GOVERNING INTER-ORGANIZATIONAL RELATIONSHIPS IN THE
PRESENCE OF EX POST OPPORTUNISM AND UNCERTAINTY: AN
ALIGNMENT MODEL OF MANAGING OUTSOURCING
By
Ravi Srinivasan

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Supply Chain Management
2012

ABSTRACT
GOVERNING INTER-ORGANIZATIONAL RELATIONSHIPS IN THE
PRESENCE OF EX POST OPPORTUNISM AND UNCERTAINTY: AN
ALIGNMENT MODEL OF MANAGING OUTSOURCING
By
Ravi Srinivasan
Despite the importance of outsourcing engagements, little research has been done on
effectiveness of governance mechanisms. In particular, the effectiveness of governance
mechanisms were not examined in the presence of risks such as ex post opportunism and
uncertainty. Specifically, this research examines the effectiveness of transactional and relational
governance mechanisms in the management of outsourcing engagements. Consequently, answers
to three main research questions are sought as part of this dissertation.
First, this research examines the effective governance mechanisms in the presence of ex
post opportunism and project uncertainty. The results indicate that the configurations of effective
governance mechanisms are different for different configurations of risk. Second, the research
explores if there are any specific patterns of governance mechanisms that are being currently
used by outsourcing engagements. The results indicate that managers tend to choose specific
patterns of governance mechanisms based on the strategic importance as well as risk faced in the
engagement. Finally, this research examines if transactional governance mechanisms and
relational governance mechanisms are complements or substitutes. The results indicate that
transactional and relational governance mechanisms act as complements to each other.
Specifically, the results depend on the level of opportunism exhibited by the supplier and the
strategic importance of the outsourcing engagement. When the supplier is cooperative, relational
governance mechanisms provide superior outsourcing performance. On the other hand, when the

supplier is uncooperative (i.e., behaves in an opportunistic manner), the results diverge.
Transactional governance mechanisms are beneficial when the outsourcing engagement is
strategically important.
Based on the results, both transactional and relational governance mechanisms are seen
as important. The effectiveness of the governance mechanisms differ based on the level of risk
and the strategic importance of the outsourcing engagements. Managerial insights corresponding
to these results are presented in this dissertation. The results provide clarity and
recommendations to managers on instituting appropriate governance mechanisms in outsourcing
engagements.

Copyright by
RAVI SRINIVASAN
2012

Dedication
This dissertation is dedicated to my wife, Leila and my parents, Srinivasan and Janaki. They
provided me the support and encouragement in following my dream of pursuing a doctoral
degree.

v

ACKNOWLEDGEMENTS
I would like to first thank my family for providing me the support and encouragement during my
pursuit of the doctorate degree. My parents, Srinivasan and Janaki, instilled in me the love for
knowledge and the necessary work ethic that immensely helped me during my doctoral years,
especially during my dissertation. I would like to thank my wife, Leila, from the bottom of my
heart. Her love, encouragement, patience and occasional admonition helped me stay focused
during my doctoral years.
The most important person who helped me shape this dissertation is my dissertation chair, Dr.
Ram Narasimhan. His guidance has helped me appreciate the pursuit of knowledge and helped
me shape this dissertation. I also appreciate his valuable advice on career, family and work-life
balance. I also appreciate the valuable insights from Dr. Sriram Narayanan and Dr. David
Griffith. In addition, I would like to thank the following faculty members during my doctoral
studies – Dr. Roger Calantone (for his advice), Dr. Harrison McKnight (for helping me
appreciate research as a fine art), Dr. Vallabh Sambamurthy (for being constant source of
encouragement) and Dr. Morgan Swink (for his support).
Finally, I would like to thank the doctoral students in my cohort and the ones before me for their
support during my scholarly journey at MSU. The numerous discussions on research topics and
teaching methods have made me a better researcher and teacher. In particular, I thank the other
two members of the three musketeers (you know who you are) for being a constant source of
support. Your support helped me during the days when the journey felt long and the end not in
sight.

vi

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................................................... ix 
LIST OF FIGURES ........................................................................................................................ x 
CHAPTER 1: INTRODUCTION ................................................................................................... 1 
1.1 
Research topic .................................................................................................................. 1 
1.2 
Motivations....................................................................................................................... 2 
1.2.1  Theoretical motivation.................................................................................................. 3 
1.2.2  Practical motivation ...................................................................................................... 4 
1.3 
Methodology .................................................................................................................... 6 
1.4 
Contributions .................................................................................................................... 6 
1.5 
Dissertation outline .......................................................................................................... 7 
1.6 
Summary of introduction ................................................................................................. 8 
CHAPTER 2: LITERATURE REVIEW ........................................................................................ 9 
2.1 
Outsourcing ...................................................................................................................... 9 
2.2 
Opportunism ................................................................................................................... 12 
2.3 
Project uncertainty.......................................................................................................... 13 
2.4 
Governance mechanisms ................................................................................................ 14 
2.4.1  Transactional governance ........................................................................................... 15 
2.4.1.1  Monitoring .............................................................................................................. 16 
2.4.1.2  Contractual flexibility ............................................................................................. 17 
2.4.1.3  Transaction-specific investments ............................................................................ 18 
2.4.2  Relational governance mechanisms............................................................................ 19 
2.4.2.1  Information exchange ............................................................................................. 20 
2.4.2.2  Shared Understanding ............................................................................................. 20 
2.5 
Outsourcing engagement performance........................................................................... 21 
2.6 
Summary of literature review ......................................................................................... 22
CHAPTER 3: RESEARCH FRAMEWORK ............................................................................... 23 
3.1 
Research perspective ...................................................................................................... 23 
3.2 
Fit as profile deviation.................................................................................................... 28 
3.3 
Fit as gestalts .................................................................................................................. 39 
3.3.1  Outsourcing relationship configurations .................................................................... 40 
3.3.2  Governance configurations ......................................................................................... 44 
3.3.3  Linking the elements (Gestalts) .................................................................................. 47 
3.4 
Summary of research framework ................................................................................... 50 
CHAPTER 4: RESEARCH METHODOLOGY .......................................................................... 51 
4.1 
Data collection................................................................................................................ 51 
vii

4.1.1  Response rate .............................................................................................................. 53 
4.1.2  Nonresponse bias tests ................................................................................................ 56 
4.2 
Scale development.......................................................................................................... 57 
4.3 
Measures......................................................................................................................... 58 
4.3.1  Opportunism ............................................................................................................... 58 
4.3.2  Project Uncertainty ..................................................................................................... 60 
4.3.3  Governance mechanisms ............................................................................................ 61 
4.3.4  Outsourcing performance and learning outcomes ...................................................... 66 
4.3.5  Controls ...................................................................................................................... 67 
4.4 
Measurement reliability and validity.............................................................................. 72 
4.5 
Summary of research methodology................................................................................ 73 
CHAPTER 5: ANALYSIS AND RESULTS ............................................................................... 75 
5.1 
Fit as profile deviation.................................................................................................... 75 
5.1.1  Risk profiles................................................................................................................ 75 
5.1.2  Outsourcing performance ........................................................................................... 76 
5.1.3  Learning outcomes ..................................................................................................... 82 
5.2 
Fit as gestalts .................................................................................................................. 89 
5.2.1  Relating the gestalts to Outsourcing performance ...................................................... 93 
5.2.1.1 Strategic partnership ................................................................................................. 93 
5.2.1.2 Adversarial relationship ............................................................................................ 94 
5.2.1.3 Arm’s length relationship ......................................................................................... 95 
5.2.1.4 Selective partnership ................................................................................................. 96 
5.3 
Summary of analysis and results .................................................................................... 98 
CHAPTER 6: DISCUSSION........................................................................................................ 99 
6.1 
Knowledge of buyer-supplier relationships ................................................................... 99 
6.1.1  Fit as profile deviation ................................................................................................ 99 
6.1.1.1 Results for outsourcing performance ....................................................................... 100 
6.1.1.2 Results for learning outcomes ................................................................................. 103 
6.1.2  Fit as gestalts ............................................................................................................ 105 
6.1.3  Theoretical implications ........................................................................................... 108 
6.2 
Managerial insights ...................................................................................................... 111 
6.2.1  Fit as profile deviation .............................................................................................. 111 
6.2.2  Fit as gestalts ............................................................................................................ 112
CHAPTER 7: CONCLUSION ................................................................................................... 114 
7.1 
Summary of research .................................................................................................... 114 
7.2 
Limitations of study ..................................................................................................... 116 
7.3 
Future research ............................................................................................................. 117 
APPENDIX ................................................................................................................................. 119 
REFERENCES ........................................................................................................................... 195 

viii

LIST OF TABLES

Table 1 – Respondent demographic information .......................................................................... 52 
Table 2 – Industry information for the sample ............................................................................. 53 
Table 3 – Risk constructs, items and sources ............................................................................... 63 
Table 4 – Transactional governance constructs, items and sources.............................................. 69 
Table 5 – Relational governance constructs, items and sources ................................................... 70 
Table 6 – Performance constructs, items and sources .................................................................. 71 
Table 7 – Correlation matrix ......................................................................................................... 74 
Table 8 – Governance mechanisms significantly related to outsourcing performance ................ 79 
Table 9 – Relationship between misalignment measure and performance ................................... 82 
Table 10 – Governance variables significantly related to learning outcomes .............................. 85 
Table 11 – Relationship between misalignment measure and learning outcomes........................ 88 
Table 12 – Frequencies, percentages of relationship configurations ............................................ 90 
Table 13 – Results of cluster analysis on governance mechanisms.............................................. 91 
Table 14 – Governance configuration for Strategic partnership ................................................... 94 
Table 15 – Governance configuration for Adversarial relationship ............................................. 95 
Table 16 – Governance configuration for Arm’s length relationship ........................................... 96 
Table 17 – Governance configuration for Arm’s length relationship ........................................... 96 
Table 18 – Gestalts data analysis results....................................................................................... 97 
Table 19 – Summary of hypotheses and their support .................................................................. 98 

ix

LIST OF FIGURES

Figure 1: Configurations of risk in outsourcing engagements ...................................................... 33 
Figure 2 – Research framework – Fit as profile deviation............................................................ 39 
Figure 3 – Configurations of relationships in an outsourcing engagement .................................. 44 
Figure 4 – Configurations of governance mechanisms in an outsourcing engagement ............... 46 

x

CHAPTER 1: INTRODUCTION
This chapter introduces the research topic for this dissertation and provides motivations
to study this topic. The research methodology used to collect data and analyze results is
presented. Following this, the contributions to literature is discussed. Finally, the chapter
concludes by providing an outline of chapters in this dissertation.

1.1

Research topic
This dissertation examines the effective management of buyer-supplier relationships in

the context of outsourcing engagements. Specifically, this dissertation examines effective
configurations of governance mechanisms in the presence of project uncertainty and supplier ex
post opportunism. Researchers have established that transactional governance mechanisms
enhance the performance of buyer-supplier relationships (Jap & Anderson, 2003; Mayer &
Argyres, 2004; Stump & Heide, 1996). Furthermore, researchers have called for the use of
relational governance mechanisms to effectively manage buyer-supplier relationships (Handley
& Benton Jr, 2009; Li, Xie, Teo, & Peng, 2010; Liu, Luo, & Liu, 2009; Paulraj, Lado, & Chen,
2008).
However what is not clear is the effectiveness of transactional and relational governance
mechanisms on outsourcing performance in the presence of risk. Specifically, do the
configurations of governance mechanisms that effectively improve performance change with
different risk profiles. Some research has argued for the examination of transactional and
relational mechanisms as being complementary or substitutes (Li et al., 2010; Liu et al., 2009).
This dissertation offers clarity by incorporating risk – both project and relational risks – to
1

examine the effectiveness of transactional governance mechanisms in achieving superior
outsourcing performance.
Although the governance mechanisms examined are not exhaustive, general inferences
can be made regarding management of buyer-supplier relationships. Typically, buyers institute
mechanisms that are transactional in nature, such as monitoring, to ensure that suppliers are
working toward the best interest of the buyer. Furthermore, recent calls for the use of relational
governance mechanisms have been heeded by practitioners and increasingly have been instituted
in the outsourcing engagements. Some clarity is still needed in effective deployment of these
governance bundles when the buyer encounters risks in the outsourcing engagements.
In accordance with the above motivations, two principal questions are addressed in this
dissertation. What are the effective configurations of governance mechanisms corresponding to
risk profiles? What are the commonly occurring sets of governance mechanisms (i.e., gestalts)
that correspond to the nature of the outsourcing relationship? Both these questions are asked with
the implication that the effectiveness of governance mechanisms will lead to better outsourcing
performance. Through examination of these questions, this dissertation also attempts to answer
the question if transactional and relational governance mechanisms act as complements or
substitutes. In the next section, the motivation for pursuing these questions is presented.

1.2

Motivations
This subsection describes both the theoretical and practical reasons for researching

effective configurations of transactional and relational governance mechanisms in the presence
of project uncertainty and supplier ex post opportunism.

2

1.2.1

Theoretical motivation
A resolution to the question of how the effective configurations of governance

mechanisms change with changing risk profiles and nature of outsourcing engagements is of
theoretical value. The key focus of this dissertation is to examine the effectiveness of governance
mechanisms in the presence of project uncertainty and supplier ex post opportunism. Typically,
researchers have claimed that relational governance mechanisms need to be implemented
alongside with transactional governance mechanisms (Handley & Benton Jr, 2009; Li et al.,
2010; Liu et al., 2009). These studies argue for universality of such an approach without
consideration of contingent factors. Contingent factors considered are project related risks,
relational risks and the strategic importance of outsourcing engagements. Both transaction cost
economics and agency theory have argued for the importance of uncertainty and opportunism
and their influence on inter-organizational relationships. In this dissertation, uncertainty is
conceptualized as project related risks and relational risks as supplier ex post opportunism. Given
the importance of risk mitigation in buyer supplier relationships, it is particularly important to
address this gap in literature. In addition, this dissertation also provides insights on effective
governance mechanisms based on strategic importance of the outsourcing engagements.
Increasingly, researchers have recognized that firms are outsourcing activities that are considered
strategic in nature (Gilley & Rasheed, 2000; Gottfredson, Puryear, & Phillips, 2005; Holcomb &
Hitt, 2007). The paucity of research in examining effective governance mechanisms is addressed
through this dissertation.
More recently, researchers have been calling for examination of transactional and
relational governance mechanisms. One question that remains unanswered is if the two forms of
governance mechanisms (transactional & relational) complement or substitute each other. This
3

dissertation provides insights by examining the effectiveness of governance mechanisms under
different contingent conditions.
1.2.2

Practical motivation
Motivations to pursue outsourcing have changed in the recent years. Traditionally, firms

were outsourcing activities that were considered peripheral to the firm. Increasingly, many firms
are outsourcing strategic activities that are directly related to a firm’s core competence.
Furthermore, the firms are collaborating with specialist organizations to gain capabilities
(Gottfredson et al., 2005). In addition to the varied motivations to outsource activities, the
monetary value of outsourcing projects has increased. The average size of the top 20 outsourcing
contracts is just under $1 billion and on average outsourcing contracts were valued at around
$200 Million (Gartner, 2008). These trends suggest that the size and scope of outsourcing
engagements has increased considerably.
Whereas the size and scope of outsourcing engagements has increased over the years, the
problems with managing these engagements have persisted. In 2007, Gartner report cautioned
that “sourcing strategies and governance structures are still immature, lacking altogether, or
misaligned with enterprise objectives. Because these organizations lack the basic building blocks
for successful vendor management and outsourcing success, expected cost savings and other
benefits are difficult to obtain” (Potter 2007, p.2). Commenting on managing suppliers, Choi and
Krause (2006, p. 637) state that, “With the recent trend of increasing levels of outsourcing,
orchestrating activities with suppliers in the supply base from the perspective of a focal company
has become a top strategic issue”. This sentiment is also reflected in practitioner articles. For
example, HR Outsourcing association mention in a report that, “arguably the hardest part of

4

outsourcing occurs after the deal is done: undertaking the transition and performing ongoing
outsourcing management and governance (OM/G)” (HR Outsourcing Association, 2007). These
statements provide the practical motivations to understand the effective management of
outsourcing engagements.
Buyer-supplier relationships are mired with risks. For example, BusinessWeek (2006)
reported that considerable delays in the design and development of Boeing 787 Dreamliner were
encountered because Boeing was late in providing the requirements of the product to its
suppliers. Furthermore, the use of competitors as suppliers working on related and
interconnected components created additional problems. Partnerships between competing
suppliers resulted in the suppliers undermining the best interest of Boeing and acting with selfinterest. These issues bring to light important issues that are of interest to managers. First, it
brings to light the importance of clarity in project requirements. Project related uncertainty can
considerably influence the outsourcing performance. Second, the Boeing 787 example highlights
the importance of managing suppliers, especially when they show self-interest and act in an
opportunistic manner. Providing clarity in terms effective governance mechanisms countering
supplier opportunism and project uncertainty will be of value to managers.
The above arguments motivate this research in the following ways. First, it examines the
effectiveness of various governance mechanisms, already established in the literature, under
different risk profiles. Second, it provides guidance to managers in allocating valuable resources
in establishing transactional and relational governance mechanisms in managing outsourcing
engagements.

5

1.3

Methodology
This dissertation utilized a configuration approach to examine governance mechanisms

that are effective in providing superior outsourcing performance. Venkatraman (1989) suggested
two forms of fit are best suited for configuration research - fit as profile deviation and fit as
gestalts. Hypotheses are presented based on buyer-supplier relationship literature, transaction
cost economics (Williamson, 1979, 1981; Williamson, 1983), agency theory (Eisenhardt, 1989)
and relational norms (Heide & John, 1992; MacNeil, 1980). This dissertation intends to test the
fit between different governance mechanisms and risk profiles that can maximize outsourcing
performance. The data was collected using web survey methodology. Members from Project
Management Institute (PMI) and International Association of Outsourcing Professionals (IAOP)
were approached to provide feedback through surveys. Literature on buyer-supplier relationships
and marketing channel relationships were used to develop theoretically grounded measurement
model that was validated through appropriate measurement model analysis. Multivariate
regression, cluster analysis and comparison of means were used to test the hypotheses based on
fit as profile deviation and fit as gestalts. The results from the analysis provide a richer
understanding of effective governance mechanisms in outsourcing engagements.

