13
337 Furniture and related product 6
339 Miscellaneous (medical devices) 8
42 Wholesales 2
48 Transportation 1
54 Professional services 1
(b)
Market Dynamism Frequency Market Competitiveness Frequency
Dynamic market 59 More competitive market 57
Stable market 75 Less competitive market 77
(C)
Number of Employee Frequency
50 2 X 17
50 < X S 200 37
200 < X S. 500 33
500 < X S 1000 12
X > 1000 18

 

99

5.4 Data Analysis and Discussion

The statistical analyses are presented in the order of the research questions, followed

immediately by the discussion.

5. 4.1 Analysis and Discussion for Research Question I

Research question (I) aims to provide empirical evidence for the validity and reliability

of the operations capabilities set and the operations practices set.

Confirmatory Factor Analysis (CFA) was the technique used to achieve this research
objective. Since 134 usable responses fell below the minimum requirement for running
CFA for all the constructs in one model, CFA was run individually for the operations
practices set and the operations capabilities set. The results of these analyses are

presented in Table 5.5 and Table 5.6 respectively.

In the operations practices set, CFA started with seven practice initiatives, as presented in
the initial framework. However, the factor loadings of “supplier relationships
management” were problematic because the items were found to have stronger
relationships with other constructs, which indicated this construct is not distinct from
other practice initiatives. As the goal of this study is not to identify a comprehensive list
of operations practices but to obtain a set of core practice initiatives to investigate their
relationships with operations capabilities and performance, this construct was deleted in

the further analysis to achieve a higher fit with the data.

l00

 

 

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102

All factor loadings were significant at p < 0.01 with values above 0.61, which indicated
good convergent validity. Discriminant validity was assessed roughly by examining the
matrix of factor loadings and the con'elation matrix for all possible paired combinations
of practice initiatives. Factor loadings of the individual items on their respective factors
were of greater magnitude than with other latent factors in the measurement model. The
correlation between any two constructs was between 0.52 and 0.77. Discriminant validity
was further assessed by using two-factor CFA models involving each possible pair of
practice initiatives, with the correlation between them first set free and then constrained
to one (Bagozzi, Yi and Philips, 1991). In all cases, the x2 value of the unconstrained
model was significantly lower than that of the constrained one. All the evidence
established the discriminant validity of practice initiatives in the study. Cronbach’s alphas
were computed to measure the reliability of each practice initiative (Cronbach, 1951).

With the minimum value of 0.74, the reliabilities of the constructs were ensured.

103

 

 

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105

All factor loadings were significant at p < 0.01 with values above 0.59, which denoted
good convergent validity of operations capabilities. Factor loadings of the individual
items on their respective operations capability were of greater magnitude than with other
capabilities in the measurement. model. The correlation between any two operations
capabilities was between 0.45 and 0.77. Discriminant validity was further assessed by
using two-factor CFA models involving each possible pair of operations capabilities, with
the correlation between them first set free and then constrained to one (Bagozzi et al.,
1991). In all cases. the x2 value of the unconstrained model was signiﬁcantly lower than
that of the constrained one. All of these established discriminant validity among all
operations capabilities in this study. Cronbach’s alphas were computed to measure
constructs reliability. All of them were greater than 0.73; and the reliabilities of

operations capabilities were ensured.

As noted in Chapter Four, survey research is exposed to common method bias. Common
method bias could either inﬂate or deflate observed relationships between items and
constructs. To assess the presence of common method bias, Harman’s single-factor test
(Podsakoff et al., 2003) was applied in a CFA (containing all the constructs of operations
practices and operations capabilities) where all the measurements loaded on one latent
variable. This model generated poor results as indicated by x2 = 1914.32 with degree of

freedom 740, p < .001, BNNFI = 0.57, CFI = 0.59, [F] = 0.59, RMSEA= 0.11.

This model was compared with another first order measurement model where all the
items were linked to one of the core operations practices and operations capabilities they

were supposed to measure. The overall measurement model fit was significantly

106

improved with the following evidences: x2(674) = 999.14, p < .001, BNNFI = 0.88, CFI
= 0.90, IF] = 0.90, RMSEA= 0.06. The comparison of x2 in these two models rejected the
hypothesis of the presence of a single factor accounting for the majority of variance
among the measures. Therefore, common method bias did not seem problematic in this

study.

Given the overall validity of the operations practices set and the operations capabilities
set respectively, CFA involving both sets was done to ensure the distinction between
them. Two competing second-order measurement models were proposed in Figure 5.1
and tested. It is important to note that the number of items and constructs did not match
exactly with those in CFA due to graphic limitation. The left one hypothesizes that all the
operations practice initiatives and operations capabilities reﬂect a single higher level
latent concept (i.e., they could not be distinguished from one another). In contrast, the
right one hypothesizes that all the operations practice initiatives reﬂect a higher order

concept while all the operations capabilities reﬂect another higher order concept.

The left model in Figure 5.1 generated results of x2028) = 1130.14, p < .001, BNNFI =
0.85, CFI = 0.86, IF] = 0.86, RMSEA= 0.06 while the right model produced x2027) =
1075.10, p < .001, BNNFI = 0.87, CFI = 0.88, IF] = 0.88, RMSEA= 0.06. Comparing the
two second-order CFAs based on the ﬁt indices and the change of x2 per change of the
degree of freedom (sz/Adf), it is apparent that the left hand side model did not ﬁt as well
as the right hand side model. That is to say, the practice initiatives set and operations
capabilities set do not seem to come from one higher level concept, but more likely from

two concepts.

107

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108

The distinction between the operations practice initiatives and operations capabilities can
also be demonstrated by examining the correlations among them. As presented in Table
5.7, all of the correlations were signiﬁcant at p< 0.05. Given that the study uses
perceptual data, it is not surprising to see moderately strong correlations among
constructs. However, none of the correlations were high enough (above 0.90) to threaten
the validity of the constructs. Consequently, there was no need to combine any two into

one COIISII'LICI.

Table 5.7 Correlation Matrix of Core Operations Practices and Core Operations

 

 

 

 

 

 

 

 

 

 

 

 

 

Capabilities

COS PPD RSP IPI RPI PRC QMP JFP COP IPD WDP LDP
COS l
PPD 0.55 l
RSP 0.45 0.45 l
IPI 0.59 0.45 0.35 l
RPI 0.46 0.5 I 0.40 0.67 l
PRC 0.67 0.66 0.47 0.70 0.58 l
QMP 0.26 0.53 0.4l 0.49 0.45 0.47 l
JFP 0.48 0.50 0.35 0.65 0.46 0.63 0.65 l
COP 0.45 0.49 0.27 0.42 0.29 0.59 0.53 0.6l l
IPD 0.49 0.41 0.26 0.43 0.35 0.55 0.49 0.59 0.62 l
WDP 0.4! 0.36 0.24 0.58 0.45 0.52 0.65 0.77 0.58 0.60 l
LDP 0.59 0.42 0.25 0.50 0.37 0.57 0.58 0.66 0.65 0.54 0.76 l

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Note: COS — cooperation skills; PPD — proprietary processes development;
RSP — responsiveness; IPI — incremental process improvement;
RP] — radical process innovation; PRC — process reconfiguration.
QMP — quality management practices; J FP - J IT ﬂow practices;
COP — customer orientation practices; IPD — integrated product development
practices; WDP - workforce development practices; LDP - leadership practices.