1.4

Contributions
The contributions of this dissertation are three-fold. First, the dissertation makes

substantial contribution to the literature by showing that a deviation from the ideal profile of
governance mechanisms can result in lowered outsourcing performance as well as learning
outcomes. The negative relationship between outsourcing performance and the deviation from

6

the ideal profile does not change for strategic and peripheral outsourcing engagements. Similarly,
the learning outcomes derived from the peripheral and strategic outsourcing engagements are
lower when there is misalignment of governance mechanisms. In addition, this dissertation also
provides further clarity on the effective governance mechanisms for different risk profiles based
on supplier opportunism and project uncertainty.
Second, this dissertation identifies relationship configurations based on strategic
importance and supplier opportunism. Gestalts of governance mechanisms for the relationship
configurations are identified and the outsourcing performance for gestalts and non-gestalts is
compared. The results indicate the importance of relational governance mechanisms. Whereas
literature has claimed the importance of relational governance mechanisms, this dissertation
provides further clarity by showing the importance of relational governance mechanisms when
the supplier is uncooperative.
Finally, this dissertation examines if relational and transactional governance mechanisms
are complements or substitutes. The results of the analysis show the importance of relational
governance mechanisms. The results did not indicate the superiority of transactional or hybrid
governance mechanisms over relational governance mechanisms. A key take-away from this
dissertation is that firms should start developing shared values with their suppliers to gain
superior outsourcing performance and learning outcomes.

1.5

Dissertation outline
The next chapter will review the extant literature related to the concepts in this

dissertation – i.e., outsourcing, buyer supplier relationships, opportunism, uncertainty,
transactional governance mechanisms and relational governance mechanisms. Chapter 3 presents
7

the research framework examined in this dissertation. In total, eight hypotheses are developed
based on the research framework. Chapter 4 provides the details on data collection, measures and
measurement validation. Chapter 5 presents the analysis method and the results of analysis. The
theoretical and managerial implications of the results are discussed in Chapter 6. Chapter 7
concludes the dissertation by presenting a summary of the research, limitations and opportunities
for further research that can extend the findings from this dissertation.

1.6

Summary of introduction
This chapter provided an introduction of the dissertation. The research topic was

presented along with theoretical and practical motivations. The contribution of this dissertation
to extant literature as well as practitioner community was discussed. Finally, the outline for the
dissertation was presented. To understand the contributions of this dissertation, the extant
literature must be reviewed. The details are provided in the next chapter.

8

CHAPTER 2: LITERATURE REVIEW
In this chapter, the literature relevant to this dissertation is presented. First, the extant
literature on outsourcing is presented. Following this, the literature on two main risk
components, opportunism and project uncertainty, is presented. This is followed by the literature
on governance mechanisms.

2.1

Outsourcing
OM researchers have examined buyer-supplier relationships for many years. There is a

growing trend among OM researchers to study outsourcing engagements in particular. Firms are
choosing to outsource activities not only to gain cost efficiencies but also to gain capabilities
(Gottfredson et al., 2005; Holcomb & Hitt, 2007). Literature on outsourcing has dealt mainly
with three broad areas of research 1) antecedents and conditions leading to outsourcing specific
activities within a firm 2) the determinants of successful outsourcing – including structuring of
contracts and 3) utilizing appropriate governance mechanisms to improve outsourcing
performance. Literature on all of these areas of research is presented.
Predominantly, researchers used transaction cost economics to study outsourcing as an ex
ante decision making process. Typically, the reasons for outsourcing specific activities are
examined in terms of the cost of governing outsourcing engagements. For example,
(Balakrishnan, Mohan, & Seshadri, 2008) found that front end processes are outsourced when
the requirement for customer contact and information intensity is low. The nature of activities
being outsourced can depend on the outsourcing strategy of the firm (Bardhan, Mithas, & Lin,
2007). Supply risk and competency of the outsourcing firm also influence the activities that are
9

outsourced (Mantel, Tatikonda, & Liao, 2006). The key determinant of the make-buy decision is
the cost of governing an outsourcing engagement. Some invisible costs exist when tasks are
being outsourced to the supplier. The invisible cost can depend on the level of interaction
required and the distance (geographic, language and cultural) between the outsourcing partners
(Stringfellow, Teagarden, & Nie, 2008). Sometimes, fixed costs incurred by a firm can also be a
key determinant in deciding whether to outsource an activity (Ellram, Tate, & Billington, 2008).
Interestingly, some actions taken by a firm can reduce the transaction costs because there is a
streamlined process in place. One such example is the implementation of enterprise resource
planning system. Stratman (2008) found that firms have a higher propensity to outsource when
they implement ERP systems. There are other considerations, in addition to costs, that influence
outsourcing decisions. For example, Gray, Tomlin, and Roth (2009) found that power of a
contract manufacturer affects the benefits gained from outsourcing. They suggest that partial
outsourcing is an optimal strategy to outsource. Similarly, Hui, Davis-Blake, and Broschak
(2008) find that the power of owner firms is an important determinant for controlling and
coordinating among outsourcing partners.
Another focus of researchers is to identify sources of better firm performance. Jiang,
Belohlav, and Young (2007) examined the impact of types of outsourcing on firm stock market
valuation. They found that core business related outsourcing, offshore outsourcing and shorterterm outsourcing have positive influence on performance but non-core business related
outsourcing, domestic outsourcing and longer-term outsourcing are not found to enhance value.
Bhalla, Sodhi, and Son (2008) explored the link between company’s performance and the extent
of offshoring. They found that the extent of offshoring does not impact company performance.
More recently, Kroes and Ghosh (2010) argue that outsourcing congruence on five competitive
10

priorities (cost, quality, delivery, flexibility and innovation) is significantly related to supply
chain performance.
Governing outsourcing engagements has come to prominence in recent years. Evidence
from literature suggests that there are many factors that can improve outsourcing performance.
Supplier selection based on past performance has been shown to be an important indicator of
future performance (Cui, Loch, Grossmann, & He, 2011; Handley & Benton Jr, 2009). Once the
supplier is selected, appropriate incentives have to be put in place to align supplier’s goals with
the buyer’s goals. Outsourcing contracts can be governed using contractual (i.e., transactional or
formal) control mechanisms such as service level agreements. Goo, Huang, and Hart (2008) find
that service level agreements can influence the benefits (functional, strategic and technological)
gained from an outsourcing engagement. In contrast, Aron, Bandyopadhyay, Jayanty, and Pathak
(2008) suggest that buying firms can avoid costly inspection by specifying a minimum threshold
of quality. Gopal and Koka (2010) found that suppliers are more likely to provide quality outputs
when the outsourcing engagement is structured as a fixed price contract rather than time &
material contract. Increasingly, there is evidence that social (i.e., relational) control mechanisms
are gaining prominence in governing outsourcing engagements. Handley and Benton Jr (2009)
found that relationship management practices result in positive outsourcing performance. Some
social mechanisms that affect outsourcing project performance include trust (Amaral & Tsay,
2009; Cui et al., 2011), information exchange (Cui et al., 2011; Narayanan, Balasubramanian, &
Swaminathan, 2009) and distribution of rewards (Amaral & Tsay, 2009). There is also evidence
suggesting that all control mechanisms are equal. Formal mechanisms are better suited for
outsourcing engagements focused on incremental innovation and social mechanisms provide
better outcomes from radical innovation projects (Li, Liu, Li, & Wu, 2008). There is general
11

consensus that relational governance serves either as a substitute or complement to contractual
buyer-seller governance (Cousins, Handfield, Lawson, & Petersen, 2006; Griffith, Harvey, &
Lusch, 2006; Li et al., 2010; Liu et al., 2009; Narasimhan, Nair, Griffith, Arlbjørn, & Bendoly,
2009). Though it is unclear as to the nature of this relationship and the conditions under which
transactional and relational governance mechanisms act as substitutes or complements.
Whereas researchers have examined buyer-supplier relationships in general and
outsourcing engagements in particular, few studies have considered the presence of uncertainty
and opportunistic behavior. This study fills this gap in literature by examining the governance
mechanisms that are related to superior outsourcing performance and learning outcomes in the
presence of project uncertainty and ex post opportunism.

2.2

Opportunism
Transaction cost economics (Coase, 1937; Williamson, 1979, 1981; Williamson, 1983)

has been the foundation for many studies in OM research. Opportunism is one of the behavioral
assumptions of transaction cost economics (Williamson, 1981). The resultant uncertainty due to
opportunism has been dubbed as behavioral uncertainty (Williamson, 1985). Jap and Anderson
(2003) describe opportunism as “self-interest seeking with guile”. Williamson (1985, p. 47)
describes guile as “lying, stealing, cheating, and calculated efforts to mislead, distort, disguise,
obfuscate”. Opportunism can be exhibited ex ante, before the start of a relationship, where the
supplier can misrepresent their capabilities (Wathne & Heide, 2000). Similarly, opportunism can
also exist ex post (i.e., after the start of the relationship) where the partner can renege explicitly
or implicitly by shirking or failing to keep promises and obligations (Jap & Anderson, 2003).

12

Opportunism has been originally conceptualized as an explicit violation of contractual
agreements (Williamson, 1983). Wathne and Heide (2000) termed this conceptualization as
“Strong form” opportunism. They suggest that opportunism can exist when a partner either
engages in or refrains from certain actions. Thus, active opportunism is exhibited when a partner
engages in activities that were either explicitly or implicitly prohibited (Wathne & Heide, 2000).
For example, the supplier can exhibit active opportunism by demanding the buyer to pay more
for correcting a problem. In addition, partners can exhibit opportunistic behavior in a passive
manner by evading or shirking their responsibilities. For example, the supplier can exhibit
evasion by promising to do certain things in the project but does not deliver on those promises.
Together, the opportunistic behavior exhibited by the supplier can impact the success of an
outsourcing engagement.
This research conceptualizes opportunism as actions exhibited by the supplier once the
buyer and supplier are engaged in an outsourcing relationship. From a TCE perspective,
opportunism increases the cost of coordination for the buying firm. Thus, supplier’s ex post
opportunism has an impact on outsourcing performance due to increased cost of coordination.
This increased cost of coordination occurs due to the requirement that the buyer has to closely
monitor the supplier.

2.3

Project uncertainty
In addition to behavioral uncertainty (Williamson, 1985), the firms also encounter

primary uncertainty (Koopmans, 1957) due to “lack of knowledge about states of nature, such as
the uncertainty regarding natural events” (Sutcliffe & Zaheer, 1998, p. 3). In this dissertation,
primary uncertainty is conceptualized as project uncertainty. These issues are encountered by the
13

project manager as well as the project team. The issues can be characterized in the form of
variation, foreseen circumstances, unforeseen circumstances and chaos (Pich, Loch, & Meyer,
2002). Shenhar (2001) takes a different view where the projects are classified based on the scope
and technological uncertainty encountered. Project scope is determined by the number of subsystems and their interdependencies and technological uncertainty is based on the “newness” of
the technology being used to implement the project. In contrast, Nidumolu (1995) identified two
main sources of uncertainty when studying software development projects. First, uncertainty
arises due to difficulty in “eliciting requirements from the users” (Nidumolu, 1995, p. 195). The
source of requirements uncertainty is the lack of clarity or lack of consensus among the
stakeholders of a project. Second, technological uncertainty arises when “state-of-the-art”
technologies need to be used to carry out the requirements of the project. In addition, the project
members should be able to prioritize the tasks and time of completing the tasks (Bendoly &
Swink, 2007). Taken together, project uncertainty is conceptualized as the lack of clarity or
consensus on the requirements of the project, lack of ability to prioritize the tasks that need to be
executed in the project and the inability of the project team members to anticipate problems in
advance.

2.4

Governance mechanisms
Increasingly, researchers have argued that firms should use both transactional and

relational governance when managing outsourcing engagement (Handley & Benton Jr, 2009; Li
et al., 2010; Liu et al., 2009). In line with the arguments from these researchers, the key elements
of transactional and relational governance mechanisms that are relevant to managing and
outsourcing engagement are discussed below.
14

2.4.1

Transactional governance
Agency theory has been used to examine exchange relationships between a buyer

(principal) and supplier (agent) (Eisenhardt, 1989). The key focus of agency theory is to deal
with resolving two main problems – agency problem and problem of risk sharing. Typically, in a
principal-agent relationship the agent may not have similar goals in comparison to the principal.
The goal of agency theory is to manage the contract most efficiently by either monitoring the
behavior of the agent or monitoring the outcomes. The nature of monitoring (i.e. behavior vs.
outcomes) is dependent on contingent factors such as bounded rationality, opportunistic behavior
and risk aversion (Eisenhardt, 1989). There are strong similarities between agency theory and
transaction cost economics. Both theories examine the most efficient form of contract under
contingent conditions. The common assumptions between the two theories are self-interest (i.e.,
opportunistic behavior) and bounded rationality (Eisenhardt, 1989). Behavior based contracting
under agency theory is similar to hierarchies in transaction cost economics where the principal is
able to monitor the behavior of the agent. Market based contracting (in TCE) is similar to
outcome based contracting in agency theory. The key difference between TCE and agency theory
is that transaction cost economics does not take into account the risk propensity of the actors
(i.e., principal and agent). Using the TCE and Agency theory arguments, researchers have
identified monitoring, contractual flexibility and transaction specific investments as governance
mechanisms to manage buyer-supplier relationships (Anderson & Weitz, 1992; N. Argyres &
Mayer, 2007; Stump & Heide, 1996). These governance mechanisms are discussed in detail in
the following subsections.

15

2.4.1.1 Monitoring
Monitoring has been recognized as an essential aspect of buyer-supplier relationships
(Ellram et al., 2008; Metters, 2008; Modi & Mabert, 2007). Supplier’s opportunistic behavior
increases transaction costs in terms of monitoring the outcomes as well as safeguarding costs
associated (Ellram et al., 2008; Modi & Mabert, 2007). Monitoring has been shown to reduce ex
post opportunistic behavior even when the buyer and supplier are engaged in a long-term
relationship (Morgan, Kaleka, & Gooner, 2007). Monitoring mechanisms are considered as
transactional mechanisms and appropriate levels of supplier monitoring has been recognized as
beneficial (D. E. Boyd, Spekman, Kamauff, & Werhane, 2007). Specifically, monitoring
mechanisms are considered to reduce supplier’s opportunistic behavior in two ways (Wathne &
Heide, 2000). First, it applies social pressure on the supplier to comply with the requirements of
the outsourcing engagement. Second, it helps the buyer to take necessary actions to curb
supplier’s opportunistic behavior by monitoring the quality of the deliverables (Stump & Heide,
1996).
Monitoring mechanisms have been advocated by researchers and practiced by managers
for a long time. For example, Williamson (1993) recognized that ex ante effort in supplier
selection and incentive design are largely incomplete and require firms to monitor their suppliers.
Similarly, Heide (1994) suggests that monitoring is essential irrespective of whether the buyer
resorts to market or hierarchical forms of governance. Furthermore, monitoring is essential
because initial screening and qualification of supplier alone will not reduce opportunism, in
particular ex post opportunism (Stump & Heide, 1996). Monitoring mechanisms have been
established as part of service level agreements where the expectations of both parties involved is
explicitly stated. In addition, service level agreements also specify the “metrics by which the
16

effectiveness of various contracted services and processes will be measured and controlled”,(Goo
et al., 2008, p. 471). Typically, monitoring mechanisms are used to measure the outputs provided
by the supplier by tracking the milestones in an outsourcing engagement (Lewis, Welsh, Gordon,
& Green, 2002). In addition, buying firms can establish standards for quality and delivery and
measure the compliance of the supplier with such established standards (Stump & Heide, 1996).
2.4.1.2 Contractual flexibility
Every contract is inherently incomplete (Grossman & Hart, 1981). Uncertainty in an
outsourcing engagement manifests because of bounded rationality (Rindfleisch & Heide, 1997).
Bounded rationality implies that the buyer is unable to specify all the contingencies that may
occur in an outsourcing engagement a priori. Typically, transaction cost economics perspective
suggests that hierarchy is the most efficient form of organizing when a firm is unable to
anticipate contingencies. The implicit assumption in TCE is that the buyer is able to
instantaneously learn about the contingencies associated with the outsourcing engagement
(Mayer & Argyres, 2004). Furthermore, the buyer has a choice of either pursuing an alternate
supplier or pursuing vertical integration based on the transaction costs of the contingencies.
Mayer and Argyres (2004) refute this assumption and argue that both parties go through a
learning process. Through this learning process, the parties in the relationship develop the ability
to plan for contingencies (N. Argyres & Mayer, 2007). Thus, contingency planning is
incorporated into the contract to provide flexibility to modify the requirements of the project as
the parties learn the details of execution. Both parties are able to adjust to the changing nature of
the outsourcing engagement and plan accordingly.
Contingency planning can be classified into two main types – generic contingency
planning and specific contingency planning (N. S. Argyres, Bercovitz, & Mayer, 2007). In a
17

generic contingency plan, the process of future changes is agreed upon by the outsourcing
partners. For example, the partners may agree on the process of changing the statement of work
(SOW) through the use of change requests (N. S. Argyres et al., 2007). On the other hand,
specific contingency plans can include details on contingencies that may occur during the lifecycle of the project and procedures that need to be followed (N. S. Argyres et al., 2007).
Typically, specific contingency plans are associated with situations where the parties are aware
of the possible contingencies that can happen in the due course of the engagement but lack
information on the occurrence of a specific contingency. In an outsourcing engagement, the
firms may pursue an option of renegotiating the terms of the contract to accommodate the
changing nature of the engagement. Through this process of contingency planning, the details of
outsourcing engagement can be clarified and the “hold-up problem” is avoided.
2.4.1.3 Transaction-specific investments
Transaction cost economics researchers have shown the importance of transactionspecific investments (TS investments) in acting as safeguards from opportunistic behavior in a
relationship (Anderson & Weitz, 1992; Jap & Anderson, 2003; Williamson, 1993). Transactionspecific investments are relationship specific and hold very little value outside of the relationship
context. These investments can be both tangible and intangible (Jap & Anderson, 2003).
Tangible investments include investments in manufacturing facilities, logistics systems, specific
tools, machines or systems. On the contrary, intangible investments include transfer of tacit
knowledge, specific technology, capability or processes and procedures that requires both parties
to work closely to extract rents from them. Nyaga, Whipple, and Lynch (2010) argue that
transaction-specific investments enable the firms to appropriate higher rents because they can
develop inter-firm routines and processes that are boundary spanning in nature. TS investments
18

also act as safeguards by creating a mutual hostage situation where both parties have to work out
the differences and make the relationship work (Jap & Anderson, 2003; Liu et al., 2009). Thus,
TS investments are used as signals to indicate commitment to the relationship (Nyaga et al.,
2010). In an outsourcing engagement, the initial effort and time spent by both parties in
understanding the nature of the activities that are outsourced can result in substantial reduction in
effort and cost during later stages of the outsourcing engagement. The outsourcing partners can
also establish processes and procedures that are specific to the outsourcing engagement that
enable smooth functioning by establishing appropriate escalation mechanisms in case of
ambiguities. Furthermore, it also establishes procedures for knowledge transfer that can result in
higher relational rents between the buyer and supplier.
2.4.2

Relational governance mechanisms
In addition to agency theory and TCE, researchers have utilized social exchange theory

(Emerson, 1962; Homans, 1958) to examine the relationships between buyers and suppliers.
Relational norms (Heide & John, 1992; MacNeil, 1980) have been recognized in the literature as
an important aspect of social exchange. Relational norms instill shared values and norms that can
be used as a substitute for clan mechanism (Ouchi, 1979) resulting in behaviors where the buyer
and supplier are not acting in an opportunistic manner. Two relational norms are considered –
information exchange and joint problem solving through shared understanding. Information
exchange is used by the parties to reduce information asymmetry and increase the quality of the
relationship. Joint problem solving allows the buyer and supplier to mutually agree on new
information that may influence the outcomes of an outsourcing engagement.

19

2.4.2.1 Information exchange
Researchers have examined the role of information exchange in reducing inefficiencies
between the buyer and supplier firms. Intra-organizational communication has been found to
improve performance of a firm (Monczka, Petersen, Handfield, & Ragatz, 1998; Narasimhan &
Kim, 2002). Similarly, inter-organizational communication has been found to improve both
strategic and operational performance of supply chain partners (Choi & Hartley, 1996;
Narasimhan & Kim, 2002; Prahinski & Benton, 2004; Shin, Collier, & Wilson, 2000; D. Y. Wu
& Katok, 2006). Information exchange allows the buyer and supplier to synchronize activities
and harness knowledge that exists within the team toward effective problem solving (Fugate,
Stank, & Mentzer, 2009). Information exchange between buyer and supplier fosters partnership
and builds trust, resulting in lowered opportunistic behavior from both parties (Goffin, Lemke, &
Szwejczewski, 2006; Paulraj et al., 2008). In addition, information exchange improves
information processing capacity and hence reduces task uncertainty (Stock & Tatikonda, 2008).
By investing in inter-organizational information exchange the buyer can harness strategic
advantages from the relationship (Paulraj et al., 2008). In this study, we hypothesize that
information exchange between buyer and supplier will foster better problem-solving and result in
increased performance through effective risk mitigation.
2.4.2.2 Shared Understanding
Diversity of opinion and experience helps outsourcing engagements to explore and
innovate. Shared language enables firms to effectively communicate and create knowledge
(Cohen & Levinthal, 1990; Nahapiet & Ghoshal, 1998; Zenger & Lawrence, 1989). Shared
language enables outsourcing partners to interpret, understand and respond to information in a
similar manner (Zenger & Lawrence, 1989). Through shared understanding, “subtle and
20

technical experience” (Ingram & Simons, 2002) are exchanged between the buyer and supplier.
It takes considerable effort, time and repeated interactions to develop a shared language and
experience between buyers and suppliers (Carlile & Rebentisch, 2003; McFadyen & Cannella,
2004). In an experiment simulating a merger activity, (Weber & Camerer, 2003) find that
subjects develop a shared language through repeated interactions and it takes considerable
number of iterations to regain similar understanding (i.e., shared language) when partners are
changed.
Shared language enables efficient communication that results in identification and
organization of pertinent information for the outsourcing relationship (Kogut & Zander, 1992;
Zenger & Lawrence, 1989). Suppliers that do not share a common language with the buyer are
likely to misinterpret the information from the buying firm (Zenger & Lawrence, 1989). Shared
understanding between the buyer and supplier results in higher efficiency and effectiveness of
joint-problem solving. Shared understanding encompasses the participation (Nyaga et al., 2010)
aspect of relational norm and adds to it the efficiency and effectiveness of problem-solving. In
this study, we utilize these findings to argue that shared understanding helps in resolving
uncertainty in an outsourcing engagement resulting in improved outsourcing performance.

2.5

Outsourcing engagement performance
Traditionally, outsourcing engagements have been measured on both efficiency and

effectiveness criteria (Raz & Michael, 2001). The immediate concern for the buying firms in an
outsourcing engagement is on-time and under-budget completion of tasks within the engagement
(Clark, 1989; Hartley, Zirger, & Kamath, 1997; Swink, Talluri, & Pandejpong, 2006). In
addition, learning outcomes are considered critical by the buying firms (Clark, 1989; Lewis et
21

al., 2002; Raz & Michael, 2001). Learning outcomes include gaining technical knowledge
(Tatikonda & Rosenthal, 2000a; Lewis et al 2002), commercial knowledge, proprietary
information and technology that is useful to other projects within the organization (Lewis et al.,
2002). This dissertation examines effectiveness of governance mechanisms in gaining superior
outsourcing performance as well as learning outcomes.

2.6

Summary of literature review
In this chapter, the pertinent literature for the dissertation was reviewed. First, the extant

literature on outsourcing engagements was presented. Following this, the literature on risks and
governance mechanisms were discussed. Finally, the performance of outsourcing engagements
and learning outcomes were discussed. These concepts will be linked in the research framework
to provide a configuration research perspective that provides insights into effective management
of outsourcing relationships. This research framework and the relevant theoretical underpinnings
are discussed in the next chapter.

22

CHAPTER 3: RESEARCH FRAMEWORK
This chapter presents the research framework in this dissertation. First, the research
perspective is presented to lay the groundwork for the research framework. Following this, the
theoretical underpinnings pertinent to the two configuration approaches – fit as profile deviation
and fit as gestalts are presented. In total, eight hypotheses corresponding to the research
framework are presented.

3.1

Research perspective
Researchers have studied the ex ante decision making of activities that need to be

outsourced based on several criteria. For example, the level of customer contact required was
used as a determinant in outsourcing activities within a firm (Balakrishnan et al., 2008).
Similarly, other factors such as supply risk (Mantel et al., 2006), level of interaction and distance
(Stringfellow et al., 2008), fixed costs (Ellram et al., 2008) and ERP system implementation
(Stratman, 2008) were shown to influence a firm’s outsourcing strategy. More recently, the
interest in studying effective management of outsourcing engagement is increasing as witnessed
by recent studies (Li et al., 2010; Liu et al., 2009). Researchers have determined that firms use
different approaches to ensure success of an outsourcing engagement. For example, (Cui et al.,
2011) found that supplier selection is critical to the success of an outsourcing engagement.
Similarly, other factors such as service level agreements (Goo et al., 2008), nature of the contract
(Gopal & Koka, 2010), relationship management practices (Handley & Benton Jr, 2009),
communication (Narayanan et al., 2009) and distribution of rewards (Amaral & Tsay, 2009)
were found to contribute toward successfully managing an outsourcing engagement. Even

23

though these governance mechanisms were examined, very few studies have incorporated the
influence of risk in the success of an outsourcing engagement.
Albeit risk has been considered as an important construct to be considered, very few
studies have explicitly used a risk management perspective when examining outsourcing
engagements. This dissertation explicitly addresses this gap in literature and examines the
salience of risk and its effective mitigation strategies in outsourcing engagement. Risk
management literature identifies many forms of risk. Two primary sources of risks that are
particularly salient to outsourcing engagements are considered. Koopmans (1957) argued that a
buyer-supplier relationship can be plagued by primary uncertainty – risk due to the “state of
nature” (Sutcliffe & Zaheer, 1998: pp3) and secondary uncertainty – risk due to the behavior of
the supplier. Primary uncertainty in an outsourcing engagement manifests itself in the form of
project uncertainty. The lack of clarity in terms of the priorities of tasks can result in wasted
effort, inefficient execution of tasks and inability to anticipate problems. These factors contribute
toward deterioration in outsourcing performance. In contrast, secondary uncertainty manifests
itself in the form of supplier ex post opportunistic behavior. Studies have shown that an
uncooperative supplier can contribute toward deterioration of performance in a buyer-supplier
relationship (Jap & Anderson, 2003). Typically, researchers have considered either one or
another form of risk. In contrast, this dissertation simultaneously examines effective governance
mechanisms in the presence of both primary uncertainty (i.e., project uncertainty) and secondary
uncertainty (i.e., supplier ex post opportunistic behavior).
Researchers have identified two main sets of governance mechanisms that firms use to
manage outsourcing engagements. First, transaction cost economics (Coase, 1937; Williamson,
1979, 1981; Williamson, 1985) and agency theory (Eisenhardt, 1989) perspectives are utilized
24

and the effectiveness of transactional governance mechanisms in governing buyer-supplier
relationships has been examined. Studies have shown the importance of monitoring, contract
flexibility and transaction-specific investments in safeguarding buyer-supplier investments (N.
Argyres & Mayer, 2007; Jap & Anderson, 2003; Stump & Heide, 1996). Second, increasingly,
relational view has been shown to be important in managing buyer-supplier relationships by
researchers (Handley & Benton Jr, 2009; Nyaga et al., 2010). Specifically, relational norms
(Heide & John, 1992; MacNeil, 1980) have been shown to be effective in managing buyersupplier relationships. Whereas researchers have examined these governance mechanisms, very
few have examined the use of both transactional and relational mechanisms simultaneously (Li et
al., 2010; Liu et al., 2009). In contrast with prior literature, this dissertation examines both
transactional and relational governance mechanisms simultaneously.
In addition to the above stated reasons, this dissertation takes a different methodological
approach than prior studies. Venkatraman (1989) identified six forms of fit – moderation,
mediation, covariation, matching, profile deviation and gestalts. Appropriate use of each form of
fit is dependent on the level of precision (i.e., functional form of the fit) and the relationship of
fit to an external criterion (Venkatraman, 1989). In addition, Venkatraman and Prescott (1990)
categorize coalignment research into two main perspectives, reductionistic and holistic. Other
researchers have suggested analogous approaches such as interaction versus systems approach
(Drazin & Ven, 1985). Interaction approach is similar to the reductionistic approach and systems
approach is analogous to holistic approach. Reductionistic perspective suggests that the
relationship between two constructs can be examined in terms of “pairwise coalignment among
the individual dimensions that represent the two constructs” (Venkatraman & Prescott, 1990).
The precise relationship between constructs can be specified and the relationship can be tested
25

for superior performance. By replicating the process and extending the constructs being
examined, cumulative knowledge can be developed (Venkatraman & Prescott, 1990). In contrast,
holistic perspective retains the systemic nature of the inter-linkages between many constructs and
tests for the performance effects based on the simultaneous and holistic pattern of inter-linkages
of the constructs. Venkatraman and Prescott (1990) contend that the use of reductionistic
approach such as “co-alignment between individual dimensions” (pp. 2) provides a narrow
perspective of the relationships between the variables of interest. The relationships are examined
under ceteris paribus conditions and fit is conceptualized as a set of “bivariate co-alignments”
(Venkatraman & Prescott, 1990, p. 2). In contrast, they suggest that the use of configurational
approach is more “holistic” in nature and it lends itself to a richer understanding of the interlinkages between variables. Numerous researchers have similarly called for configurational
approach in examining research questions where the relationship between many variables can be
simultaneously tested (Drazin & Ven, 1985; Hambrick, 1984; D. Miller, 1981).
This reliance on reductionistic perspective can be observed in buyer-supplier relationship
literature as well. As mentioned earlier, researchers examined the influence of individual
governance mechanisms on relationship performance. Few studies have simultaneously
examined sets of governance mechanisms that can be classified as transactional or relational in
nature. The studies that examined transactional and relational governance issues resorted to
creating separate constructs rather than incorporating the governance mechanisms identified in
the literature. For example, Liu et al. (2009) created two constructs – contract and relational
norms – that correspond to transactional and relational governance mechanisms. Similarly, Li et
al. (2010) created two constructs – formal control mechanisms and social control mechanisms –
to examine domestic and international buyer-supplier relationships in China. This dissertation
26

uses a configurational approach to address this methodological problem. Venkatraman (1989)
suggests that fit as profile deviation and fit as gestalts are appropriate to use when utilizing a
configurational approach. Consequently, these forms of fit (profile deviation and gestalts) are
utilized to address the research questions in this dissertation.
Fit as profile deviation is the “degree of adherence to an externally specified profile”
(Venkatraman, 1989, p. 433) that allows for a multi-dimensional assessment of fit. Furthermore,
the degree of adherence to an ideal profile by a business unit for a given environment can be
related to performance (Venkatraman, 1989). Similarly, gestalts are defined as “degree of
internal coherence among a set of theoretical attributes” (p.432). The goal of “fit as gestalts” is to
identify commonly occurring attributes rather than be precise about the functional form that the
attributes take. These methodological ideas are used in the examination of governance
mechanisms in this dissertation. The commonly occurring gestalts of governance mechanisms
are identified and their implication on outsourcing performance and learning outcomes are
examined.
Taken together, the uniqueness of this dissertation results from examination of
governance mechanisms by taking into account the risk from primary sources (project
uncertainty) and secondary sources (supplier ex post opportunism). In addition, this study utilizes
configuration approach (both fits as profile deviation and fit as gestalts) to examine if
transactional and relational governance mechanisms can act as complements to each other. In
doing so, this dissertation sets itself apart from prior research on buyer-supplier relationships.
The following sections develop the research framework and develop hypotheses based on risk
management perspective, buyer-supplier relationships and governance mechanisms.

27

3.2

Fit as profile deviation
Configurational approach uses fit as profile deviation as one of the methods of examining

relationships. Using this approach, the performance implications of different configurations of
variables can be examined simultaneously. This approach has been utilized in strategic
management literature. For example, Venkatraman and Prescott (1990) identify eight different
environments and correspondingly identify important elements of strategy that correspond to
superior performance for each of the environment variables. Similarly, Lukas, Tan, and Hult
(2001) found that firms in “transitional economies” like China use either a prospective or
protective strategies based on the level of environmental uncertainty they encounter.
Interestingly, fit as profile deviation has not been widely utilized in examining supply chain
management phenomena with the exception of a few studies. For example, da Silveira (2005)
utilized this methodology to analyze the order-winners framework proposed by (Hill, 1993). The
study examined the ideal profiles of product and markets and manufacturing decisions and
related them to alternative process choices. The results indicated that a “misfit” in process
choices resulted in a deterioration of domestic market share.
Researchers have typically used a “reductionistic” approach and examined the impact of
a limited set of governance mechanism variables and their effectiveness in managing outsourcing
engagements. For example, Goo et al. (2008) relate the service level agreements to the benefits
gained from the outsourcing engagement. In addition, they use the level of commitment in the
relationship as a moderator and find support that service level agreements provide benefits in the
presence of commitment. Whereas the study provides valuable insights on the impacts of service
level agreements, it does not take into account the relational mechanisms that a buyer may use in

28

managing the relationship. Studies have also taken into account the relational norms between the
buyer and supplier and relate it to performance. For example, Nyaga et al. (2010) found that
information sharing, participation and dedicated investments improve the level of trust and
commitment, thus improving the level of satisfaction in the relationship and performance.
Similarly, Prahinski and Benton (2004) and Paulraj et al. (2008) argue for the importance of
information exchange in improving performance of buyer-supplier relationships. Again, these
studies take into account the relational governance mechanisms but ignore transactional
mechanisms. More recently, some researchers have called for examining the two forms of
governance mechanisms simultaneously. The results of these studies are not conclusive. For
example, Li et al. (2010) studied domestic and international buyer-supplier relationships in China
and found that formal controls and social controls are substitutes in domestic relationships but
they are “neither pure substitutes nor complements” (pp. 340) in international relationships.
In contrast to the aforementioned studies, this dissertation uses fit as profile deviation to
examine the effectiveness of transactional and relational governance mechanisms in managing
outsourcing engagements. Fit as profile deviation allows for the creation of an ideal profile in an
n-dimensional space (Venkatraman, 1989; Venkatraman & Prescott, 1990). Venkatraman and
Prescott (1990) studied environment-strategy relationship and found ideal profile strategies that
correspond to different environments. Similar to their approach, the fit between governance
mechanisms and risk profiles is examined. The risk profiles are derived based on the level of
relational risk (i.e., supplier opportunism) and project risk (i.e., project uncertainty) in an
outsourcing engagement. Prior literature has shown that both supplier ex post opportunism and
project uncertainty will negatively impact the performance of outsourcing engagement (Jap &
Anderson, 2003; Nidumolu, 1995). Using this framework, we can examine the choices made by
29

managers based on the nature of risk encountered in outsourcing engagements. This framework
allows us to formulate an ideal profile of governance mechanisms used by high performers.
An approach similar to Venkatraman and Prescott (1990) is followed. The environment
of the relationship is characterized by two main sources of risk – relational risk (supplier ex post
opportunism) and project risk (project uncertainty). The outsourcing engagements are classified
into four groups based on the level of supplier ex post opportunism and project uncertainty. The
groups are labeled as unstable (low supplier opportunism – high project uncertainty),
uncooperative (high supplier opportunism – low project uncertainty), routine (low supplier
opportunism – low project uncertainty) and high-risk (high supplier opportunism – high project
uncertainty). The specific nature of risk encountered in each group is discussed.
First, let us consider the risk configuration characterized as unstable. Outsourcing
engagements belonging to this risk configuration primarily encounter risk due to uncertainty in
project specifications. The requirements of the outsourcing engagement lack clarity. The team
members do not have complete understanding of the tasks that need to be completed because the
details of the tasks are not specified in a timely manner. In addition, the sequence of activities
that need to be completed may not be clarified. The difficulty in navigating the
interdependencies of tasks can result in execution problems. Moreover, the execution problems
cannot be anticipated by the project team members. Due to the interdependencies, outsourcing
engagements may have to undertake rework of some tasks resulting in additional costs (Bendoly
& Swink, 2007). Such delays can result in the outsourcing engagements to be delayed resulting
in overall deterioration of outsourcing performance.