After establishing the construct validity of the operations practice initiatives and
operations capabilities, all the measures of each construct were averaged and single

measure for each construct was used in the regression analysis.

109

5.4.2 Analysis Results and Discussion for Research Question 2

Research question (2) addresses the nature of the relationships among the core operations
capabilities set and the core operations practices set. Two competing models were tested
— compensatory model and additive model. The former one argues that operations
capabilities (or operations practice initiatives) can be compensated by each other and the

latter one suggests that a minimum level is needed to produce good performance.

5. 4.2.] Analvsis Results

 

Following the setup of human judgment models (Patton and King, 1992), the original
dataset was recoded to test the two types of models. Specifically, as the average level of
all the operations capabilities (or practice initiatives) was hypothesized as a predictor to
operations performance (denoted as “PERF”) in the compensatory model, new variables
were generated to reﬂect the average value of operations capabilities (denoted as
AVGCC) and the average usage of practice initiatives (denoted as AVGPR) of each
respondent. In contrast, the lowest value of all operations capabilities (or practice
initiatives) was assumed to determine operations performance in the additive model. New
variables were generated to represent the minimum value of each respondent’s answers to
all types of operations capabilities (denoted as LOWCC) and practice initiatives (denoted

as LOWPR) respectively.

Operations performance contained three constructs: cost, quality, and delivery. Each
construct was measured using multiple items. Conﬁrmatory factor analysis was done to
establish construct validity and results are summarized in Table 5.8. These three

dimensions of operations performance served as the dependent variables.

110

Table 5.8 Measurement Model for Operations Performance

 

Constructs and Items Standardized t-value
Coefficient

 

Relative to your competition, how would you rate the
performance of your plant operation on the following
dimensions of performance?

Cost (Cronbach’s Alpha = 0.88)

Manufacturing unit cost 0.81 10.75
Manufacturing overhead cost 0.82 11.05
Total cost (acquisition, setup, maintenance, service, etc.) 0.90 12.65

Quality (Cronbach’s Alpha = 0.89)

Product conformance 0.89 12.66
Product reliability 0.91 13.19
Product features 0.79 10.65
Delivery (Cronbach’s Alpha 2 0.84)
Delivery accuracy 0.83 10.99
Delivery dependability 0.77 9.89
Delivery quality 0.84 11.2
Delivery availability 0.60 7.19

Model Fit: x262) = 57.82, p < .001, BNNFI = 0.95, CFI = 0.97, [F] = 0.97,
RMSEA= 0.08

 

To examine question (2), a series of regressions were run in SPSS between average
operations practice initiatives (AVGPR) and the three dimensions of operations
performance. Then another set of regressions were run between the minimum level of the
practice initiatives (LOWPR) and three dimensions of operations performance. The
coefﬁcients of determination (R2) and the regression coefﬁcients ([3) are summarized in

Table 5.9.

111

Table 5.9 Regression Results of the Nature of the Relationship between Operations
Practice Initiatives and Performance

 

 

 

 

 

 

 

 

 

Model Type Dependent Variable

Independent Variable Cost Quality Delivery
ADDITIVE LOWPR

R2 0.17 0.14 0.02

B 0.41* 0.37* 0.15
COMPENSATORY AVGPR

R2 0.17 0.13 0.06

[3 0.41* 0.36* 0.25*

 

 

 

 

 

 

 

Note: * denotes p < 0.05.

Similarly, regressions were run between the average operations capabilities (AVGCC)
and three dimensions of operations performance. Then another set of regressions were
run between the minimum level of the operations capabilities (LOWCC) and three

dimensions of operations performance. The results are reported in Table 5.10.

Table 5.10 Regression Results of the Nature of the Relationships between
Operations Capabilities and Performance

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Model Type Dependent Variables

Independent Variable Cost Quality Delivery
ADDITIVE LOWCC

R2 0.12 0.03 0.04

[3 0.35* 0.18* 0.20*
COMPENSATORY AVGCC

R2 0.21 0.08 0.04

(5 0.46* 0.28* 0.20*

 

 

 

 

 

5. 4. 2.2 Discussions

In terms of the intra-relationships among operations practice initiatives, Table 5.9 showed
that both compensatory and additive models appear to have approximately the same
explanatory power on cost and quality dimension of performance. Actually, the additive

model is more resource demanding than the compensatory model because it requires

112

resource investment in all kinds of operations practice initiatives. Given the fact that both
models fit with the data equally well, it is the additive model that determines decisions
regarding practices implementation. That is, firms have to use all the practice initiatives

to a certain level to improve the competitiveness in cost and quality.

Yet, the compensatory model outperformed the additive model in explaining firm’s
delivery performance. That is to say, to improve delivery performance, firms do not need
to adopt all the practice initiatives. Rather they can be effective on certain areas to

compete well in the market.

Going back to the data, it can be seen that firms tend to implement all the practice
initiatives at the same level, no matter how intensively they use them. Thus, the one with
high average usage of all the practice initiatives has a high minimum usage as well; while
the one with low average usage of all the practice initiatives tends to have a low
minimum usage. That could be the reason to give both models approximately the same

explanatory power.

As to the nature of the relationships among operations capabilities, the results in Table
5.10 indicated that the compensatory model outperforms the additive model in explaining
a firm’s cost and quality performance and the two models tie in explaining delivery
performance. That can be interpreted as that firms can improve the ﬁrst two dimensions
of performance by developing various types of operations capabilities. What matters is
not a “threshold” that needs to be met by all the operations capabilities. Rather firms can
develop different capability portfolios, which could render them the same competitive

advantage. If a ﬁrm is weak at one dimension, it could still competes well in the market

113

as long as it can excel at other capabilities. This is an evidence of showing the presence

99

of “equifinality . Equifinality, as introduced by Hambrick (1984), recognizes that there
are multiple paths available for firms and their management teams to reach a desired

812116.

Combining the results above, it is interesting to find that the nature of relationships of
operations practice initiatives and that of operations capabilities are quite different in
terms of their impact on a firm’s operations performance. Implementing all kinds of
practice initiatives is a necessary condition to improve cost and quality performance,
whereas it is not such a condition for operations capabilities. Put alternatively, there
could be a variety of ways to configure a ﬁrm’s operations capability portfolio, but fewer
choices for the practice initiatives. The results, on one hand, conﬁrmed the conclusion
gained from the Collins et al. (1996) study, where the researchers found that operations
practices have to be put in place in all areas to build long-term sustained performance.
Yet, on the other hand, the results also indicated that firms have great ﬂexibility in

building operations capabilities to be equally successful.