30

Now let us consider an uncooperative risk configuration. Even though there may be
general clarity in requirements, sequence of tasks to be performed and the desired quality of the
deliverables, the outsourcing engagement may not be able to achieve superior performance due
to relational risk. Typically, the buyer is unable to switch the supplier once the outsourcing
engagement is underway resulting in increased power in favor of the supplier. The supplier may
take advantage of the situation through different actions. For example, the supplier may shirk the
responsibilities creating possible delays in the outsourcing engagement. The supplier can also
extract higher rents from the outsourcing engagement in many ways. For example, the supplier
may use substandard material or assign lower skilled employees to the project. Employees
belonging to the supplier may not be vested in the project and may work on activities that are
more valuable to them than the client (Clemmons & Hitt, 1997). The supplier can
opportunistically renegotiate by charging additional fees to the buyer for tasks that need to be
performed, thus resulting in additional costs to the buyer. These actions, either taken separately
or collectively, can result in substantial loss of productivity and consequently diminished
outsourcing performance. For these reasons, it can be argued that the outsourcing performance
will be affected due to uncooperative suppliers.
Now consider the risk configuration where the outsourcing engagement is affected by
high levels of both project uncertainty and an uncooperative supplier. The outsourcing
engagement is even more vulnerable in this risk configuration. This is characterized as high-risk
configuration. There may be compounding effect because the managers have to not only contend
with lack of clarity in the outsourcing engagement but also an uncooperative supplier. There is
evidence in literature that the potential for supplier opportunism may increase in the presence of
project uncertainty (Stump & Heide, 1996). Hence, the engagements in this risk configuration
31

are considered most vulnerable and may experience the highest deterioration in outsourcing
performance.
Finally, the “routine” risk configuration is characterized by low project risk as well as
relational risk. The outsourcing engagements with this risk configuration are relatively
straightforward in their project requirements. The buyer is clear on the requirements of the
project. The tasks that need to be undertaken are specified a priori with considerable level of
clarity. The interdependencies between tasks are not complex and are easily specified. The team
members have clarity in executing the tasks and are able to anticipate any execution issues and
take actions before the risk materializes. In addition, the supplier is cooperative and works in
conjunction with the buyer in proactively executing the tasks. The supplier provides adequate
updates to the buyer and is responsive to minor changes in the project. It can be argued that the
routine configuration provides the most stable risk profile for outsourcing engagements. The four
configurations of risk are represented in Figure 3 below.

32

Figure 1: Configurations of risk in outsourcing engagements
Fit as profile deviation suggests that there is an “ideal profile” for each environment and
deviation from this ideal profile may result in deterioration in outsourcing performance. An
interesting implication of ideal profile is that the deviation can result from both underuse as well
as overuse. This implies that there is possibly a curvilinear relationship between the antecedents
and the dependent variable. Typically, studies have hypothesized a strictly linear relationship
between constructs. A positive relationship between the variables implies that an increase
(decrease) in the antecedent will result in an increase (decrease) in the dependent variable. In
contrast, the use of ideal profile in analyzing the fit between governance mechanisms and risk in
an outsourcing engagement suggests that the relationship is not strictly linear. Interestingly, a
study by Hartley et al. (1997) found that the relationship between governance and performance is
not strictly linear and that a firm can experience deterioration in performance through overuse of
governance.
33

Two classes of governance mechanisms are considered when creating an ideal profile.
First, transactional governance mechanisms such as monitoring, contract flexibility and
transaction-specific investments are considered. Second, information exchange and shared
understanding, aspects of relational norms (Heide & John, 1992; MacNeil, 1980) are considered.
The theoretical arguments for choosing these specific set of transactional and relational
governance mechanisms have been provided in the literature review section.
There is an implicit performance implication associated with fit as profile deviation. The
method suggests that firms deviating from the “ideal profile” will experience deterioration in
performance. In this research, it is hypothesized that firms deviating from the ideal profile will
exhibit multiple performance issues. First, the classic project management metrics of the
outsourcing engagement are considered. An outsourcing engagement will be unable to meet the
goals of on-time and within-budget accomplishments of deliverables. Furthermore, the
deliverables of the project may not be of the highest quality. Second, the buyer may not be able
to achieve the desired learning outcomes when there is a misfit between the risks faced in the
outsourcing engagement and the governance mechanisms used to manage the outsourcing
engagement. It can be argued that when the risks associated with the outsourcing engagements
are not adequately managed, the ability to learn new knowledge and assimilate it into the
knowledge base of the firm is reduced considerably. Based on this reasoning, two hypotheses are
proposed:
H1: Fit (as profile deviation) between risks to outsourcing engagement and governance
mechanisms is positively associated with outsourcing performance

34

H2: Fit (as profile deviation) between risks to outsourcing engagement and governance
mechanisms is positively associated with learning outcomes
Researchers have recognized that firms are increasingly outsourcing activities that are
considered strategic in nature (Gilley & Rasheed, 2000; Gottfredson et al., 2005; Holcomb &
Hitt, 2007). Taking a resource-based view perspective (Barney, 1991), it can be argued that
activities that are strategic in nature are considered valuable to a firm. Furthermore, it is
important to ensure that these activities are not easily imitated by competitors and partners. In
addition, the firm should ensure that other firms do not develop products that can act as
substitutes. That is, as a result of the strategic activity a firm can gain competencies that provide
a sustained competitive advantage to the firm. Similarly, strategic activities can result in
development or augmentation of core competence of a firm (Prahalad & Hamel, 1990). Core
competence is considered as the collective learning of an organization and its ability “to
coordinate diverse production skills and integrate multiple streams of technologies” (Prahalad &
Hamel, 1990, p. 4). A firm can maintain core competence through its ability to organize work
and its ability to communicate the necessary information to other functions within the firm
(Prahalad & Hamel, 1990). Core competence of a firm enables it to apply its capabilities in
creating new products and services that can result in higher rents. A subtle distinction between
the two perspectives is that core competence can be considered as the realization by a firm of the
capabilities it already possesses and is providing competitive advantage but RBV is the
realization of capabilities that are needed by the firm to succeed. That is, core competence
argues that there are certain activities that are fundamental to a firm and the firm should never let
go of it. In contrast, RBV suggests that firms should identify capabilities that can provides
sustained competitive advantage and develop them either organically within the firm or by
35

acquiring them from other suppliers. For example, Prahalad and Hamel (1990) argue that a firm
such as Honda has developed technology for engines that allows it to enter into diverse markets
such as motorcycles to airplane engines. Similarly, over the years, Canon has perfected its optics
technology that allowed it to produce both personal photo copiers as well as introduce single-lens
reflector (SLR) cameras. With the advent of digital storage technology, the firm was able to
easily transition into digital SLR market.
Both core competence and RBV perspectives recognize that there are capabilities that are
important for a firm and they should be safeguarded. Otherwise, it can result in loss of
uniqueness of the capabilities, resulting in increased competition and lowered rents from
products and services that can threaten the long-term success of the firm. This research argues
for the importance of strategic activities based on the similarity between the two perspectives.
When firms pursue strategic outsourcing engagements, they increase their vulnerability due to
over-exposure of capabilities to their partners and competitors (Gilley & Rasheed, 2000). This
vulnerability can manifest in many ways. For instance, strategic sourcing can impede the ability
of a firm to compete in the market place. When Boeing decided to utilize strategic sourcing for
their 787 Dreamliner, the company suffered significant delays in the completion of the project.
The key issue cited was that the project was complex and was beyond the capability of Boeing to
coordinate numerous suppliers in achieving the interdependent tasks. Failure to complete the
project on time has significantly reduced Boeing’s ability to compete, resulting in significant
cutbacks in orders by its customers and at times cancellation of the orders (BusinessWeek,
2006).
Strategic nature of an activity can also pose a threat to the intellectual property of a firm
and can result in knowledge leakage (McEvily & Chakravarthy, 2002; Narasimhan & Talluri,
36

2009). When interacting with the suppliers, firms should take extra caution in ensuring that the
knowledge possessed by the firm does not leak to its partners. For example, a recent court-ruling
in Germany found that Samsung, a supplier of video display for Apple, was in violation for
encroaching into Apple’s market for iPad (New York Times, 2011). Samsung released the
Galaxy Tab that directly competes with Apple’s iPad. This is an example of a threat to long-term
success of a firm due to a supplier or customer encroaching on a firm’s market (Porter, 1979).
Engaging in outsourcing activities that are strategic in nature can result in transfer of knowledge
to a firm’s suppliers and customers (McEvily & Chakravarthy, 2002). Firms need to ensure that
the leakage of sensitive knowledge is prevented by instituting appropriate governance
mechanisms that can act as deterrent for its suppliers.
When pursuing new innovation, firms have a choice of either developing the technology
in-house or acquiring it from external sources such as a supplier. Gottfredson et al. (2005) argue
that firms should pursue outsourcing to gain capabilities. They suggest that firms should evaluate
activities for outsourcing based on the criticality of the activity, the capability of the firm and the
level of control necessary to manage the outsourcing process. An example of sourcing for
capability is fast prototyping (Shenhar & Dvir, 2007). Firms can collaborate with their suppliers
to acquire capability that can result in fast turnaround of prototype products in the market.
Typically, a firm may have to make changes to the product when introducing a “break-through
product” (Shenhar & Dvir, 2007) to the market. Break-through products are considered new
because they introduce a new concept or idea that was hitherto not available to the customers
(Shenhar & Dvir, 2007). Firms can be vulnerable to opportunism from the suppliers when
pursuing innovative projects and need to ensure that the suppliers do not take advantage of the
vulnerable position and extract additional rents for continuation of the relationship.
37

Typically, strategic activities are closely related to the revenue stream of a firm. Loss of
knowledge and the ability to compete can result in both short-term and long-term problems for a
firm. In the short-term, the firm has a potential to lose revenue because of competing products or
services are introduced by competitors. As stated above, Samsung’s Galaxy tablet has a potential
for reducing the revenue that can be garnered by Apple by selling iPad. In the long-term, the firm
has a potential to lose the uniqueness of its capabilities. The capabilities may no longer be
inimitable and rare resulting in loss of competitive advantage. Hence it can be argued that it is
important for the firm to utilize appropriate governance mechanisms to safeguard against
potential opportunistic behavior from the suppliers. Furthermore, the governance mechanisms
should be deployed in order to ensure that the project uncertainty in the outsourcing engagement
is reduced. That is, the fit between governance mechanisms and risks in the outsourcing
engagements are even more critical when a firm is engaged in strategic outsourcing
engagements. Ensuring high levels of outsourcing performance is critical because firms are able
to introduce products and services on-time to the market. Furthermore, it can be argued that
learning outcomes from strategic outsourcing engagements can be incorporated into the firm’s
core competence resulting in sustained competitive advantage. Therefore the following
hypotheses are proposed:
H3: Strategic importance of the engagement positively moderates the impact of fit
between risks to outsourcing engagement and governance mechanisms on outsourcing
performance
H4: Strategic importance of the engagement positively moderates the impact of fit
between risks to outsourcing engagement and governance mechanisms on learning outcomes

38

The research framework corresponding to the above hypotheses are depicted in figure 2
below.

Figure 2 – Research framework – Fit as profile deviation

3.3

Fit as gestalts
Whereas fit as profile deviation provides an intuitive method of examining a choice

model, there is a key criticism that has been laid against the method. The core premise of the
approach is dependent on the definition of an ideal profile. The problem with an ideal profile is
that few firms, if any, will be able to conform to the ideal profile (Lee, Miranda, & Kim, 2004).
When applied to fit as profile deviation discussed in the previous section, this issue poses a
limitation wherein the firms may not be able to deploy the necessary governance mechanisms.
Hence, an alternate method of examining governance mechanisms through configurational
research is explored.
Fit as gestalts suggests that there are internally congruent sets of variables that may occur
together (Venkatraman, 1989). This idea is utilized to examine the set of governance
mechanisms that may “naturally occur” based on the nature of outsourcing engagement. Lee et
39

al. (2004) argue that the “mutually constraining nature” (pp. 115) of the choices may result in
ineffective and inefficient patterns of choices. Furthermore, they provide a density-dependence
argument that suggests that more firms will choose “congruent patterns” over “incongruent
patterns”, resulting in gestalts because incongruent patterns are inefficient and ineffective. In
this dissertation, gestalts are developed based on the argument that for certain relationship
configurations there are “congruent patterns” of governance choices that the firms may use to
manage their outsourcing engagements. The relationship configurations, governance
configurations and the gestalts are discussed in the following sections.
3.3.1

Outsourcing relationship configurations
Two important considerations have been examined in buyer-supplier relationship

literature - strategic importance and supplier ex post opportunistic behavior (Gilley & Rasheed,
2000; Jap & Anderson, 2003). In this dissertation, they are used to develop configurations of
buyer-supplier relationships in outsourcing engagements. First, consider the strategic importance
of an outsourcing engagement. Gilley and Rasheed (2000) characterized two types of
outsourcing based on their impact on the firm as either core or peripheral in nature. Peripheral
outsourcing is when a firm decides to outsource “less strategically relevant” (Gilley & Rasheed,
2000, p. 767) functions to its supplier. Peripheral outsourcing consists of outsourcing tasks that
are considered not related to focal firm’s core competence to “specialist organizations” (Gilley &
Rasheed, 2000, p. 769) who consider the tasks to lie within their core competence. They argue
that there are three main reasons for firms to pursue outsourcing of peripheral activities. First, it
provides the firm an ability to concentrate on their core competencies rather than waste valuable
resources on activities that do not directly impact their revenue. Second, through peripheral
outsourcing the buyer will be able to rely on “specialist organizations” to provide the necessary
40

capabilities that are not related to their core competence. Consequently, the quality of the
capabilities gained may be higher because the specialist organization is able to execute the
project within their core competence. Finally, the buyer can gain cost advantages by outsourcing
peripheral activities to the supplier.
In contrast, core outsourcing is considered to be “important to long-run success” (Gilley
& Rasheed, 2000, p. 767) of the firm. Core outsourcing consists of functions that relate directly
to the revenue stream of the firm. Increasingly, firms are pursuing outsourcing of their core
activities to gain capabilities through partnerships with their suppliers (Gilley & Rasheed, 2000;
Gottfredson et al., 2005; Holcomb & Hitt, 2007). Outsourcing of core activities has to be
undertaken carefully. Researchers have identified some potential issues when outsourcing tasks
that are related to the core competence of a firm. First, there is a potential for the firm to lose
their competitiveness toward innovation (Teece, 1988). In addition, suppliers may gain the
capabilities and have a potential to become competitors (Prahalad & Hamel, 1990; Quinn, 1992).
Finally, failures associated with core outsourcing are far more detrimental than failures
associated with peripheral outsourcing activities because they can jeopardize the future
performance of a firm (Gilley & Rasheed, 2000).
Firms also consider relationship quality with their supplier in an outsourcing engagement.
Relationship quality is determined by the level of conflict and amount of trust between the
partners (Kumar, Scheer, & Steenkamp, 1995). One of the key contributing factors for
deterioration of relationship quality is supplier opportunistic behavior, in particular ex post
opportunistic behavior. Deterioration in relationship quality can result in lowered relational
competence. Relational competence is defined as the ability of a firm to work in a collaborative
manner with its partners (Paulraj et al., 2008). In particular, relational competence manifests
41

itself in the form of information exchange and ability to understand each other when solving
problems. Each party is unwilling to share valuable and critical information that may be relevant
to the other party. The information that the parties share may not be in a timely manner to allow
adequate time for the other party to utilize the information constructively. Such mistrust can
increase misunderstanding between the two parties resulting in unresolved problems in the
outsourcing engagement. Overall, the level of cooperation and collaboration between
outsourcing partners deteriorates due to ex post opportunism exhibited by the supplier.
Both strategic importance and supplier opportunism, are considered together to create
four configurations of relationships in an outsourcing engagement (Figure 3). First let us
consider the configuration where a firm has outsourced activities that are strategically important
to the firm and the supplier is cooperative resulting in better relationship quality. We characterize
this relationship configuration as strategic partnership. Due to the strategic nature of the
engagement, the buyer may have to transfer technology and knowledge that is related to its core
competency. The buyer expects a high level of cooperation from the supplier for two reasons.
First, the supplier should exhibit discretion and not misappropriate the technology. Second, the
success of the outsourcing engagement is critical to the long-term success of the buyer (Gilley &
Rasheed, 2000). Buyer-supplier relationship literature has shown that early supplier involvement
leads to positive performance for both the buyer and supplier (Petersen, Handfield, & Ragatz,
2005). Strategic partnership between the buyer and supplier can lead to the supplier being
involved in increased levels of planning, coordination, prioritization and problem-solving
resulting in higher levels of relational rents (Paulraj et al., 2008).
In contrast, we characterize an outsourcing engagement as adversarial relationship when
the strategic importance is high but the relationship quality is low (i.e., supplier ex post
42

opportunistic behavior is high). When the supplier exhibits opportunistic behavior, the level of
trust between the buyer and supplier is low. This may lead to multiple, related problems. The
buyer may be reluctant to reveal core technology to its supplier resulting in incomplete
information transfer to the supplier. The supplier is now providing services based on incomplete
information where the full potential of the relationship is not harnessed resulting in lowered
benefits gained from the outsourcing engagement. In addition, the smooth functioning of day-today activities will be inhibited. The strategic importance of the engagement combined with the
supplier’s opportunistic behavior can create working conditions that call for increased scrutiny of
the decision-making process (Jap & Anderson, 2003). The relationship can be transformed where
more “role players” (Jap & Anderson, 2003) such as supervisors and executive managers get
involved in salvaging the project. This can result in friction between the buyer and supplier that
may exacerbate the already frayed quality of the relationship.
The third configuration where the supplier’s ex post opportunistic behavior is high but the
buyer is engaged in peripheral outsourcing. This relationship configuration is characterized as
arm’s length relationship. Arm’s length relationship is characterized by lower levels of
cooperation between the outsourcing partners. When the supplier exhibits ex post opportunistic
behavior the buyer may disengage from the supplier (Jap & Anderson, 2003). Instead, the buyer
may rely predominantly on the contract in defining the nature of work. Minimal effort is
expended by the buyer toward the maintenance and enhancement of the relationship (Lee et al.,
2004). The buyer may resort to a priori specification of quality of deliverables resulting in
lowered monitoring costs (Aron et al., 2008).
Finally, we consider the fourth configuration where the buyer is engaged in peripheral
outsourcing and the supplier is highly cooperative. Even though the outsourcing engagement is
43

not strategic in nature, the relationship quality between the buyer and the supplier is high. This
evokes higher relational competence between the two parties that can result in long-term benefit
for the buyer. The buyer can leverage the relationship with the supplier resulting in higher
quality deliverables. In addition, the buyer has a unique opportunity to gain new knowledge
from the supplier. Finally, the buyer is able to reduce the overall cost of operations because the
peripheral tasks have been outsourced to the supplier that specializes in those tasks. We
characterize this configuration as selective partnership. The configurations of relationship based
on strategic importance and relationship quality is depicted below in figure 3.

Figure 3 – Configurations of relationships in an outsourcing engagement
3.3.2 Governance configurations
Using the broad classification of transactional and relational governance, we arrive at
four configurations of governance. First consider the configuration with low transactional
44

governance and high relational governance. We characterize this governance configuration as
relation-dominant governance. In this governance configuration, the buyer relies more on
relational norms such as information exchange and shared understanding to resolve uncertainty
in the project. In contrast, when the buyer predominantly relies on transactional governance
mechanisms, the governance configuration is characterized as contract-dominant governance.
The outsourcing partners may communicate and solve problems but the buyer relies primarily on
monitoring the supplier to ensure the time and quality requirements of the outsourcing
engagement are met. When necessary, the buyer may resort to renegotiating the contract to
accommodate changes rather than communicating with the supplier and resolving the issues
through relational mechanisms.
Transaction cost economics (Coase, 1937; Williamson, 1979, 1981; Williamson, 1985)
suggests that firms resort to market transaction when cost of coordination is low. An example of
market-based governance is to pursue a strategy of minimal governance. In this configuration,
the buyer does not monitor the outcomes of the engagement but rather stipulates upfront the
necessary requirements that the supplier may have to fulfill as part of the outsourcing
engagement. Lee et al. (2004) found that firms use a fee-for-service type strategy when pursuing
outsourcing engagements that are selective in nature.
Finally, consider the hybrid governance configuration. In this governance configuration,
the buyer uses both relational and transactional governance mechanisms with high intensities.
Even though relational norms have been touted as important in establishing trust and increasing
performance of buyer-supplier relationships, there are studies that show that reliance on just
relational mechanisms may not be enough. For example, Langfred (2004) found that team
performance reduced when the monitoring of activities was low even though the level of trust
45

among the members was high. Similar findings were found in a buyer-supplier setting by Jeffries
and Reed (2000). They found that too much trust, without appropriate contractual safeguards,
resulted in lower performance. Jap and Ganesan (2000) found that it is easier to mitigate
opportunistic behavior through transactional governance rather than relational governance
mechanisms. Based on these studies, it can be argued that the use of both transactional and
relational governance mechanisms allows the buyer to take advantage of “best of both worlds”.
Whereas relational governance mechanisms allow firms to cooperate and coordinate with the
supplier to resolve uncertainty, contractual governance mechanisms keep the outsourcing
engagement “on track” for successful completion. The governance configurations based on
transactional and relational governance mechanisms are shown in figure 4.

Figure 4 – Configurations of governance mechanisms in an outsourcing engagement

46

3.3.3 Linking the elements (Gestalts)
In this dissertation, it is hypothesized that for each relationship configuration there is an
“ideal configuration” of governance mechanism. That is, there exists a gestalt of governance
configuration for each relationship configuration. Firms should experience superior outsourcing
performance when they use the ideal governance configuration associated with the relationship
configuration. As mentioned earlier, Venkatraman (1989) suggests that fit as gestalts is “defined
in terms of the degree of internal coherence among a set of theoretical attributes”. D. Miller
(1981) emphasizes that it is important to not only consider the strategic choices but also the
conditions under which the choices are made. In this research, configurations of governance
mechanisms represent the “strategic choices” and the conditions are represented by the
relationship configuration.
First, let us consider strategic partnership configuration. The relationship quality
between the buyer and supplier is high resulting in higher levels of cooperation in solving
problems faced in an outsourcing engagement. The buyer and supplier can engage in joint
problem solving in addition to communicating with each other in a proactive manner.
Transactional governance mechanisms have been shown to keep projects on track. Buyer may
engage in transactional governance mechanisms such as monitoring to ensure that the milestones
of the engagement are achieved. There is also evidence that shows that overuse of transactional
mechanisms such as monitoring can result in distrust between outsourcing partners resulting in
lowered performance outcomes. Instead, when the relationship quality is high, transactional
governance mechanisms can act as positive reinforcement toward accomplishing the goals of the
engagement (Jeffries & Reed, 2000). Hence, in strategic partnerships buyers will engage in
hybrid form of governance to gain benefits from the outsourcing engagement.
47

H5: Hybrid governance will provide superior outsourcing performance in a strategic
partnership with its supplier in comparison to other governance configurations
In an adversarial relationship configuration, the activities performed are of high
importance to the buyer because it is related to its core competence. Prior research found that the
principal has a tendency to rely more on transactional governance mechanisms when it
encounters opportunistic behavior from the agent (Eisenhardt, 1989; Stump & Heide, 1996). The
buyer and supplier may not engage in joint problem solving and over time the level of proactive
information exchange between the buyer and supplier may reduce as well. The outsourcing
partners have a potential to be entrenched in a negative spiral of mistrust and rely less on shared
values and norms of the engagement. In order to achieve higher level of outsourcing
performance, the buyer has no other choice but to rely on transactional governance mechanisms
and monitor activities rather than develop relational competence through information exchange
and joint problem solving. Therefore:
H6: Contract-dominant governance will provide superior outsourcing performance in an
adversarial relationship with its supplier in comparison to other governance configurations
Now consider the arm’s length relationship. The relationship is characterized by the
supplier exhibiting higher levels of ex post opportunistic behavior but the firm is engaged in an
outsourcing engagement that is peripheral to its core competence. Due to higher levels of
opportunistic behavior, the buyer may have to rely on transactional governance mechanisms due
to the difficulty in engaging with the supplier in developing relational competence. Furthermore,
the peripheral nature of engagement may result in the buyer stipulating the requirements of the
engagement through the contract rather than closely monitor the actions of the supplier.

48

Researchers have shown that the buyer can avoid “costly monitoring” when the quality outcomes
of the outsourcing engagement are stipulated a priori (Aron et al., 2008). Lee et al. (2004) found
that when a firm is engaged in selective outsourcing, they tend to rely on fee-for-service type of
arrangements. Hence, it is hypothesized that the relationship can be adequately managed using a
minimal governance mechanism (i.e., fee-for-service). Therefore:
H7: Minimal (fee-for-service) governance will provide superior outsourcing performance
in an arm’s length relationship with its supplier in comparison to other governance
configurations
Finally, consider the selective partnership. The relationship quality between the buyer
and supplier is high due to lower levels of supplier ex post opportunistic behavior. An
environment that is conducive for increased information exchange and participation because of
supplier’s cooperative nature. The buyer may benefit by communicating and solving problems
jointly, even though the nature of outsourcing engagement is not related to the core competence
of the buyer. Due to the peripheral nature of the tasks, the buyer may not rely on monitoring the
outsourcing engagement closely. In addition, the buyer may decide that any changes to the
relationship can be handled through information exchange and shared understanding rather than
spending valuable resources on renegotiating the contract. Therefore:
H8: Relation-dominant governance will provide superior outsourcing performance in a
routine relationship with its supplier in comparison to other governance configurations

49

3.4

Summary of research framework
Chapter 3 discussed the new perspectives in managing outsourcing engagement based on

buyer-supplier relationship perspective. This chapter provided a configurational approach to
understanding the management of outsourcing engagements. Specifically, two methods of
configurations are considered. First, fit as profile deviation was presented. Using this approach,
ideal profiles of governance mechanisms that enable effective management of outsourcing
engagements for different risk profiles were identified. The risk profiles were identified based on
two sources of risk – project uncertainty and supplier ex post opportunism. Second, fit as gestalts
was presented. Using this approach, gestalts of governance mechanisms that correspond to the
relationship configuration of the outsourcing engagements was presented. Two factors were
considered in identifying the relationship configurations – strategic importance of the
relationship and supplier ex post opportunistic behavior. To find support for the research
frameworks, an empirical methodology was used. The details of this approach are presented in
the next chapter.

50

CHAPTER 4: RESEARCH METHODOLOGY
This chapter explains the methodological approach used in this dissertation. The data
collection methodology including the sampling frame is first presented. Following this, scale
development and the measures used in this dissertation are presented. Finally, the measurement
validation process and the results are presented.

4.1

Data collection
The data for this research was collected using web-survey methodology. Web-survey

methodology is a cost and time efficient method of collecting data (Dillman, Smyth, & Christian,
2008). Two organizations, Project management institute (PMI) and International association of
outsourcing professionals (IAOP), were approached for data collection. The respondents were
members of buying organization (clients) who belonged to PMI and IAOP. It is important to
identify appropriate sampling frame because it is crucial for the validity of the study (Dillman et
al., 2008; Lessler & Kalsbeek, 1992; Rogelberg & Stanton, 2007). Since the unit of analysis for
this research is an outsourcing engagement, it was deemed appropriate to approach members of
PMI and IAOP. It is also important to seek appropriate respondents who possess adequate
knowledge and appropriate information about the phenomenon being examined (B. K. Boyd,
Dess, & Rasheed, 1993). In accordance with their argument, only respondents who were closely
associated with their outsourcing engagement were asked to participate in the survey. The
respondents held titles such as project manager, program manager, project sponsor, portfolio
manager and project team member. The demographic information on the respondents is provided
in Table 1.

51

In order to gain generalizability of the findings, no restrictions were placed on the
industry association of the respondent. Consequently, the respondents belonged to a varied set of
industries. The industry information of the respondents in the sample is provided in Table 2.
Whereas a majority of the respondents belonged to the manufacturing sector, respondents from
sectors such as professional and technical services, finance and information are represented in
adequate numbers as well. This provides evidence for a larger applicability of the findings from
this research to sectors other than manufacturing sector.
Role

Number of Respondents

% of Total

Project Manager

54

24.66%

Program manager

43

19.63%

Team member

34

15.53%

Project Sponsor

19

8.68%

Portfolio manager

5

2.28%

Other

50

22.83%

Did not specify

13

6.39%

TOTAL

218

100.00%

Table 1 – Respondent demographic information

52

Industry

NAICS Code

Manufacturing

Sample Percentage

31 - 33

53

24.20%

Professional, Scientific, and Technical Services

54

48

22.02%

Finance & Insurance

52

39

17.81%

Information

51

32

14.61%

Health care and social assistance

62

13

5.94%

Utilities

22

7

3.20%

Public Administration

92

6

2.74%

Mining, Quarrying, and Oil and Gas Extraction

21

6

2.74%

Educational services

61

6

2.74%

Retail Trade

44 - 45

4

1.83%

Transportation & Warehousing

48 - 49

2

0.91%

23

1

0.46%

56

1

0.46%

218

100.00%

Construction
Administrative and Support and Waste
Management and Remediation Services
TOTAL
Table 2 – Industry information for the sample

4.1.1 Response rate
Researchers have suggested different methods of calculating response rates (Klassen &
Jacobs, 2001; Lessler & Kalsbeek, 1992). The numerator in the response rate calculation is the
sample size obtained as part of the data collection process. The denominator can be “expressed
as all firms approached, as only deliverable surveys or as only firms expressing interest in the
survey following a pre-notification letter or telephone call.” (Klassen & Jacobs, 2001, p. 714).
Response rate is calculated by dividing sample size by the size of the sampling frame. On the
53

other hand, completion rate is calculated by dividing the sample size by the number of
respondents expressing interest in the research study (Lessler & Kalsbeek, 1992). Researchers
have utilized two approaches to solicit responses through web survey. First, a pre-notification
method is utilized where the respondents are invited to participate in the research study. The
potential respondents are contacted via email or telephone calls to explain the purpose of the
survey. After the initial contact, the link to the web survey is sent only to the respondents who
express interest in the research study. For example, (De Jong & Elfring, 2010) solicited
participation from respondents by sending invitations to the consultants to take the survey. This
approach yielded a response rate of 82%. Similarly, (Gatignon, Tushman, Smith, & Anderson,
2002) reported a response rate of 70% when soliciting responses from R&D directors in their
study. Second approach for soliciting responses is to directly send the link to the survey to the
members of the sampling frame without sending an invitation for participation. Typically, this
method yields low response rate. For example, Cao and Zhang (2011) reported their response
rate as 6% based on the number of emails they sent out and the sample size of the data. The
response rates for studies using web surveys without sending invitations were found to be in the
range of 1% to 10% (Grandcolas, Rettie, & Marusenko, 2003). One of the reasons cited for low
response rate is the fatigue among respondents toward spam email (Grandcolas et al., 2003;
Klassen & Jacobs, 2001; Mehta & Sivadas, 1995).
Some researchers have argued that it is not possible to calculate response rate because the
manner by which the respondents were approached precludes accurate determination of the
number of respondents that were sent the link to the survey. For example, Shapiro, Kirkman, and
Courtney (2007) sought responses from academics and business practitioners to study the
research-practice gap among academy of management (AOM) members. The authors provided a
54

link to the web survey and solicited participation from the members by sending emails using a
listserv email list. Such attempts provided sizeable sample size (n=548) but resulted in the
researchers’ inability to accurately calculate response rate. Due to the method by which the
authors solicited survey participation, the authors estimated that their response rate is between
8% and 10%. Similar response rates were reported by other researchers when they directly
approach the respondents without using a pre-notification method (Grandcolas et al., 2003).
Based on the evidence from literature and the recommendations by Dillman et al. (2008),
the approach of utilizing pre-notification method was considered appropriate. Furthermore, direct
access to the membership of PMI and IAOP was refused by the leaders of both organizations.
The main reason stated was that the members were inundated by email requests to fill out
surveys. The organization’s leaders agreed to send an invitation email that provided an
explanation of the purpose of the research study as well as the contact information of the
researcher. Members who were interested in participating in the study were asked to directly
contact the researcher via email. In total, 289 members expressed interest in filling out the survey
and they were sent a link to the web survey. The respondents were asked to choose a specific
outsourcing engagement when responding to the questionnaire. Reminder emails were sent 3
weeks after the start of the survey period with the link to the web survey included. In total, data
collection lasted for 6 weeks for each organization. Out of the 289 members who expressed
interest, 218 members finished the survey providing a completion rate of 75.4%. In order to
calculate the response rate, the organizations were asked to provide information regarding
number of emails that were sent as part of the original invitation to participate in the research
study. In addition, the organizations were asked to provide information on number of valid
emails as well as demographic information of the potential respondents. Unfortunately, both
55

organizations mentioned that their mailing lists are not up-to-date. That is, the representatives of
both organizations mentioned that they do not remove email addresses that are invalid from the
mailing list. In addition, they were unable to provide demographic information for their
membership. Based on the information provided by the organizations the accurate response rate
cannot be calculated. This research utilized similar data collection methodology as used by
aforementioned researchers. In addition, the completion rate of the survey is comparable to the
studies that used pre-notification method for inviting respondents to participate in the survey.
Based on these criteria, it is estimated that the response rate is between 3% and 6%.
4.1.2 Nonresponse bias tests
Nonresponse bias tests were conducted in two ways. The demographic information (firm
size, relationship length, outsourcing experience) between early and late respondents were
compared. In addition, the responses for each construct for the early and late respondents were
compared. The sample was divided into early respondents (25%) and late respondents (25%) for
comparison. These tests assume that the characteristics of late respondents are equivalent to the
characteristics of nonrespondents (Armstrong & Overton, 1977). The variables were compared
between early and late responders using t-tests.
Three demographic variables - number of employees, outsourcing experience and
relationship length – were used for nonresponse bias testing. The results indicate that there were
no significant differences between early and late respondents with respect to number of
employees (p < 0.926), outsourcing experience (p < 0.343) and outsourcing relationship length
(p < 0.942). Further tests were conducted by comparing the constructs used in this research. The
results indicate that the differences are not significant for all constructs except monitoring and

56

shared understanding. The results indicate that early responders used lower levels of monitoring
(p < 0.008) and shared understanding (p < 0.027). Given the purpose of the test is to reveal if
there is a propensity among nonrespondents to avoid providing feedback. That is, the use of
governance mechanisms should be lower among late responders in comparison to early
responders in order to arouse concern about nonresponse bias. On the contrary, the results
indicate the opposite. Furthermore, there were no differences found among dependent variables –
outsourcing performance and learning outcomes. Taken together, it was determined that the
threat due to nonresponse bias may not be significant.

4.2

Scale development
Measurement instrument was developed to test the phenomena of interest. When

possible, existing scales from prior studies were used. Pretest was conducted to assess the
validity of the instrument (Dillman, 1978; Dillman et al., 2008). Pretest is an assessment of the
survey instrument by the members of the target population and knowledgeable members of the
academic community to validate the instrument for format, content and comprehension in order
to elicit accurate response from the respondents (Fowler, 2009; Vogt & Johnson, 2011;
Weisberg, Krosnick, & Bowen, 1996). For this purpose, the instrument was subjected to Q-sort
(Moore & Benbasat, 1991), a form of pretest method.
Two rounds of pretest were administered to two groups of five judges. Each group
received half of the questionnaire in the first round. During the second round, the target groups
were reversed. Thus, the items were subjected to two rounds of pretest but different set of judges
were used for each portion. For each round, the constructs with descriptions were provided along
with items in a random order. At the end of first round, the constructs and items were analyzed
57

based on the item placement scores (Moore & Benbasat, 1991). Items were either reworded or
dropped based on the level of cross loadings with other constructs. Items that were dropped were
replaced with new items. The updated instrument was subjected to a second round of pretest. The
raw agreement scores (Moore & Benbasat, 1991) improved from 71.3% at the end of first round
to 84.24% at the end of the second round. The modified and improved instrument was
administered to the respondents.