5.4.3 Analysis Results and Discussion on Research Question 3

After examining the nature of the relationships among operations practice initiatives set
and operations capabilities set individually, both sets were brought into one model with
the purpose of identifying the relative roles of each set in enhancing operations

performance.

114

5.4.3. .1 Analysis Results

 

Figure 1.1 suggests that the operations capabilities set could mediate the relationship
between the practice initiatives set and operations performance. The three-step regression
approach was then used to detect the potential mediation described in the Chapter Four.
As noted previously, operations practices and operations capabilities can be either
compensatory or additive. Therefore, the potential mediating effect was tested under both
scenarios. Results of the compensatory scenario and additive scenario are summarized in
Table 5.11 and Table 5.12 respectively. Adjusted R2 was reported in regressions with
more than one independent variable. Regression diagnostics were performed and there
was no substantial evidence of multicollinearity, heteroscedasticity, and omitted variables
in the regression model. Therefore the results obtained using ordinary least square

method are unbiased and reliable.

Two things were evident after testing the proposed model under the two scenarios. First,
the compensatory model worked better to explain the difference in ﬁrms’ cost and
delivery performance (in terms of how much variance of dependent variables can be
explained by the independent variables). This implied that firms have a greater freedom
to implement various practice initiatives and develop different operations capabilities to
be equally successful in both performance dimensions. However, additive and
compensatory models worked similarly well in explaining the quality performance,
which indicated that a minimum level of every core practice initiative is crucial to

provide quality products or services.

115

Second, capabilities played a full mediation role in explaining the cost performance. That
is to say, the usage of practice initiatives has to be transformed into a set of unique
capabilities to reduce cost. However, the intensive use of all practice initiatives still
played the major role in explaining quality performance while the role of capabilities was
not substantial in the relationship. Unfortunately, neither practices nor capabilities could
explain delivery performance well (as indicated by R2 = 0.07). Even though the linkage
between operations practices and performance was significant, the explanatory power of
the full model was weak, as only seven percent of variance in operations performance can

be traced down to practice initiatives implementation.

116

 

 

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117

5. 4. 3. 2 Discnssimzs

 

The reported results appeared to indicate that there are three situations that need to be
explained separately. First, why do operations practice initiatives have an indirect impact
on cost performance through operations capabilities? Second, why do operations practice
initiatives have a direct impact on quality performance? Third, why neither practice

initiatives nor operations capabilities inﬂuence delivery performance substantially?

The answers to the first question lay in the measurement of cost, the nature of operations
practices and operations capabilities. Cost in this study was actually a comprehensive
measurement of performance, because it included manufacturing unit cost as well as total
cost. The total cost covers both the direct purchasing cost and the hidden cost associated
with using a product or service, for instance, cost associated to setup, maintenance,
service, and operating. Therefore, the total cost actually embraces other dimensions of
performance implicitly into cost. For instance, it is hard to imagine a low total cost if a
product has poor quality. Though the purchasing cost could be low for the product, the
maintenance cost could be extremely high and eventually end up with a high total cost. In

return, competitiveness in “total cost” could also mean high quality.

From a trade-off perspective, improving quality performance or delivery performance
could come at the price of losing the competitiveness in cost. Though there are ﬁrms that
could manage to solve the tradeoff thanks to the advert of new technology and innovative
management philosophy, the pressure of cost increases still exists. How ﬁrms overcome
the tradeoff impasse could not be traced down to the practices they use. There are at least

two reasons. First, practices are specific and task—oriented activities with a very detailed

118

goal. However, the performance is measured at a high level in terms of cost, quality, and
delivery. It is difficult to build up the one-to-one correspondence between specific

activity and broad performance.

Moreover. practices can be identified, communicated, and disseminated easily. This
nature of practices indicates they can be followed quickly and the potential rent coming
from adopting the practices can be exploited rapidly and will not sustain. In contrast,
capabilities are long lasting due to the nature of elusiveness and uniqueness. Firms
develop their capabilities inside their organizations through various activities and
practices in everyday operations over a long period of time. Once they are established,
they tend to have a long time inﬂuence on performance, as they are hard to be identified,
articulated, and decoded. Therefore, operations capabilities generate a strong mediation

effect between operations practices and cost performance.

The second question concerns why operations practices have a direct impact on quality
performance. It can be argued as follows. The signiﬁcance of quality in business led
many organizations to conclude that effect quality management can enhance their
competitive abilities (Anderson, Rungtusanatham and Schroeder, 1994). Since Japan’s
leading industrialists instituted Edwards Deming’s quality control methods, Japanese
quality, productivity, and competitive position have been improved and strengthened
enormously (Buffa, 1984; Garvin, 1984; Juran, 1981; Riggs and Felix, 1983). Deming’s
idea has quickly spread in the USA. afterwards. In 1988, the US. government
established the Federal Quality Prototype and the Malcolm Baldrige National Quality

Award was established in 1995. Ferdow and De Meyer’s (1990) “sandcone” model even

119

suggested a path to improve performance where quality lays a solid foundation for

improving other dimensions of performance and improving performance continuously.

As quality holds such a critical position in competition, it has been studied intensively for
more than half a century. Improving quality has been emphasized for long in practices.
Juran (1986) described quality management with three elements: quality planning, quality
control, and quality improvement. However, he found that often a very low priority is
given to planning and improvement while top priority and resources are given to control.
With the tremendous growth of quality literature in both academic and practitioner-
oriented journals, the term quality management has been diluted to mean different things
and the scope of activities underlying it lacked consensus (Watson and Korukonda,
1995). Yet, it is widely believed that the underlying practices in quality management are

fundamental and essential for effective management (Nair, 2006).

Over time, the tacit knowledge of managing quality has become increasingly more
explicit. Guidelines and procedures have been documented to facilitate the articulation of
the tacit knowledge and the communication of quality improvement. Trainings are given
from the top management team to the frontier operators about the commitment,
philosophy, tools, and teamwork. Tools, such as process ﬂowcharting, scatter diagrams,
Pareto analysis, cause and effect analysis, control charts, become standardized. All of
these help convert operations capabilities into operations practices, which reinforces the

direct relationship between practice initiatives and quality performance.