4.3

Measures
This section provides details on the source for survey instruments used in this

dissertation.
4.3.1

Opportunism
Opportunism has been identified as an important construct in both transaction cost

economics (Coase, 1937; Williamson, 1979, 1981; Williamson, 1985) and agency theory
(Eisenhardt, 1989). Opportunism is defined as self-interest seeking with guile (Jap & Anderson,
2003). Guile can comprise of activities that can be constituted as “calculated efforts to mislead,
distort, disguise [or] obfuscate” (Williamson, 1985, p. 47). In the literature, opportunism has
been defined as any activity pursued by one of the partners that is not in the best interest of the
relationship. In the context of this research, opportunism is defined as actions that a supplier can
exhibit that are not in the short-term or long-term interest of the buyer. Opportunism has been
found to manifest itself in both ex ante (i.e., before the beginning of a relationship) and ex post
(i.e., after the relationship has commenced) forms. There is a distinction between these two
forms of opportunistic behaviors. The buyer may be exposed to ex ante opportunism when it is in

58

the process of selecting a supplier. The supplier may misrepresent and position itself as
possessing the skills required for the task even though it may not actually possess those skills.
This scenario creates an adverse selection problem because the buyer is choosing the supplier
based on information that is not completely accurate.
Once the outsourcing engagement is underway, the supplier can still exhibit opportunistic
behavior. In this scenario, the supplier may exhibit ex post opportunistic behavior by not acting
in the best interest of the supplier. The supplier may show lowered interest through different
actions. For example, the level of commitment shown to the task may not be at the desired level
that is required for the success of the relationship. The actual effort exerted by the supplier may
be less than required level, resulting in quality problems. The supplier may decide to act in a
manner that can cause intended or unintended delays when accomplishing the tasks. It is not
necessary that the supplier is in explicit violation of the contract, rather the supplier can be
exhibit ex post opportunistic behavior by failing to fully comply with the requirements of the
project. Taken together, the supplier’s actions can be characterized as creating a moral hazard
problem for the buyer. Research has shown that both forms of opportunistic behavior have a
negative influence on performance in inter-organizational relationships (Jap & Anderson, 2003;
Tangpong, Hung, & Ro, 2010). This dissertation primarily focuses on the actions that a buyer
can take to mitigate the moral hazard problem. That is, the focus of this research is on ex post
opportunistic behavior exhibited by the supplier.
Despite its importance, very few studies have measured the construct due to difficulty in
overcoming social desirability bias (Jap & Anderson, 2003). That is, it is very difficult to solicit
respondents to self-report on their own opportunistic behavior. To overcome this issue,
researchers have relied on other techniques to adequately measure opportunism in buyer-supplier
59

relationships. For example, Schilling and Steensma (2002) measure opportunism as a perception
of the buyer on threat of being taken advantage and level of oversight that is needed to thwart the
effects of opportunism on performance. Ang and Cummings (1997) mention that when a buyer
does not experience a “lock-in” situation (Narasimhan et al., 2009) the threat due to opportunism
is reduced. They use this concept and operationalize threat due to opportunism as presence of
alternate suppliers that are reputable and trustworthy. Interestingly, Schilling and Steensma
(2002) and Ang and Cummings (1997) refer primarily to ex ante opportunism where the buyer
can choose different suppliers before entering into the relationship. Their measures are not
suitable because the primary focus of this research is to evaluate supplier ex post opportunistic
behavior (moral hazard problem) rather than ex ante opportunistic behavior (adverse selection
problem). Jap and Anderson (2003) developed scales that overcome the issues mentioned above.
They measure ex post opportunism by asking the respondent to assess the opportunistic behavior
exhibited by their partner. In this research, their measures are adapted for outsourcing
engagements and respondents from the buyer organization were asked to assess the ex post
opportunistic behavior exhibited by the supplier.
4.3.2 Project Uncertainty
Project uncertainty in an outsourcing engagement arises due to lack of clarity in
priorities. Typically, outsourcing engagements need to execute a series of interdependent tasks in
to accomplish the goals of the outsourcing engagements. The degree of interdependency between
tasks increases with the increase in scope of the project (Shenhar 2001). When the stakeholders
of the project cannot reach consensus on the goals of the project and the priorities, it is difficult
to define the requirements of the project with ease. Consequently, the project team members are
unable to anticipate any problems that may be encountered during execution because they lack a
60

clear understanding of the requirements of the project. Researchers have considered the novelty
of technology and complexity of the tasks as contributing factors to uncertainty of tasks (Stock &
Tatikonda, 2000; Tatikonda & Rosenthal, 2000). Typically, the newness of a project can create
uncertainty due to the learning process involved in fully understanding the end-state of the
project requirements. Furthermore, technological uncertainty can play an important role as well.
That is, the technology being used to achieve the tasks can create barriers to smooth execution of
the outsourcing engagement. Taken together, project uncertainty addresses the uncertainty in an
outsourcing engagement arising out of lack of clarity of requirements, interdependency among
tasks, inability to prioritize important tasks and newness of technology being used. The measures
for project uncertainty were adapted from Bendoly and Swink (2007) and Nidumolu (1995).
Bendoly and Swink (2007) study the impact of task uncertainty on task performance. Nidumolu
(1995) studied the impact of project uncertainty in IT projects. Items from these studies were
adapted to capture uncertainty in an outsourcing engagement.
4.3.3 Governance mechanisms
Both transactional and relational governance mechanisms are measured in this study.
Transactional governance mechanisms comprise of monitoring, contract flexibility and
transaction-specific investments. Relational governance mechanisms are derived from relational
norms (Heide & John, 1992; MacNeil, 1980). Two relational norms are considered - information
exchange and shared understanding. The measures for these governance mechanisms are
discussed in this section.
Monitoring mechanisms enable a buyer to verify that the supplier is in compliance with
the requirements of the outsourcing engagements. Through monitoring, the buyer is able to

61

ensure that the supplier is adequately providing deliverables that meet the quality standards and
other established performance standards. Agency theory (Eisenhardt, 1989) suggests that the
principal can either monitor the behavior of the agent or the outputs. Typically, the decision to
use either behavioral monitoring or output monitoring depends on the ability of the principal to
adequately gauge the deliverables of the agents. Furthermore, monitoring has been shown to be
required even when adequate ex ante effort has been expended in selecting suppliers
(Williamson, 1993). The items for monitoring were derived from Stump and Heide (1996), who
studied the impact of monitoring on opportunistic behavior of the supplier. In addition, findings
from Ellram et al. (2008) were utilized to create measures for supplier reporting. Their study
found that suppliers comply with goals of the engagement when they are required to provide
periodic reports on the progress of the outsourcing engagement.

62

Construct

Loadings

ITEM

Source

How often does your supplier do the following? (1 = Hardly ever; 3 = Sometimes; 5 = Very often)
0.803

Our supplier made hollow promises

Jap & Anderson, 2003

0.810

Our supplier violated compliance with project requirements
Our supplier expected us to pay more than agreed upon costs to correct
problems
Our supplier shirked responsibility for meeting project requirements
Our supplier made false claims about agreements made during the
engagement
Our supplier provided false information

Jap & Anderson, 2003

ex post
Opportunism

0.718

CR = 0.915;
AVE = 0.643

0.864
0.835
0.772

Jap & Anderson, 2003
Jap & Anderson, 2003
Jap & Anderson, 2003
Jap & Anderson, 2003

Please indicate your level of agreement with the following statements. (1 = Strongly Disagree; 3 = Neither Agree nor Disagree; 5 =
Strongly Agree)
In this engagement, objectives were not well defined until late in the
Bendoly & Swink, 2007;
0.804
project life-cycle
Nidumolu, 1995
0.799
In this engagement, we had conflicting requirements
Nidumolu, 1995
Project
uncertainty
In this engagement, actions that were beneficial to the success of the
0.754
Bendoly & Swink, 2007
engagement were difficult to determine
CR = 0.799;
In this engagement, technology required to complete the project was
AVE = 0.511
Nidumolu, 1995
readily available (R)
Bendoly & Swink, 2007;
0.437
In this engagement, it was difficult to anticipate execution problems
Nidumolu, 1995
Table 3 – Risk constructs, items and sources

63

Contract flexibility is used to overcome the problem of bounded rationality by the buyer
in an outsourcing engagement. Typically, in an outsourcing engagement it is difficult to specify
all the contingencies a priori. Through the use of contract flexibility (i.e., contingency planning),
the buyer is able to adjust the scope of the outsourcing engagement after it is underway. N. S.
Argyres et al. (2007) examined the evolution of a contract and the level of contingency planning
conducted by the buyer and supplier. They contend that the TCE argument that the buyer is able
to instantaneously evaluate the cost of coordination is not valid in a relationship. Typically, the
parties involved learn during the life of the relationship and thus need to make adjustments. Two
different forms of contingency planning are identified – generic and specific contingency plans.
Generic contingency planning specifies the process by which the buyer and supplier agree on the
process by which changes are made. In contrast, specific contingency planning can include
details of changes that can be made to the contract when the engagement encounters unforeseen
situations. Even though N. S. Argyres et al. (2007) provided detailed description of contingency
planning, they used only a single-item measure citing time-constraints among their respondents.
In this dissertation, their study was used to develop a set of new items that reflect contingency
planning as the level of flexibility retained in the contract by the buyer. New items are derived
from the description of this study to capture both generic and specific contingency plans that the
buyer may pursue as the contract progresses.
Transaction-specific (TS) investments in buyer-supplier relationships have been
considered important by researchers (Jap & Anderson, 2003; Liu et al., 2009; Nyaga et al.,
2010). TS investments create safeguards against opportunism because the parties involved have a
vested interest in the success of the relationship. In this research, transaction-specific investments
construct is measured using the items derived from Anderson and Weitz (1992). This study has
64

been used by many researchers who have measured transaction specific investments (Jap &
Anderson, 2003; Liu et al., 2009; Nyaga et al., 2010). The items were adapted for an outsourcing
engagement setting to reflect the level of effort and time invested by the outsourcing partners. In
addition, it also captures the information regarding specific processes and procedures that were
implemented for the outsourcing engagement.
Information exchange has been shown to provide an advantage in inter-organizational
relationships. Through information exchange, the outsourcing partners are able to keep each
other informed on important developments within the outsourcing engagement. Furthermore,
information exchange can result in synchronization of activities in an outsourcing engagement.
Through information exchange the buyer and supplier can also foster partnership and build trust
that can result in reduction in opportunism. Information exchange has been measured by many
OM researchers (Cousins & Menguc, 2006; Monczka et al., 1998; Paulraj et al., 2008). Cousins
and Menguc (2006) use a three item scale to measure communication. Paulraj et al. (2008)
measure type and frequency of information exchanged between the buyer and supplier. Monczka
et al. (1998) suggest that communication involves information quality, information participation
and information sharing. Their measure of communication covers the depth, breadth and type of
information being shared between supply chain partners. In this study, items from Paulraj et al.
(2008) were adapted to measure communication because it addresses the frequency of
information exchange as well as formality of information exchange.
Finally, Shared understanding has been shown to be essential in joint problem solving
between the outsourcing partners. Through shared understanding, the outsourcing partners are
able to resolve any disagreements regarding the actions that need to be taken in the outsourcing
engagement. Typically, shared understanding requires repeated interaction between the
65

outsourcing partners. Such repeated interactions dispel the inefficiencies in information
exchange and allows the partners to quickly identify and exchange pertinent information that is
essential for problem-solving. Despite its importance, few studies have measured this construct
to examine buyer-supplier relationships (Fugate et al., 2009; Koufteros, Vonderembse, & Doll,
2002). Only Fugate et al. (2009) measured the level of shared understanding between
organizations. Their items measured the shared understanding in a logistics setting. The items are
adapted to measure shared understanding in an outsourcing context. Tables 4 and 5 show the
constructs, items and their sources for governance mechanism constructs.
4.3.4

Outsourcing performance and learning outcomes
In this research, outsourcing performance has been conceptualized as project

performance. The immediate concern for project managers when managing outsourcing
engagements is time and cost performance. Furthermore, scope of the outsourcing engagement
has an impact as well. Hence, outsourcing performance has been operationalized using time
performance, cost performance, quality of deliverables and the technical performance. It is
difficult to obtain actual data (i.e., objective data) from outsourcing engagements. Practitioners
are reluctant to reveal this information because of company policy or proprietary nature of the
information. Hence, outsourcing performance has been measured using perceptual scales. Items
developed by Lewis et al. (2002) were adapted to measure outsourcing performance.
Researchers have established that firms engage in strategic outsourcing to gain
capabilities (Gottfredson et al., 2005; Holcomb & Hitt, 2007). Capabilities that are gained can
differ based on the nature of the engagement. For example, firms can gain key technological
capabilities that can be incorporated into the firm’s products. Firms may gain capabilities that are

66

more commercial in nature. For example, 7-Eleven utilized their outsourcing engagements to
gain commercial objective of being more competitive in the marketplace (Gottfredson et al.,
2005). This research combines these objectives together as learning outcomes. Firms look to
gaining valuable knowledge that can be utilized in ongoing operations. Thus, learning outcomes
allow firms to improve their overall capabilities. Lewis et al. (2002) measure both technical and
commercial objectives of an outsourcing engagement. In this research, the items developed by
Lewis et al. (2002) are utilized to measure the learning outcomes gained by buyers in the
outsourcing engagement. The items tap into the construct by measuring the technical and
proprietary knowledge as well as information gained from the outsourcing engagement. In
addition, the items also measure the commercial objectives and overall capabilities that the buyer
gained through the outsourcing engagement. Table 6 shows the constructs, items and their
sources for performance and outcomes constructs.
4.3.5 Controls
Literature has established that the success of a project is dependent on its criticality to top
management (Tatikonda & Rosenthal, 2000). Top management scrutiny is higher for critical
projects, increasing the probability of success of a project. Thus, it is important to control for
such confounding factors. This research will utilize a one-item measure adapted from (Tatikonda
& Rosenthal, 2000) to gauge the criticality of the outsourcing engagement.
Firms are outsourcing both domestically as well as offshore. Cultural distance between
the buyer and supplier can pose significant inefficiencies due to communication challenges. This
can result in increased uncertainty in the engagement. Whereas some researchers have developed
perceptual measures to capture cultural uncertainty, many OM researchers have used cultural

67

distance as a proxy measure for cultural uncertainty (Cheung, Myers, & Mentzer, 2010;
Kaufmann & Carter, 2006). Cultural uncertainty will be measured using cultural distance
measure using Hofstede’s measures. Cultural distance will be measured using the methodology
used by (Kogut & Singh, 1988). In addition, the following controls are used - firm size,
experience in managing outsourcing projects and length of relationship between the buyer and
supplier. Buying firm’s size can increase the power of the buyer in the relationship. In addition,
buying firm’s experience in managing outsourcing projects create tacit knowledge and
organizational routines that are utilized in managing outsourcing engagements. Finally, the
length of the relationship between the buyer and supplier can create routines in the relationship
that can potentially substitute the governance mechanisms. Hence, these factors are used as
controls.

68

Construct

Loadings

Item

Source

Please indicate your level of agreement with the following statements. (1 = Strongly Disagree; 3 = Neither Agree nor Disagree; 5 =
Strongly Agree)
0.665
In this engagement, we performed frequent formal reviews Lewis et al 2002; Tatikonda &
throughout the project
Rosenthal, 2000
0.767
In this engagement, we periodically required deliverables
Ellram, Tate & Billington, 2008
Monitoring
from the supplier
CR = 0.801;
0.808
In this engagement, we continuously monitored the quality Stump & Heide, 1996
AVE = 0.505
of deliverables
0.580
In this engagement, we continuously monitored key phases Stump & Heide, 1996; Lewis et al 2002
of the project using metrics
Please indicate your level of agreement with the following statements about the relationship between your firm and the supplier. (1 =
Strongly Disagree; 3 = Neither Agree nor Disagree; 5 = Strongly Agree)
Transaction
specific
investments

0.684

We made significant investments specific to this
Adapted from Anderson & Weitz, 1992;
relationship
Jap & Anderson, 2003
0.938
We expended a high level of effort to maintain this
Adapted from Anderson & Weitz, 1992;
relationship
Jap & Anderson, 2003
CR = 0.802;
We instituted processes and procedures that are specific to Adapted from Anderson & Weitz, 1992;
AVE = 0.674
Jap & Anderson, 2003
this relationship
Please indicate your level of agreement with the following statements. (1 = Strongly Disagree; 3 = Neither Agree nor Disagree; 5 =
Strongly Agree)
0.443
In this engagement, contract terms were flexible to
Derived from Argyres, Bercovitz &
Contract
accommodate changes
Mayer (2007)
flexibility
0.485
In this engagement, contract terms were renegotiated based Derived from Argyres, Bercovitz &
CR = 0.525;
on changing needs
Mayer (2007)
AVE = 0.274
0.624
In this engagement, detailed agreements were crafted to
Derived from Argyres, Bercovitz &
manage contingencies
Mayer (2007)
Table 4 – Transactional governance constructs, items and sources

69

Construct

Loadings

Item

Source

Please indicate your level of agreement with the following statements about the relationship between your firm and the supplier. (1 =
Strongly Disagree; 3 = Neither Agree nor Disagree; 5 = Strong Agree)
0.765
Information
exchange
CR = 0.775;
AVE = 0.486

We exchange information frequently

Paulraj, Chen, Lado, 2008

0.309

We exchange information informally

Paulraj, Chen, Lado, 2008

0.784

We exchange information in a timely manner

Paulraj, Chen, Lado, 2008

0.803

We keep each other informed about any changes that may
affect the other party

Paulraj, Chen, Lado, 2008

Please indicate your level of agreement with the following statements about the relationship between your firm and the supplier. (1 =
Strongly Disagree; 3 = Neither Agree nor Disagree; 5 = Strong Agree)
Adapted from Ko, Kirsch & King,
We agree on what is important in this engagement
2005

CR = 0.829;
AVE = 0.619

0.807

We quickly resolve disagreements

Fugate, Stank & Mentzer, 2009

0.840

We quickly reach agreement on the use of new information

Adapted from Fugate, Stank &
Mentzer, 2009

0.707

Shared
Understanding

We share similar understanding when changes occur during
the engagement

Adapted from Fugate, Stank &
Mentzer, 2009; Ko, Kirsch & King,
2005

Table 5 – Relational governance constructs, items and sources

70

Construct

Loadings

Item

Source

As a result of this engagement, we (1 = Strongly Disagree; 3 = Neither Agree nor Disagree; 5 = Strongly Agree)
0.817

gained valuable technical knowledge

Lewis et al 2002

0.547

gained information helpful to other ongoing engagements

Lewis et al 2002

0.461

improved our overall capabilities

Lewis et al 2002

met our commercial objectives

Lewis et al 2002

introduced products/services to market in a timely fashion

Lewis et al 2002

developed products/services with reasonable costs

CR = 0.845;
AVE = 0.542

Lewis et al 2002

0.938
Learning
Outcomes

gained proprietary technical knowledge

Lewis et al 2002

0.485

How do you characterize the performance of the engagement on these measures? (1 = Very disatisfied; 3 = Neutral; 5 = Very
satisfied)
0.599
On-time performance (i.e., schedule)
Lewis et al 2002
Performance

0.604

Actual costs (i.e., budget)

Lewis et al 2002

CR = 0.789;
AVE = 0.488

0.792

Quality of the deliverables

Lewis et al 2002

0.775

Technical performance

Lewis et al 2002

Overall satisfaction

Lewis et al 2002

Table 6 – Performance constructs, items and sources

71

4.4

Measurement reliability and validity
Confirmatory factor analysis (CFA) was performed using structural equations model.

EQS 6.1 was used to conduct the CFA. Measurement model fit was assessed using fit statistics
2

such as chi-squared statistic (χ ), comparative fit index (CFI), Non-Normed fit index (NNFI),
root mean squared error approximation (RMSEA) and standardized root mean square residual
2

(SRMR). The confirmatory factor analysis yielded χ (524) = 830.230, CFI = 0.912, NNFI =
0.904, RMSEA = 0.052 and standardized RMR = 0.071. The model fit indices are considered
acceptable because the fit indices are above the recommended thresholds (Hu & Bentler, 1999).
To assess the reliability, convergent validity and discriminant validity of the constructs
further analysis was conducted as suggested by Fornell and Larcker (1981). The composite
reliability of most constructs is above the criteria except for contract flexibility (CR = 0.525) that
falls below the threshold value of 0.70. The convergent validity establishes that the manifest
variables collectively tap into the latent meaning of the construct. The convergent validity of the
items generally exceeds the threshold value of 0.6. The loadings for each item are listed in tables
3, 4, 5 and 6.
Discriminant validity of the constructs were assessed based on the average variance
extracted (AVE) for each measurement scale. The values for each of the scales should equal or
exceed 0.50 (Fornell & Larcker, 1981). This test for AVE is a stronger test than other tests.
Average variance extracted for most of the constructs either exceed or is very close to the
threshold value. AVE for contract flexibility is lower than this threshold but the squared value of

72

AVE exceeds the correlations between contract flexibility and other constructs, indicating that
the construct is distinct from other constructs.
Since the data were collected using a single survey and a single respondent, there is a
potential for common method bias. Harman’s one factor test was conducted to test for this bias.
All items were assigned to a single factor (Podsakoff, MacKenzie, Jeong-Yeon, & Podsakoff,
2

2003). Harman’s one factor test yielded χ (531) = 2574.575, CFI = 0.421, NNFI = 0.388,
RMSEA = 0.133 and standardized RMR = 0.206. The fit for the single factor model was very
poor and the chi-square change from the hypothesized model was highly significant. The
potential for common method bias was found to be non-significant because of the poor fit of the
single factor model and the good fit for the hypothesized measurement model. Table 7 shows the
correlation matrix for the constructs in this study.

4.5

Summary of research methodology
In this chapter the research methodology used in this dissertation was presented in detail.

The data collection methodology was first presented. The data were collected from Project
Management Institute (PMI) and International Association of Outsourcing Professionals (IAOP).
The scale development methodology and the measures used in this dissertation were presented in
detail. Finally, the measures were validated and the results of the validation process were
presented. The next chapter will present the analysis method as well as the results of the analysis
and testing.

73

1

2

Opportunism

2
3

-0.142

Monitoring

4

-0.251

Transaction specific
investments

5

Shared Understanding

6

-0.530

Information exchange

7

-0.479

Outsourcing
performance

8

-0.657

Learning outcomes

9

-0.204

5

6

7

8

0.514

Contract Flexibility

4

1

Project Uncertainty

3

**
*

**

-0.034
**
**
**
**

-0.131
-0.338

†

**

-0.091
-0.413
-0.384
-0.483
-0.201

**
**
**
**

**

0.280

**

0.122

0.365
**

0.292

**

0.202

0.115
0.127

†
†

† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001
Table 7 – Correlation matrix

74

**

0.222

**

0.404

**

0.245

0.120

†

0.082
*

**

0.133

0.548

0.091

0.524

**

0.224

**
**

0.327

**

0.541

**

0.342

**

0.399

CHAPTER 5: ANALYSIS AND RESULTS
This chapter presents the analysis and results of this dissertation. The analysis is
conducted in two phases. First, the data are analyzed for fit as profile deviation. Following this,
analysis is conducted for fit as gestalts. Detailed explanation of each analysis method and results
are presented in the following sub-sections.

5.1

Fit as profile deviation
Fit as profile deviation is the “degree of adherence to an externally specified

profile”(Venkatraman, 1989, p. 433) that allows for a multi-dimensional assessment of fit.
Furthermore, Venkatraman suggests that the degree of adherence to an ideal profile by a business
unit for a given environment can be related to performance, thus identifying an “environmentstrategy” co-alignment. This approach is utilized to examine the governance mechanism and risk
co-alignment leading to improved outsourcing performance and learning outcomes in an
outsourcing engagement. In this dissertation, risk is characterized by project uncertainty and
supplier ex post opportunistic behavior. Configurations of risk are identified based on the level of
risk in an outsourcing engagement. Following this, the ideal profile of governance mechanisms
for each risk configuration is identified. The details of the analysis method and the results are
shown in the following sections.
5.1.1

Risk profiles
To assess the ideal profile of governance mechanisms, the “environments” (i.e., risk

profiles) for the outsourcing engagements were identified. In this dissertation, we theoretically
argued for the existence of four risk profiles based on the level of project uncertainty and
75

supplier ex post opportunism. The sample was divided into four groups using median values for
project uncertainty and supplier ex post opportunism. This approach is similar to the approach
used by other studies in the literature (Germain, Claycomb, & Dröge, 2008; Tangpong et al.,
2010; J. B. Wu, Tsui, & Kinicki, 2010). The sample was classified into unstable (n=48),
uncooperative (n=56), high-risk (n=61) and routine (n=53) groups.
5.1.2

Outsourcing performance
Once the risk profiles were identified, an ideal profile of governance mechanisms for

each configuration was identified. The ideal profile of variables can be identified either based on
theory or using empirical method (Venkatraman, 1989). In this dissertation, the ideal profile is
identified through empirical method. Each risk configuration is treated as a separate
“environment”. When creating an ideal profile, not all variables can be given equal importance
(Venkatraman & Prescott, 1990). In order to identify salient governance variables, the
outsourcing performance was regressed on the governance variables for each risk configuration
separately. Appropriate control variables were included in the regression equations. The equation
for identifying the ideal profiles is shown below:
Outsourcing performance =

β1 * (Monitoring) + β2 * (Contract flexibility)
+ β3 * (Transaction-specific investments)
+ β4 * (Information exchange)
+ β5 * (Shared Understanding) + β6 * (Criticality)
+ β7 * (Length of relationship) + β8 * (Outsourcing experience)
+ β9 * (Firm Size) + β10 * (Cultural Distance)

76

(1)

The analysis for profile deviation was conducted separately for each risk configuration.
Stepwise regression was used to arrive at salient governance mechanisms. Stepwise regression
was used because it helps to “compensate for the relatively small sample sizes…” (Flynn &
Flynn, 2004, p. 445). In this research, the sample sizes for each risk profile group is relatively
small. Hence, the use of stepwise regression in arriving at salient governance mechanisms was
utilized. For the variables, the criterion for entry was set at p < 0.05 and the criterion for exit was
set at p < 0.10. The model for all risk profiles are shown in Table 8. The detailed outputs from
SPSS are included in Appendix B. The results of this analysis are discussed below.
The sample size for unstable risk configuration (low opportunism – high project
2

uncertainty) group is 48. The R for this regression is 0.126 and the F statistic is 6.643. The
results show that shared understanding is the key driver of outsourcing performance for firms
with unstable risk profile. For this configuration none of the control variables were statistically
significantly related to outsourcing performance. Similarly, none of the transactional governance
mechanisms variables were statistically significantly related to outsourcing performance.
The sample size for uncooperative risk configuration (i.e., high opportunism – low
project uncertainty) is 56. The results in column 2 indicate that the salient governance
2

mechanism for this risk configuration is information exchange. The R is 0.178 and F statistic is
11.720 indicating that the fit is good. For this configuration none of the control variables were
statistically significantly related to outsourcing performance. Similarly, none of the transactional
governance mechanisms variables were statistically significantly related to outsourcing
performance.

77

The sample size for routine configuration (low opportunism – low uncertainty) is 61. The
results in column 3 indicate that the salient governance mechanism for outsourcing engagements
2

in the routine configuration is information exchange. The R is 0.166 and F statistic is 5.759,
indicating a satisfactory fit. In this risk profile, cultural distance is negatively and statistically
significantly related to outsourcing performance.
The sample size for high-risk configuration (i.e., high opportunism – high project
2

uncertainty) is 53. The R for this risk configuration is 0.456 and F statistic is 13.694, indicating
a good fit. The results in column 4 indicate that the salient governance mechanisms for
outsourcing engagements with high-risk profile are shared understanding and information
exchange. Interestingly, contract flexibility has a negative influence on outsourcing performance.
Similar to unstable and uncooperative risk profiles, none of the control variables are statistically
significantly related to outsourcing performance.
2

Finally, similar analysis was conducted for the entire sample. The R for the total sample
is 0.367, indicating a good fit. The results indicate that the salient governance mechanisms that
influence outsourcing performance are shared understanding and information exchange.

78

Risk group
(Group Number)

N

R

F

Unstable (1)

48

0.126

6.643

Uncooperative (2)

56

0.178

11.720

Routine (3)

61

0.166

5.759

2

Significant Independent
Variables

Coefficient

Standardized
Coefficient

t

*

Constant
Shared understanding

15.254
0.882

0.355

53.730
2.577

***

Constant
Information exchange

13.899
0.980

0.422

49.131
3.423

Constant
Cultural distance
Information exchange

16.516
-0.364
0.597

-0.244
0.313

70.804
-2.028
-2.604

Constant
Information exchange
Shared understanding
Contract flexibility

13.925
1.147
0.970
-0.571

0.473
0.378
-0.261

43.577
3.961
2.851
-2.182

Constant
Information exchange
Shared understanding

14.706
0.950
0.850

0.363
0.325

103.899
5.600
5.011

**

***

High-risk (4)

53

0.456

13.694

Total Sample

218

0.367

62.359

***

† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001
Table 8 – Governance mechanisms significantly related to outsourcing performance

79

Misalignment measure was calculated for all risk profiles using equation (2) shown
below. The top 10% of cases for each risk profile group are considered to be high performers of
that group. To prevent skewing of the distribution, the bottom 10% performers are removed from
the sample. The remaining cases are considered to be sub-sample for each risk profile group.
Misalignment score was calculated for each case belonging to the sub-sample. The misalignment
score is the composite deviation from the “ideal profile” of governance mechanisms for the
case’s risk profile. The misalignment score is calculated as the sum of weighted Euclidean
distance between the calibration sample and sub-sample based on salient governance
mechanisms. The Euclidean distances were weighted appropriately by multiplying the
standardized beta coefficient of the significant salient governance mechanism to ensure that
appropriate weightage is given to each governance mechanism.

m

MISALIGN1 =



  bi X si  X ci 2 



(2)

i 1
Where,

bi = standardized weights of significant governance variables from (1)

X ci = average score of the calibration sample (i.e. high performers) for each governance

variable

X si = score for each of the study sample for each governance variable

In total, 182 cases were included in the pooled sample. These cases are sub-samples from
unstable, uncooperative, routine and high-risk profiles. The calibration sample for unstable risk
profile included 5 cases with the highest outsourcing performance. In addition, 5 cases with the
lowest performance (bottom-performers) were excluded to create a sub-sample with 38 cases.
Similar samples were created for the other risk profiles. For uncooperative risk profile, the
80

calibration sample included 3 cases and bottom performers included 8 cases. The resulting sub
sample of 45 cases was used to create a misalignment score for this risk configuration. For
routine risk profile, the calibration sample included 8 cases with highest performance. After
removing the lowest performing 9 cases, the resulting sub sample of 44 cases were included in
the sub-sample for this risk configuration. Finally, for the high-risk profile group, the calibration
sample included 4 cases with highest performance. After removing the lowest performing 4
cases, the resulting sub sample for this group is 45 cases.
Outsourcing performance was regressed on the misalignment score for the cases
belonging to the pooled sub-sample. Control variables were included in this regression as well.
The regression analysis was conducted by introducing the control variables, main effects
variables and the interaction term in a hierarchical manner. The results of the regression models
are shown in Table 9.
Some important patterns emerge based on these results. The misalignment measure is
negatively related to outsourcing performance, even in the presence of control variables. This
shows support for hypothesis H1 that the deviation from an ideal profile of governance
mechanisms will result in deterioration of outsourcing performance. The results also indicate that
strategic importance did not directly influence outsourcing performance (i.e., main effect was not
statistically significant). The interaction term was not significant either. The results indicate that
the strategic importance of outsourcing engagement (i.e., core vs. peripheral) does not impact the
relationship between outsourcing performance and the misalignment measure. Thus, hypothesis
H2 was not supported. The implication of these results is that the deviation from the “ideal
profile” results in deterioration in outsourcing performance but the strategic importance does not
have an impact on outsourcing performance. The theoretical and managerial implications of
81

these results and important patterns of governance mechanisms for all the risk profiles are
explored in the discussion section.
Variables

Model 1

Model 2

Model 3

Criticality

-

0.012

0.013

Outsourcing experience

-

0.101

0.100

Relationship length

-

-0.073

-0.072

Firm size

-

-0.060

-0.059

Cultural distance

-

-0.049

-0.049

Misalignment

-0.270

Strategic Importance
Misalignment * Strategic
Importance
2

***

0.124

-0.277

***

0.124
0.015

-

0.106

0.106

2.786

2.427

ΔR

0.106

0.000

Change in F

2.786

0.033

d.f.

(7, 164)

(1, 163)

p-value (change)

0.009

0.855

182

182

R

F
2

Size of the group

182

† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001
Table 9 – Relationship between misalignment measure and performance
5.1.3

Learning outcomes
Similar analysis was conducted with learning outcomes as the dependent variable. First,

the significant governance variables were assessed by regressing learning outcomes on the
governance variables for each risk configuration separately. The regression equation is shown

82

below (equation 3). Stepwise regression was used to arrive at salient governance mechanisms.
The results of this analysis for each risk configuration are shown in Table 10.
Learning outcomes =

β1 * (Monitoring) + β2 * (Contract flexibility)
+ β3 * (Transaction-specific investments)
+ β4 * (Information exchange)
+ β5 * (Shared Understanding) + β6 * (Criticality)
+ β7 * (Length of relationship) + β8 * (Outsourcing experience)
+ β9 * (Firm Size) + β10 * (Cultural Distance)

(3)

The sample size for unstable risk configuration (low opportunism – high project
uncertainty) group is 48. For this group, none of the governance mechanism variables were
significantly related to learning outcomes. The sample size for uncooperative risk configuration
(i.e., high opportunism – low project uncertainty) is 56. The results show that the salient
governance mechanism for this risk profile is information exchange. In addition, relationship
2

length (a control variable) is statistically significantly related to the learning outcomes. The R is
0.281 indicating that the fit is good.
The sample size for routine configuration (low opportunism – low uncertainty) is 61. The
results indicate that the salient governance mechanism for outsourcing engagements with routine
2

risk profile is transaction specific investments. The R is 0.097 and the F statistic is 6.367.
Finally, the sample size for high-risk configuration (high opportunism – high project uncertainty)
2

is 53. The R for this risk configuration is 0.319, indicating a good fit. The results indicate that
transaction-specific investments and shared understanding are significantly related to learning

83

outcomes. In addition, cultural distance between the buyer and supplier is statistically
significantly and negatively related to learning outcomes.
2

Similar analysis was conducted for the entire sample. The R for the total sample is
0.203, indicating a good fit. The results indicate that transaction-specific investments,
information exchange and shared understanding are positively related to learning outcomes. In
addition, relationship length, a control variable, is positively related to learning outcomes as
well.

84

Risk group
(Group Number)

N

R

2

F

Unstable (1)

48

-

-

Uncooperative (2)

56

0.281

10.632

Routine (3)

61

0.097

6.367

High-risk (4)

Total Sample

53

218

0.319

0.203

Significant Independent
Variables

Standardized
Coefficient

t

-

-

Coefficient

-

-

0.367
0.466

33.452
3.087
3.911

***

Constant
Relationship length
Information exchange

12.956
1.236
1.543

*

Constant
Transaction-specific
investments

13.742
0.868

0.312

38.814
2.523

7.644

Constant
Cultural distance
Shared understanding
Transaction-specific
investments

12.959
-1.158
1.084
0.615

-0.313
0.394
0.240

32.789
-2.614
3.319
2.029

13.327

Constant
Relationship length
Information exchange
Shared understanding
Transaction-specific
investments

12.809
0.474
0.616
0.611
0.558

0.163
0.211
0.210
0.192

72.066
2.645
2.874
2.862
3.098

***

***

† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001
Table 10 – Governance variables significantly related to learning outcomes

85

Similar to the profile deviation analysis for outsourcing performance, misalignment
measure was calculated for all risk profiles using equation (4). In total, 131 cases were included
in the pooled sample from the sub-samples for uncooperative, routine and high-risk profiles. The
calibration sample for uncooperative risk profile included 7 cases with highest learning
outcomes. After removing the lowest performing 6 cases, the resulting sub sample of 43 cases
was used to calculate the misalignment score. The calibration sample for routine risk profile
included 8 cases with highest performance. After removing the lowest performing 6 cases, the
resulting sub sample of 47 cases was used to calculate misalignment score for routine
configuration. The calibration sample for high-risk profile included 5 cases with highest
performance. After removing the lowest performing 7 cases, the resulting sub sample of 41 cases
was used to calculate the misalignment score for high-risk configuration.

n

MISALIGN2 =



  b j X sj  X cj 2 



(4)

j 1
Where,

bj = standardized weights of significant governance variables from equation (3)

X cj = average score of the calibration sample (i.e. high performers) for each governance
variable

X sj = score for each of the study sample for each governance variable
The pooled sample was used to regress learning outcomes on the misalignment measure.
Control variables were included in this regression as well. The regression analysis was conducted
by introducing the control variables, main effects variables and the interaction term in a
hierarchical manner. The results of the regression models are shown in Table 11.

86

The patterns emerging from the results of the analysis are as follows. The misalignment
measure is negatively related to learning outcomes but it is significant only at p < 0.1. This
shows support for hypothesis H3 that the deviation from an ideal profile of governance
mechanisms will result in deterioration in the learning outcomes gained from the outsourcing
engagement. It has to be observed that this relationship is not strong as in the case of the
relationship between misalignment and outsourcing performance. Strategic importance of the
outsourcing engagement is not related to the learning outcomes gained from the outsourcing
engagement (i.e., main effects of strategic importance on learning outcomes is not statistically
significant). The interaction term is not statistically significantly related to learning outcomes.
The results indicate that the context of outsourcing engagement (i.e., core vs. peripheral) is not a
factor in determining the fit between governance mechanism and learning outcomes gained from
the outsourcing engagement. Thus, hypothesis H4 is not supported.