In addition, quality means quality assurance for many companies because quality

organizations stress quality control and assurance (Spencer, 1994). Quality performance

120

links directly to quality assurance approach — a systematic approach to the pursuit of
quality (Collins, 1994). The purpose of quality assurance is the conformance of products,
services and processes with given requirements and standards (Crosby, 1979; Moreno-
Lonzo and Peris, 1998). This conformance is achieved through systematic measurement
and control to detect special causes of variation and achieve process standardization
(Dale, Boaden and Lascelles, 1990). Quality assurance includes, and is an extension of,
quality control (Garvin, 1988; Moreno—Lonzo and Peris, 1998). If quality is the degree to
which an item or process meets or exceeds the user's requirements, then quality assurance
is those actions that provide the conﬁdence that quality was in fact achieved. Essentially,
the goal of quality assurance is to meet the standard and be on target through usage of
tools, methods, and training, in another word, practices. Alternatively, quality

performance can be achieved through using quality management practices.

Finally, the findings strongly indicated that neither operations practices nor operations
capabilities are good indictors to explain delivery performance. Two explanations for this
situation exist: (1) there may be more than one homogeneous group, with the result that
the relationships are disguised when the whole sample is used for the test; and (2)
delivery is rarely an independent performance variable. Few firms would only consider
delivery performance without the care of quality and/or cost. It is likely that delivery is
bundled with other performance dimensions. Undoubtedly, more research is required to

answer this question.

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5.4.4 Analysis and Discussion on Research Question 4

Research question (4) suggests that the pattern of the relationships found out in question
(3) may be contextual. That is, the competitive context could moderate the key
relationships. To explore this possibility, two dimensions of competitive context were

investigated — market competitiveness and market dynamism.

5.4.4.1 Analysis Results

 

Market competitiveness was measured by the number of competitors in the market,
growth/decline of sales, and price difference among competitors. Market dynamism was
measured by the rate of change of introduction of new product, new processes, tastes and
preferences of customers. For each construct, an exploratory factor analysis was done to
verify the validity of the measures. Then the measures of each construct were averaged to

form an index.

The whole sample was split into two subgroups by the mean values of market
competitiveness index and market dynamism index respectively. Firms were viewed as
competing in more competitive markets when the competitiveness index (MKTCOM)
was no more than 3.8. This group consisted of 57 firms. The remaining 77 were viewed
as being in less competitive markets. Similarly, firms were viewed as operating in more
dynamic markets when the dynamism index (MKTDY) is no less than 3.7 (59 firms were
assigned to this subgroup). The remaining 75 were viewed as being in more stable

markets.

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Table 5.13 and Table 5.14 contain the results of the interrelationship among operations
practices, operations capabilities and performance for ﬁrms in more competitive markets

and in less competitive markets. There are three observations based on the results.

First, the compensatory model dominates the additive model in explaining all dimensions
of performance in both situations. That is largely consistent with the results found in the
previous subsection, which also provides empirical support for the concept of
“equiﬁnality”. That is, firms have greater freedom to manage their operations practices
portfolio and operations capability portfolio regardless whether they are in more or less

competitive markets.

Second, operations capabilities play a significant mediating role between operations
practices and all three dimensions of performance in more competitive markets; while

operations practices play a more critical role in less competitive markets.

Third, the pattern of the relationships among operations practices, operations capabilities,
and operations performance under more competitive markets and less competitive
markets exhibits significant difference. This inconsistency strongly infers that market

competitiveness moderates the key relationships among them.

123

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between operations practices and cost performance, but not quality and delivery
performance in dynamic markets. In stable markets, ﬁrms that implement every
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5. 4. 4.2 Discussions

 

These findings in Table 5.13 and Table 5.14 lead to the following question: Why firms in
more competitive markets draw upon operations capabilities to improve performance
whereas firms in less competitive markets tend to rely on operations practices? The

nature of two markets needs to be further discussed to answer this question.

Based on the measurement of “market competitiveness”, it can be seen that those
competitive markets share similar features with mature markets. One of the features is
that the whole market is like a fixed “pie” as it does not grow strongly. As a result, one’s
market share increase occurs largely at the expense of the other’s lose. Under such
pressure, firms tend to learn from their competitors and adopt various kinds of practice
initiatives as they are easy to follow and exploit the beneﬁt as much as possible.
However, the benefits of practices are like low-hanging fruits and will not sustain. When
all the competitors replicate what the leader does, the economic rent of operations
practices goes away and therefore its role becomes quite marginal. What really
differentiates one from the other is operations capabilities a firm cultivates while
implementing these practice initiatives and learning by doing. Operations practices could
be the same across firms, but the transformation processes could be distinctive due to
different strategic priorities, different types of organizational culture, and different
experience of implementing other practice initiatives. That is to say, firms end up
possessing their own abilities to handle the operations; and the level of operations

capabilities have a substantial effect on performance.

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In the less competitive market. firms do not have such a pressure to follow the leaders as
they can survive anyway. Consequently. they can afford to spend more time to identify
operations practice initiatives that work best for them, take time to implement them and
digest them. They can enjoy the benefits of operations practices longer just as if one
squeezes water from a sponge slowly. Since operations capabilities are developed even
slowly in the process, the impact is not as straightforward as that of operations practices.
Respondents in this type of markets may not even aware of the subtle effect of operations

capabilities.

The findings reported in Table 5.15 and Table 5.16 indicated that firms can differentiate
themselves from competition in more dynamic markets and there is no “right” path to
improve all dimensions of operations performance. Given a market with frequent change
of customers’ taste, product innovation, and process innovation, firms need to adjust their
practices or capabilities with the pace of market change and strategy change. This is
particularly true for operations practice initiatives where each one has a very speciﬁc goal
and not all of them are relevant under all the circumstances. For instance, JIT/Lean
practices have been widely adopted for inventory management and control, yet it is more
effective in the mature phase of a product life cycle (Chase, Jacobs and Aquilano, 2005).
Using all the practice initiatives will not guarantee enhanced performance, what matters

is to use and excel at the appropriate operations practices and operations capabilities.

On the contrary, a norm may be formulated in stable markets about what operations
practices and/or capabilities a firm should have. Customers in those markets get enough

time to understand the manufacturer’s practices and capabilities and the norm in that

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industry. Doing poorly on one of them could threaten its whole competitiveness and the

weakest point largely determines a firm’s performance.

In addition, the results showed that operations practices and operations capabilities play
different roles in enhancing different performance dimensions. Operations capabilities
fully mediate the linkage between operations practices and cost performance in more
dynamic markets. Under all other situations, the impact of operations practices on

performance is substantially stronger than that of operations capabilities.

In more dynamic markets, firms have to compete by continuously introducing new
products. At the initial stage, product success (in terms of quality, features, functions, etc)
is critical to take a lead in the market. As new products are imitated and produced by a
number of competitors, process innovation becomes critical to lower production cost and
increases market share. For example, High definition TV has gone through many
innovation stages such as Liquid Crystal Display (LCD), Digital Light Processing (DLP),
and Plasma. However, the majority of consumers will not afford it unless the price drops
continuously. In such a market, a firm has to consider cost to further expand its market
share. This can be effectively achieved by modifying its existing production and planning
process, collaborating its functional areas and with its suppliers, and/0r reconfiguring its

resources. A generic practice initiatives set could hardly have an impact.

5.5 Refined Framework

Starting from a theoretical framework that was subsequently refined by the findings
generated by the focus group study, a revised framework was developed. This

framework was then used as the foundation for generating a survey. The survey was next

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evaluated and used to collect data from appropriate respondents. The empirical results
presented in section 5.4 indicated what could be best described as mixed results. In an
attempt to improve the explanatory power of the underling theoretical framework, a
refined framework was provided — one that addresses the relationships among operations
practices, operations capabilities, and operations performance under different strategic
needs and competitive environment. The refined framework was potentially attractive

and desiring of future research.

The results from subsection 5.4.3 indicated that operations capabilities could mediate the
relationship between operations practices and performance if a ﬁrm intents to excel at the
market in more than one dimensions. In contrast, operations practices still play a
significant role when a firm only focuses on improving performance in one dimension.
The reason is that the task is very clear and specific under a single goal so that a ﬁrm can
identify the appropriate practice initiatives to reach the goal. However, multiple goals
require a great number of practice initiatives and many of them are intertwined and even
contradictory. Therefore, it is hard to choose what to implement given the limited amount
of resources. Under this circumstance, what a firm could rely is its ability to extent the
usage of its assets, push the assets frontier outward, and eventually leave the firm enough
room to overcome the trade-off tension from multiple goals (Schmenner and Swink,

1998).

The contingent role of competitive environment has been confirmed in this study. The
results in subsection 5.4.4 suggested the necessity to consider that construct in operations
strategy research. In this study, the results showed that operations capabilities play

mediating role in more competitive markets where the impact of operations practices still

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dominates in less competitive markets. Additionally, operations capabilities and
operations practices bear the compensatory nature in more dynamic markets but additive
nature in more stable markets. The reasons have been discussed, but future studies are

needed to test the validity of the conclusion.

5.6 Chapter Summary

This chapter summarizes the major empirical findings on which this study was based.
Discussions are provided pertaining to each key research question. The most important
finding is that the key relationships among operations practices, operations capabilities,
and operations performance depend on strategic needs and business environments. The

original framework is revised and the refined framework is suggested for future research.

Chapter 6

CONCLUSION AND FUTURE RESEARCH

This study can be viewed as an exploratory investigation in operations strategy area that
has explored, developed, and assessed two central concepts — operations practices and
operations capabilities. Further, this study has explored the role of these two constructs
in explaining operations performance. In reviewing the literature, theoretical frameworks,
research methodologies, and empirical findings reported in the preceding five chapters,
conclusions are made and the major contributions of this study are identiﬁed.
Furthermore, this chapter draws on the insights gained during the execution of this study

to present directions for future research.

6.1 Conclusions

First, the study has provided clear definitions for operations practices and operations
capabilities that not only identify them uniquely but also distinguish them from one
another. With these definitions, several core operations practice initiatives and operations
capabilities were identified and their validities were tested through data collected from a
survey. The reported results have shown that operations practices and operations

capabilities are distinct but correlated constructs.

Second, the study has examined the intra-relationships among the core Operations
practice initiatives set and the core operations capabilities set separately by testing two
competing models. The two competing models reflect two types of nature of the intra-

relationship: compensatory and additive. The study found out that the nature of the

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relationships among operations practice initiatives is quite different from that among
operations capabilities. For instance, fimis were more likely to improve cost and quality
performance through adopting various kinds of operations practice initiatives and
implementing them at a similar level, whereas firms had greater freedom in cultivating
their operations capabilities to strengthen the competitiveness in these two performance
dimensions. The latter offered another piece of empirical evidence for the concept of
“equiﬁnality.” That is, firms do not need to develop the same set of operations
capabilities to develop competitive advantages in the market place. Given limited
resource, they can choose different strategies by conﬁguring their own portfolios of
operations capabilities. With “equifinality,” differentiation, not imitation, is the key to

SUCCESS.

Third, the study has investigated the contribution of operations practice initiatives and
operations capabilities on operations performance simultaneously. The reported results
have shown that operations capabilities play a mediating role in the linkage between
operations practice initiatives and cost performance. Consequently, these ﬁndings
showed that operations capabilities should not be omitted when studying operations
practices and their impact on performance. In addition, the results have shown that ﬁrms
can achieve improved performance by conﬁguring the composition of the core operations
capabilities set and operations practice initiatives set in their own ways. It is interesting to
note that the intra-relationships among the operations practice initiatives set has changed
from additive when it was studied alone to compensatory when it was examined with
operations capabilities. This is also an example to show that a misspecification of a

model could produce misleading results.

135

Last, the study has evaluated potential moderators that could change the sign and
magnitude of the relationships among operations practices, operations capabilities, and
operations performance. The goal was to identify alternative speciﬁcation that enhances
the explanatory power of the model. Market competitiveness and dynamism were
specifically examined. The findings have shown that operations capabilities play a
significant mediating role in more competitive markets whereas operations practices are
more effective in less competitive markets. Under both scenarios, what matters is not
whether a firms have everything in place but whether a firm is good at something. The
results also showed that firms are free to conﬁgure the portfolio of operations practice
initiatives and operations capabilities in more dynamic markets; whereas firms tend to
follow a similar portfolio in more stable markets. Overall, the ﬁndings have established
the moderating roles of market competitiveness and market dynamism in the key

relationships.

6.2 Theoretical Contributions

Grounded on the conclusion of this study in the prior section, the theoretical contributions

are discussed from five aspects.

First of all, this study is the first to explore the extent to which operations practice
initiatives and operations capabilities are compensatory or additive in nature. Given
limited resources in every organization, this is such a critical issue since an inappropriate
decision here can lead to firms investing resources in areas that may not generate the best
possible returns. The existing literature is inundated with articles examining various kinds

of practices initiatives individually or in a bundle (Boyer et al., 1997; Callen et al., 2000;

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Cua et al., 2001; Dean and Snell, 1996; Flynn et al., 1995a). The results of this study
have shown that practice initiatives have to be studied in an even broader context because
they are largely not compensatory. In contrast, weakness in some of the operations
capabilities can be compensated by strength in other operations capabilities. As long as a
firm develops certain strong capabilities in those areas critical to its success, it is more
likely to be successful. This trait allows firms to be more focused when investing

resources in building specific operations capabilities.

Secondly, this study is one of the first studies to rigorously study the linkages between
operations practices, operations capabilities, and operations performance. It is also one
of the first studies to examine the process by which operations practices are transformed
into operations capabilities. Operations practices and capabilities can be viewed as
offering two options from which a firm can choose to improve performance. Yet, they are
neither isolated nor parallel. As shown by the results in Chapter 5, operations practices
have a positive impact on cost performance primarily through operations capabilities.
This relationship implies that learning takes place from implementing operations
practices over time. This learning contributes to the building of valuable capabilities that
are hard to be imitated, which should be the focus of operations strategy. The mechanism

can be observed clearly from Toyota Production System (TPS).

TPS has long been known for its kaizen (i.e., continuous improvement). The cycle of
kaizen activity is generally defined as Plan, Do, Check, and Act (PDCA) (Shewhart,
1939). To facilitate continuous improvement, TPS set up four rules (Spear and Bowen,

1999). That is, (1) All work should be highly speciﬁed as to content, sequence, timing,

137

and outcome; (2) Every customer-supplier connection must be direct, and there must be
an unambiguous way to send requests and receive responses; (3) The pathway for every
product and service must be simple and direct; and (4) Any improvement must be made
in accordance with the scientific method, under the guidance of a teacher, at the lowest
possible level in the organization. As a result, all line personnel in Toyota have learned to
stop their production line in case of any abnormality and initiate suggestions for process
simplification, process standardization, and process improvement (Imai, 1986). Even
though the competitors can imitate Toyota’s practices, the learning and building-up
capabilities take time to show impacts on performance. Therefore, Toyota is still enjoying

the competitive advantage in the automobile market.

Thirdly, from a strategic perspective, a firm has to decide how and where to invest its
resources. The results from this study have shown that the process of resource investment
is strongly contingent on the desired area of performance improvement. Investing in
operations practices was critical if the goal was to improve quality and delivery
performance. In contrast, investment in operations capabilities was preferable if the intent
was to improve cost performance. As discussed early, while quality and delivery were
independent performance dimensions, cost performance, as operationally measured in
this study, was not simply manufacturing unit cost. It also incorporated some elements of
quality into it. Consequently, the results indicated that operations capabilities may play a
critical role in improving performance in multiple dimensions. The findings were actually
in line with the broader framework of the resource-based view of the ﬁrm which
emphasizes the role of capabilities (Barney, 1991; Barney, 1995; Dierickx and C001,

1989; Gagnon, 1999).

138

Fourthly, the reported results underline the importance of the competitive environment in
operations strategy framework. Firms do not compete in a vacuum space. Consequently,
management must craft the firm’s strategy so that it is suitable with the realities of the
firm’s competitive environment. The relationships among competitive environment, the
use of operations practice initiatives, and the development of operations capabilities were
clearly evident from the reported findings. What works well in one type of market
structure may not work the same way in another type. For example, operations
capabilities have been shown to significantly mediate the impact of operations practices
on performance in more competitive markets whereas operations practices were still
more effective in less competitive markets. These ﬁndings emphasize the contingent

nature of operations strategy.

Lastly, the results of the study also provided additional empirical supports for the concept
of “equifinality”. “Equifinality” is an important concept from a strategic perspective
(Hambrick, 1984; Miles, Snow, Meyer and Coleman Jr., 1978; Van de Ven and Darzin,
1985) — a desired outcome can be reached through different approaches. Consequently,
the notion of there being one “best” way does not seem very useful and appropriate.
Rather, through extension of the findings, it becomes evident that ﬁrms have available to
them different portfolios of alternative conﬁgurations of operations practices and

capabilities to enhance performance.

6.3 Contributions for Management Practice

While directly and primarily towards academic researchers, the findings presented in this

study are also of potential importance to managers working in the business world. First,

139

the results suggest that practitioners should carefully select a narrow span of strategic
practice initiatives and operations capabilities in which to invest. The cost of investing in
a full range of practices and capabilities may outweigh the beneﬁt. More speciﬁcally, the
findings emphasize the need for strategic differentiation. That is, firms should invest

only in those capabilities that help differentiate themselves from the competition.

Second, this study points to the notion of operations strategy as a system of decisions -
something first noted by Skinner (1969). Operations strategy must be studied as a
system. Management must recognize that many factors combine to generate the total
impact. Some of these factors can be actively inﬂuenced by management (such as
operations practices and operations capabilities), while other factors should be considered
as those over which the firm may have very little control (such as competitive context).

Omitting one critical factor may result in inappropriate decisions.

6.4 Limitations

No study is perfect; every study suffers from varying extents of limitations. Some of the
limitations regarding research methodology have already been recognized in Chapter
Four (section 4.4). In this section, the major limitations from the aspect of theoretical

consideration and their implications are summarized.

The first limitation stems from the very nature of the relationship between operations
practices and operations capabilities. Operations practices and capabilities reinforce each
other over time. Capabilities do not emerge from a vacuum. Rather, the knowledge are

gained or drawn from the continued application of operations practices. Yet, operations

140

capabilities can influence operations practices by allowing firms to either implement new
practices more quickly or easily or to carry out existing practices more efﬁciently. This
would suggest a reciprocal relationship — something that could not be evaluated given the

limitation of the empirical analysis technique being used.

Second, the relationships among operations practices, operations capabilities, and
performance take place over time, thus implying that a dynamic approach might be more
appropriate. However, this study used a cross-sectional “snapshot” of the data to detect
the linkages. This snapshot approach could cover up such information that could be
evident in the longitudinal study. Consequently, it could be argued that some of the
findings reported were influenced by this sampling strategy. For example, this study
found that operations capabilities completely mediate the linkage between operations
practices and cost performance, but neither the relationship between operations practices
and quality performance nor the one between operations practices and delivery

performance.

One reason for this finding might lie in how operations practices and operations
capabilities affect performance over time. Operations practices might have a strong
impact on performance early on during the implementation. However, this impact could
then gradually dampen over time due to replication by competitors. In contrast,
operations capabilities may have a marginal impact early in the implementation process
since little knowledge has been cumulated. However, once an adequate base of
knowledge has been established, operations capabilities may experience an increasing

rate of impact that continues until the upper performance limit is reached. This pattern

141

can be envisioned as being similar to the “S” curve that is frequently used for the impact
of technology over time (Christensen, 1992; Sahal, 1981). The curve shows a slow,
relatively flat growth at the start and, after a considerable period, takes off rapidly.
Consequently, it is evident that the impact of operations capabilities is dependent on

when they are measured in the “S” curve.

6.5 Directions of Future Research

Being a pioneering research to understand how operations practices and operations
capabilities influence operations performance, this dissertation research can be extended
to at least three directions to make a better and more complete understanding of

operations strategy.

First of all, the nature of intra-relationships among operations practice initiatives and
operations capabilities has critical implications to resource investment decisions. In this
dissertation research, two possibilities were particularly investigated — compensatory
nature versus additive nature. The primary reason to consider these two models was that
they represent opposite cases: while additive nature is very resource demanding,
compensatory nature provides much ﬂexibility in resource decisions. In fact, the additive
model is only one of the three types of non—compensatory models (Patton and King,
1992). Though the data better supported the compensatory model, the other two (i.e.,
lexicographic model and disjunctive model) which imply alternative views to manage

resources/assets could not be ruled out.

142

The commonality of all three non—compensatory models is that they only consider one
criterion in decision making. To be more specific, the worst performance in practice
initiatives and operations capabilities put a limit on operations performance in the

additive model. As a result, firm need to balance the investment in all areas.

In a lexicographic model, consumers make decisions based on what they believe the most
important area of a product’s multiple attributes (Gigerenzer and Goldstein, 1996; Sloss,
1995). In this study context, firms that do best in the most important practice initiatives
(or operations capabilities) could outperform those that do poorly in the most important
area(s). That implies that firms has the flexibility to accommodate only those practice
initiatives or capabilities that they believe are essential and allocate more resource onto

them.

Disjunctive model assumes that a consumer’s purchasing decision depends on the
strongest area of a product’s multiple attributes (Khan, 1987; Williams, 1994). In this
study context, a firm’s strongest operations capabilities or practices initiatives determine

its success. Accordingly, a firm should focus more investment on those areas.

Overall speaking, both lexicographic nature and disjunctive nature suggest concentrating
resource investments or developing certain operations capabilities. Unlike compensatory
nature which does not identify the investment focus or strategy focus, these two actually
make a clear specification. From the strategic perspective, these two models are worth
testing in the future studies to help production organizations make concrete strategy

plans.

143

The second area of future study concerns the relative impact of operations practices and
operations capabilities over time. Having been pointed out in the limitations section, time
as a factor has to be considered in exploring the short-term effect and long-term effect of
operations practices and operations capabilities. One way to examine the impact without
a longitudinal dataset is to split the sample based on how long ﬁrms have implemented
core practice initiatives and observe the role of operations capabilities under different
scenarios. However, due to the limitation of the sample size, this plan could not be

executed in this study.

A conjecture was made that operations capabilities have a sustainable impact on
operations performance whereas operations practices have a pulse impact on operations
performance. Future studies are required to conﬁrm/disconfirm the conjecture. Empirical
evidence supporting the conjecture signifies that operations capabilities are strategic
assets. Therefore, the foci of operations strategy are to cultivate and establish certain
operations capabilities while implementing various kinds of practice initiatives.
Consequently, a firm needs to pay attention to the learning in the practice implementation
process. It is the learning, the extension of existing resources, and the improvement of
standard processes that generate the unique assets and production proficiency that a firm

can keep inside without being easily replicated by competitors.

Lastly, this study only considered external factors as potential moderators in the proposed
model. Actually, internal factors, though not directly related to production, could also
make a difference in production efficiency and productivity. One of them that have been

recognized in the literature is organizational culture (Gagnon, 1999; Nahm et al., 2004).

144

Conceptually, organizational culture has been viewed as an infrastructural decision in the
operation strategy literature (Hayes and Wheelwright, 1984). In formulation and
implementation of operations strategy. the organizational culture may manifest itself in
the organic processes within a firm, the cooperation in functional areas, the teamwork,
and the reward system that drives people towards the organizational goals and lead to

higher performance.

Toyota’s culture has been perceived as DNA in a longitudinal study carried out by Spear
and Bowen (1999). Toyota is very open about its system. However, ﬁrms learning from
Toyota found that its system could not be easily copied and transplanted. A major reason
is that observers focus on tools, practices, and techniques which are visible and easy to
observe. However, the Toyota production system is a belief-based system rather than a
tool-based system, and it is the organization context that makes the tools effective. Its
culture combines the shared beliefs, goals, and expectations and determines people’s
behaviors. Part of the culture is the philosophy of making continuously improvement
through processes standardization. This type of culture motivates employees to engage in
experimentation to improve every activity, every connection of workstations, and every
pathway for producing every product. This eventually leads to its excellent performance

in the automobile industry.

Though critical, research in investigating the role of organizational culture in operations
strategy was sparse (Dangayach and Deshmukh, 2001). Kitazawa and Sarkis (2000)
concluded in their study that employee empowerment and their willingness to make

suggestions for improvement are critical elements in successful operations strategy.

I45

Nahm et al. (2004) found that certain underlying assumptions (e.g., customer orientation)
affect the beliefs in facilities and equipment investment, decision making, working
collaboration and management control. Such beliefs drive employees’ behaviors to
support time-based manufacturing practices and lead to high performance. All of the
evidence shows that the role of these attributes of culture also need to be taken into

account in the future studies on operations strategy.

6.5 Chapter Summary

This last chapter of this dissertation sums up the major ﬁndings in the study and
recognizes the limitations from a conceptual perspective. More importantly, this chapter
emphasizes its contributions in the area of operations strategy from the theoretical
perspective and implications to operations managers. This dissertation research inspires
more future studies in the operations strategy area, particularly on the exact nature of
practice initiatives and operations capabilities, the role of practice initiatives and

operations capabilities over time, and the impact of organizational culture.

146

 

APPENDICES

147

 

Appendix A. Protocol of Focus Group Study

Part I. Demographic Information

Age

 

Total number of years in operations/supply chain management

 

Total number of years in the FIRM

 

Total number of years in Operations/Service area

 

How many divisions/plants in the FIRM have you been worked with?

Divisions/plants you have worked with

 

What is your role?

[I Help with Kaizen

CI Identify opportunity for improvement
[I Lead quality

[I Education/training

3 Others

 

Which description best characterizes your business unit? Please check ONE response
only.

Corporate level - representing entire company
Group
Division
Plant/site
Other (specify)

EEC—IDS

L.-

 

148

Part 11. Main Concepts and their Relationships

1) Discussion the definition.

0 Operations practices are defined as task-specific ways of organizing resources
with an aim to improve operations performance.

0 Operations capabilities are potentials to execute a specified course of action in
operations in a unique and proﬁcient way.

Do you agree with the deﬁnitions of operations practices and capabilities? Do you think
they have separate meanings?

2) Given the agreed deﬁnition, please list examples of core operations practices and
capabilities.

3) Look at the sets of operations practices and operations capabilities. Do you think that a
firm needs to have all in place to be competitive in the market?

All have to be in presence.

Some are the core and have to be in presence.

Not necessary. Strengths in some areas are good enough.
Other answers

DDUD

 

4) How do you suggest connecting the key terms by lines with arrow? Why?

      

Operations
Practices

 
  

  
   
 

Operations
Performance

 
 
    

  

Operations
Capabilities

149

Appendix B. Protocol of Q-sort

Instruction: Please read the definitions of the constructs and then assign each of the
measurements to the most appropriate construct. Please write down the construct number
in the following table.

Definitions

1. Collaboration skills: the realized potential to create healthy and stable relationships
with people from different areas of the firm/plant, customers and suppliers.

2. Proprietary process: the realized potential to customize the use of the resources
(equipment, people, systems, etc.) to make it unique.

3. Responsiveness: the realized potential of an operations management system to react to
changes in input or output requirement quickly and at low cost.

4. Incremental process improvement: the realized potential to refine and reinforce
existing processes.

5. Radical process innovation: the realized potential to create and implement unique
manufacturing processes that radically transform the existing ones.

6. Resource Reconﬁguration: the realized potential to accomplish the necessary internal

and external transformation to re-establish the ﬁt of operations strategy and market
environment as changes occur.

150

 

 

Measurement

Construct

 

I. We continuously simplify production processes

 

2. We have introduced new, internally developed materials and
content into our employee training programs

 

3. We adjust for unexpected variations in components and material
inputs easily and quickly

 

4. We sense/aware the change in the market and competitive
environment

 

5. We have created innovations that made our prevailing processes
obsolete

 

6. We continuously reduce waste and variance

 

7. Our equipment has been used in unique ways that differentiate us
from our competitors

 

8. We adjust for unexpected variations in labor requirement easily
and quickly

 

9. We reconfigure (combine or release) resources to respond to
changes

 

10. Our planning systems have been customized and extended to
better serve the needs of our customers

 

11. We adopt new and better practices to respond to changes

 

12. Our employees partner with suppliers/clients to develop
solutions for improvement.

 

13. We adjust for the unexpected changes in shipment requirements
easily and quickly

 

14. We have created innovations that fundamentally changed our
prevailing processes

 

15. We stimulate teamwork to facilitate the sharing of individual
knowledge to throughout the organization.

 

16. We reduce uncertainty of equipment availability by quickly and
easily changing the route

 

17. Our employees are skilled at collaborating with each other to

diagnose and solve problems

 

18. We develop competence and skills to respond to change

 

19. Our formal procedures facilitate collaboration across functions.

 

20. We have created innovations that made our existing expertise in
prevailing processes obsolete

 

2]. We continuously standardize production processes

 

22. Our production process have been customized and extended to
ain unique positions in the market.

 

23. Our product design process have been customized and extended
to better serve the needs of our customers

 

24. Our information system facilitates collaboration across functions

 

 

25. We have learned from past successes and failures for continuous
improvement

 

 

151

 

Appendix C. Questionnaire in the Survey Study

Achieving Operational Excellence: Insight from the Field

Part I. Qualiﬁcation

You are encouraged to ﬁll out Part I. Even if you are not qualiﬁed to move to the next
section, ﬁlling the information will enter you in the drawing of the prize.

1. Is operations your primary area of responsibility? "Operations" includes all the
activities of an organization directed toward producing a product or rendering a service.
Such activities may include, but are not limited to, planning, scheduling, performance
measurement, delegating and supervising the work, supply chain management,
procurement/purchasing, or logistics/warehousing, etc.

F .
Yes

F No. Please go to Question18 (Q.18).

What is your job title?
Chief operations ofﬁcer
VP manufacturing/operations
Plant manager
Director of operations
Production supervisor
Operations/production manager
Supply chain manager

2.
(a
(..
(a
(‘
(a
(‘
(a
F Planning and inventory systems manager
F

 

Others. Please specifyl -

3. How long have you been in this position?
F Less than 1 year
F 1-5 years
F 6-10 years
(‘

Over 10 years

152

4. How long have you been in the field of operations?
F Less than 1 year
(- 1-5 years
F 6—10 years
F Over 10 years

5. How confident do you feel assessing plant level issues?
r Not at all. Please go to Q.18.
F Somewhat conﬁdent
r Conﬁdent
(" Extremely conﬁdent

6. Are you willing to participate in the survey? Please note that participation in this
survey is entirely voluntary, with all individual responses being kept strictly
confidential. Only aggregate data results will be reported.

Yes. Please go to Q.8a.

No. Please go to Q. 7.
7. For those choosing not to participating, please check one of the following reasons
that best describes your reason for not participating and go to Q.18.

We do not participate in such surveys.

Concerns over conﬁdentiality.

We lack sufﬁcient resources/time to complete the survey.

We do not see any value in participating in this study.

We can not participate at this time because of factors outside of our control.

The timing of the study is not "right" for us.

 

Others. Please specify l

153

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10. Relative to your competition, how would you rate the performance of your plant
operation on the following dimensions of performance?

Poor Excellent
1 2 3 4 5 6 7
Manufacturing unit cost F F f‘ f‘ r r“ F
Manufacturing overhead cost (‘ r‘ r‘ r“ r“ r c
Total cost of ownership t“ r" r' r r p p
Product conformance t" r r‘ r r ('~ (~
Product durability r‘ r" r r F F r‘
Product overall quality F t“ r‘ r r (‘ p
Product reliability 1" r r' (‘ t‘ (- (~
Product features F F r‘ r r‘ F r

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Lead time to introduce new products F f” r" f“ f“ F F
Number of new products introduced each (L (L (L (L (L (L (L

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158

11. How would you indicate the rate of change for the following dimensions?

Slow Rapid
1 2 3 4 5 6 7

The industry rate of introduction of new products (L (L

or services

The Industry rate of introduction of new (L (L (L (L (L (L (L
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12. To what extent do you agree or disagree with the applicability of the following
phrases to describe the culture of your plant?

Strongly _ _ Neutral _ _ Strongly
disagree agree
1 2 3 4 5 6 7
Willingness to take risks F f” F F F F“ (‘
Has a value-added mentality F f“ (" f“ F (" ("
Customer orientated F f“ F f“ (‘ f“ (‘
Employees feel empowered (" F F I“ F r‘ r
Process orientated 1"“ P t“ t“ (‘ r F
13. How would you describe the industry your plant competes in?
None/ Numerous/
low/ high/
short long
1 2 3 4 5 6 7
The number of major competitors F f“ F F f“ F i“
Price difference between you and your (L (L (L (L (L (L (.

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Growth/decline in sales

Product life cycle length

159

14. Please indicate the industry in which your business competes.

If you know the NAICS code, please specify your three-digit code (please refer to the
website for the NAICS code information

 

(http://www.census.gov/epcd/naicsOZ/naicodOZ.htm)I .

 

If you know the SIC code, please specify your three-digit code i

 

Major product

 

Name of the ﬁrm to which your plant belongs l _

(15. What is the approximate annual sales dollars in 2005 (in 1,000$) in the plant?

 

 

16. What is the approximate number of employees in the plant?

 

 

Part III. Follow-up

17. During the course of any study such as this one, interesting or unexpected ﬁndings
often emerge. Determining what has happened requires some additional work. In some
cases, this means asking some of the respondents for additional feedback in the form of a
short (i.e., one page) survey. This information greatly enhances the quality of the ﬁndings
and improves the nature of the insights gained from the study.

Would you be interested in participating in this small group feedback initiative?

'— Yes. In exchange for your participation, you will receive a more detailed executive
summary.

F' No.

18. Thank you very much for participating in the survey. Please enter the following
information so that you can qualify for the drawing and/or receive an electronic version
of the executive summary. Your email address is optional, but please note that we
cannot send anything to you if we do not know who you are.

 

 

Email address _.

160

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161

 

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