87

Variables

Model 1

Model 2

Model 3

Criticality

-

-0.055

-0.050

Outsourcing experience

-

0.058

0.070

Relationship length

-

0.127

0.114

Firm size

-

-0.032

-0.029

Cultural distance

-

0.067

0.066

†

Misalignment

-0.159

Strategic Importance
Misalignment * Strategic
Importance

0.069

2

-0.164

†

0.068
0.123

-

0.055

0.070

1.030

1.153

ΔR

0.055

0.015

Change in F

1.030

1.961

d.f.

(7, 123)

(1, 122)

p-value (change)

0.414

0.164

131

131

R

F
2

Size of the group

131

† p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001
Table 11 – Relationship between misalignment measure and learning outcomes

88

5.2

Fit as gestalts
Venkatraman (1989) argues that there are six forms of fit – moderation, mediation,

matching, covariation, profile deviation and gestalts. Fit as gestalts represents the “degree of
internal coherence among a set of theoretical attributes” (Venkatraman, 1989, p. 432). Using this
methodology, governance mechanisms that are used in outsourcing engagements are identified.
Furthermore, these sets of governance mechanisms are examined for their effectiveness in
managing outsourcing engagements. It is hypothesized that there is a specific governance
configuration that corresponds to the relationship configuration (i.e., configuration based on the
level of supplier ex post opportunistic behavior and the strategic importance of the outsourcing
engagement). Strategic importance of an outsourcing engagement was assessed based on a one
item question that asked the respondents to classify their outsourcing engagement based on its
direct impact on revenue. The outsourcing engagements were classified as “core” when the
activities outsourced had a direct impact on the revenue of the firm. The frequencies of
standardized score for supplier ex post opportunistic behavior were used to split the samples in
quartiles. The top quartile (25%) with high supplier ex post opportunism was classified as
experiencing high levels of relational risk. Similarly, the bottom quartile (25%) was classified
into low relational risk category. The relationship configurations and corresponding mean and
standard deviation of supplier ex post opportunism is shown in Table 12 below.

89

Configuration

Dimensions
Strategic
ex post
Importance
Opportunism

Frequency
(n = 218)

Percentage
(100%)

Strategic partnership

Core

Low (6.38, 0.49)

32

14.68%

Adversarial relationship

Core

High (18.17, 3.06)

24

11.01%

Arm's length relationship

Peripheral

High (18.35, 3.03)

28

12.84%

Selective partnership

Peripheral

Low (6.31, 0.47)

26

11.93%

Other configurations

-

- (11.27, 2.12)

108

49.5%

Table 12 – Frequencies, percentages of relationship configurations
Cluster analysis (K-means clustering) was performed to group the sample into
governance configurations based on the governance mechanisms used in the outsourcing
engagement. Literature has suggested many methods of standardizing the parameters before
clustering (G. W. Milligan & Cooper, 1988). In accordance with prior literature, standardized
scores for each construct were used before the data was subjected to cluster analysis (J. Miller &
Roth, 1994).
Assessing the number of clusters is a “thorny issue” (Laseter & Ramdas, 2002, p. 113; J.
Miller & Roth, 1994, p. 290). Three criteria were used to arrive at the number of clusters. First,
the number of clusters was limited to between n/60 and n/30, where n is the sample size
(Lehman, 1979). Based on these criteria, the appropriate number of clusters should be between 4
clusters and 7 clusters. Second, managerial interpretability of the clusters was sought using
ANOVA and Scheffe pairwise comparison tests of mean differences (Harrigan, 1985) was used
to arrive at the number of clusters. Finally, pseudo-F statistic was used to assess the “best fitting”
cluster solution. G. Milligan and Cooper (1985) assessed multiple indices and found that
Calinski – Harabasz index (pseudo-F statistic) is the most reliable.

90

Four solutions were evaluated with number of clusters between 4 and 7. The pseudo-F
statistic for the four cluster solutions (pseudo-F = 52.32) was higher than the other cluster
solutions (5 cluster solution = 48.73; 6 cluster solution = 47.31 and 7 cluster solution = 44.52).
Four cluster solution best satisfied the criteria. The resulting clusters were examined based on the
mean values of each governance mechanisms. The clusters, cluster mean, sample mean and
sample median values are provided for each governance mechanisms in the Table 13.
Governance
*
Mechanism
Shared
Understanding
Information
exchange
Transaction-specific
investments
Contract flexibility

Sample
Median

Sample
Cluster 1
Average

Cluster 2

Cluster 3

Cluster 4

11.000

10.505

9.372

11.481

12.028

7.743

15.000

15.092

14.187

15.727

16.881

11.886

8.000

7.309

7.889

6.233

8.198

6.038

10.000

10.123

10.239

9.465

11.311

8.375

Monitoring

16.000

15.865

16.629

14.255

17.878

12.686

Cluster size

218

218

58
Contractdominant

51
Relationdominant

Cluster type

74
35
Hybrid
Minimal
Governance Governance

* Non-standardized summated values are shown in this table

Table 13 – Results of cluster analysis on governance mechanisms
Some interesting patterns emerge as part of the cluster analysis. First, the clusters
obtained correspond to the theoretical arguments presented for governance configurations.
Cluster 1 (N = 58) corresponds to contract-dominant cluster. Outsourcing engagements in this
cluster predominantly use transactional governance to manage the relationship. Cluster 2 (N =
51) corresponds to relation-dominant governance configuration. Consequently, the outsourcing
engagements predominantly use relational governance mechanism to manage the relationship.
Cluster 3 (N = 74) corresponds to hybrid governance where the outsourcing engagements use
91

both transactional and relational governance mechanisms with high intensities. Finally, cluster 4
(N = 35) corresponds to minimal (fee-for-service) governance. The intensity of usage of both
transactional and relational governance is low for this governance configuration.
The Scheffe pair-wise comparison tests revealed the following results about the
governance configurations. The results show that the outsourcing engagements in cluster 3
(hybrid governance) exhibit highest intensities of governance with respect to all governance
mechanism with two exceptions. First, the intensity of shared understanding implemented by the
outsourcing engagements in hybrid governance is higher than relation-dominant cluster but it is
not statistically significant. Second, the intensity of transaction-specific investments
implemented by the outsourcing engagements in hybrid governance cluster are higher than
contract-dominant cluster but it is not statistically significant. The outsourcing engagements in
the contract-dominant governance cluster exhibit lower intensities of relational governance
mechanisms in comparison to relation-dominant and hybrid governance clusters but higher than
minimal governance cluster. The intensities of transactional mechanisms are higher than all other
clusters and they are statistically significantly different from all clusters except hybrid
governance cluster. The observations in the relation-dominant cluster exhibit higher intensities
of relational governance mechanisms in comparison to all other clusters. For this cluster, the
intensities of transactional mechanisms are higher than minimal governance cluster but lower
than both contract-dominant and hybrid governance clusters. Finally, the outsourcing
engagements in the minimal governance cluster use lower intensities of both transactional and
relational governance mechanisms than all other clusters. Collectively, these observations show
support for the existence of hypothesized governance clusters based on transactional and
relational governance mechanisms.
92

5.2.1

Relating the gestalts to Outsourcing performance
To assess the predictive validity, a dichotomous variable was constructed as fit variable

based on gestalts and non-gestalts for each relationship configuration. The observations
corresponding to the gestalt were classified as “Match” (i.e., a match between the relationship
configuration and governance configuration) and the value for the dichotomous variable was set
to one. The other governance configurations were classified as “Mismatch” (i.e., the value for the
dichotomous variable was set to zero). The outsourcing performance corresponding to the
gestalts and non-gestalts were compared using a t-test for statistical significance. The results of
comparison of outsourcing performance for each relationship configuration are discussed below.
5.2.1.1 Strategic partnership
The governance configuration corresponding to strategic partnership is shown in Table
14. The data provide support to the argument that outsourcing engagements with lower supplier
ex post opportunism and high strategic importance tend to choose hybrid governance to manage
the outsourcing relationship. In this relationship configuration, most number of outsourcing
engagements chose to use a hybrid form of governance. When the outsourcing performance of
the gestalts and non-gestalts are compared, the results show that hybrid governance outperforms
other governance configurations but the results are not statistically significant at p < 0.05. Thus,
hypothesis H5 is not supported.

93

Governance configuration

Outsourcing
performance
Average
Std. Dev.

Frequency
(N = 32)

Match

Hybrid governance

19

17.26

1.41

Yes

Relation-dominant governance

8

17.34

2.43

No

Contract-dominant governance

2

16.37

2.31

No

Minimal governance

3

15.91

1.14

No

Table 14 – Governance configuration for Strategic partnership

5.2.1.2 Adversarial relationship
The governance configuration corresponding to adversarial relationship is shown in
Table 15. The data provide support to the argument that outsourcing engagements with higher
supplier ex post opportunism and high strategic importance chose contract-dominant governance
to manage the outsourcing relationship. In this relationship configuration, most number of
outsourcing engagements chose to use contract-dominant governance. The results also show that
many outsourcing engagements chose to use minimal governance strategy. When the outsourcing
performance of the gestalts and non-gestalts are compared, the results show that contractdominant governance outperforms other governance configurations and the results are
statistically significant at p < 0.05. Thus, hypothesis H6 is supported.

94

Governance configuration

Outsourcing
performance
Average
Std. Dev.

Frequency
(N = 24)

Match

Hybrid governance

4

12.43

1.60

No

Relation-dominant governance

3

13.57

1.41

No

Contract-dominant governance

9

13.75

1.17

Yes

Minimal governance

8

11.5

2.39

No

Table 15 – Governance configuration for Adversarial relationship

5.2.1.3 Arm’s length relationship
The governance configuration corresponding to arm’s length relationship is shown in
Table 16. In this relationship configuration, most number of outsourcing engagements chose to
use either minimal or contract-dominant governance, providing support to the theoretical
arguments made earlier. When the outsourcing performance of the gestalts and non-gestalts are
compared, the results show that relation-dominant governance outperforms other governance
configurations and the results are statistically significant at p < 0.05. This result is counter to the
hypothesized relationship. Thus, the support for hypothesis H7 is reversed. The implications of
these findings will be discussed in the subsequent discussion section.

95

Governance configuration

Outsourcing
performance
Average
Std. Dev.

Frequency
(N = 28)

Match

Hybrid governance

0

-

-

-

Relation-dominant governance

3

14.91

3

No

Contract-dominant governance

12

12.29

2.59

Yes

Minimal governance

13

11.31

2.29

Yes

Table 16 – Governance configuration for Arm’s length relationship

5.2.1.4 Selective partnership
The governance configuration corresponding to selective partnership is shown in Table
17. In this relationship configuration, most number of outsourcing engagements chose to use
hybrid governance which is counter to the theoretical arguments made earlier. In addition, many
outsourcing engagements chose relation-dominant governance as well. The outsourcing
performance for relation-dominant configuration was not statistically significantly different than
other configurations at p < 0.05. Thus, hypothesis H8 was not supported.
Governance configuration

Frequency

Outsourcing performance

Match

(N = 26)

Average

Std. Dev.

Hybrid governance

13

17.21

1.33

No

Relation-dominant governance

12

16

2.45

Yes

Contract-dominant governance

0

-

-

No

Minimal governance

1

14

-

No

Table 17 – Governance configuration for Arm’s length relationship

96

The overall results of the data analysis for gestalts are shown in Table 18.
Relationship configuration Gestalt Non-Gestalt t-value

Support

Strategic partnership

17.263

16.861

0.646

Not supported

Adversarial relationship

13.748

12.164

2.070

Supported

Arm's length relationship

11.778

14.91

-1.738

Reversed

Selective Partnership

16

16.98

-1.198 Not supported

Table 18 – Gestalts data analysis results

97

5.3

Summary of analysis and results
This chapter presented the data analysis process to find support for the research

framework presented in this dissertation. Results of the data analysis were presented that found
general support for the research framework. The summary of the hypotheses and their support is
presented in Table 20 below. The results make both theoretical and managerial knowledge
contributions. These contributions are discussed in detail in the next chapter.

Hypothesis

Supported?

H1 Fit as profile deviation → Outsourcing performance

Yes

H2 Fit as profile deviation → Learning outcomes

Yes

H3 Fit as profile deviation * Strategic importance → Outsourcing performance

No

H4 Fit as profile deviation * Strategic importance → Learning outcomes

No

H5 Strategic partnership - Hybrid governance Gestalt → Outsourcing
performance
H6 Adversarial relationship - Contract-dominant Gestalt → Outsourcing
performance
H7 Arm's length relationship - Minimal governance Gestalt → Outsourcing
performance
H8 Selective partnership - Relation-dominant Gestalt → Outsourcing
performance

No

Table 19 – Summary of hypotheses and their support

98

Yes
Reversed
No

CHAPTER 6: DISCUSSION
In this chapter, both the theoretical and the managerial insights gained from this research
are discussed. The first section will discuss the contributions to buyer-supplier relationship
literature. Following this, the managerial implications are discussed.

6.1

Knowledge of buyer-supplier relationships
Firms are outsourcing tasks to not only reduce cost but also gain capabilities (Gottfredson

et al., 2005; Holcomb & Hitt, 2007). Researchers have argued that it is important to effectively
manage outsourcing relationships to gain benefits. Increasingly, researchers have called for the
use of both transactional as well as relational governance mechanisms (Handley & Benton Jr,
2009; Li et al., 2010; Liu et al., 2009; Tangpong et al., 2010). There is consensus among
researchers that firms should use both transactional and relational governance mechanisms. This
research expands our understanding by examining the effectiveness of transactional and
relational governance mechanisms by taking into account two forms of risks – supplier ex post
opportunism and project uncertainty. In addition, this dissertation also examines the
effectiveness of governance mechanisms for different relationship configurations. Finally, this
dissertation answers the question of whether transactional and relational governance mechanisms
are complements or substitutes.
6.1.1

Fit as profile deviation
In this dissertation, the key question that is addressed is that the effective configuration of

governance mechanisms is dependent on the risks faced by the outsourcing engagement.
Whereas numerous studies have argued for the importance of different governance mechanisms,
99

few have examined the fit between the risks faced by an outsourcing engagement and the
corresponding governance mechanisms that result in superior performance.
In this dissertation, two main sources of risk were examined - relational and project (Jap
& Anderson, 2003; Nidumolu, 1995). Theoretical arguments were presented for classification of
outsourcing engagements into four risk profiles and subsequently empirical support was shown
for the theoretical arguments. These risk profiles are classified as routine, unstable,
uncooperative and high-risk configurations. In addition, in this dissertation it is argued that the
effective configuration of governance mechanisms will be different for different risk profiles.
The results of the analysis using fit as profile deviation is presented. First, the results of the
analysis for each risk configuration are presented. Following this, the overall results of the test of
relationship between the misalignment measure and performance is discussed. The discussion is
presented for both outsourcing performance and learning outcomes.
6.1.1.1 Results for outsourcing performance
The results of the analysis with outsourcing performance as the dependent variable are
discussed in this subsection.
First let us consider routine outsourcing engagements. The results of the analysis showed
that only information exchange is statistically significantly related to outsourcing performance.
Outsourcing engagements with routine risk configuration are characterized by low project
uncertainty and low supplier ex post opportunism. With a cooperative supplier, the relationship
quality is high and the level of monitoring required to manage the outsourcing engagement is
low. Furthermore, there is little need for renegotiating the contract because the requirements of
the project are relatively stable. In a routine outsourcing engagement, few changes need to be

100

addressed. Through information exchange, buyer and supplier can exchange information and
resolve any changes that may be required during the course of the outsourcing engagement.
Interestingly, one of the control variables, cultural distance, is statistically significantly related to
outsourcing performance. The results corroborate the results from literature that argue that when
cultural distance is high, buyer and the supplier can experience inefficiencies in information
exchange resulting in lower outsourcing performance.
Now let us consider outsourcing engagements with uncooperative supplier. The
requirements of the outsourcing engagement are stable but the relationship quality is affected by
the supplier ex post opportunistic behavior. Few studies have specifically considered supplier ex
post opportunistic behavior when examining buyer-supplier relationship. Jap and Anderson
(2003) examined buyer supplier relationships in the presence of supplier ex post opportunism
and found that at high levels of opportunism, goal congruence acts as a safeguard whereas
interpersonal trust becomes less effective. This study adds to their findings and shows that
information exchange can facilitate superior outsourcing performance. Liu et al. (2009) found
that relational norms have a positive influence on buyer-supplier relationship performance. This
dissertation generally corroborates their finding and adds further clarity by showing that
information exchange between buyer and supplier can result in better outsourcing performance
when the supplier is uncooperative.
For the unstable risk configuration, shared understanding is the key driver of outsourcing
performance. Project uncertainty is related more to the clarity of requirements that is internal to
the buyer. In addition, there is lack of clarity of tasks that needed to be executed that results in
lower outsourcing performance. Through shared understanding, the buyer and supplier can
quickly resolve any disagreements that arise out of project uncertainty. Furthermore, the buyer
101

and supplier can quickly agree on the new developments and information that are discovered as
part of project execution.
Finally, consider the high-risk configuration. In this risk configuration, outsourcing
engagements experience high levels of both project risk and relational risk. Agency theory
(Eisenhardt, 1989) has argued that the supplier has a higher propensity to exhibit opportunism
(moral hazard) in the presence of uncertainty. Interestingly, the results of the analysis showed
that the fit of the model is strongest for this risk configuration. The statistically significant
governance mechanisms include shared understanding and information exchange. Interestingly,
contract flexibility is negatively related to outsourcing performance. The results corroborate
some findings in literature but also contradict others. Prior studies have argued for the
importance of relational governance mechanisms. Specifically, studies have argued that firms
develop relational competence through information exchange and shared understanding (Paulraj
et al., 2008). Furthermore, researchers have argued that relational governance mechanisms can
positively influence performance (Li et al., 2010; Liu et al., 2009; Nyaga et al., 2010). The
results of the analysis corroborate their findings to suggest that relational mechanisms such as
information exchange and shared understanding are critical to outsourcing performance,
especially when the outsourcing engagement faces high levels of relational and project risk. High
performing outsourcing engagements seem to invest the time and effort to overcome both forms
of risk. Finally, contrary to prior literature (e.g., N. S. Argyres et al., 2007; Mayer & Argyres,
2004) the results of the analysis show that contract flexibility is negatively related to outsourcing
performance. A possible explanation for this finding is that contract flexibility may provide little
help when the buyer lacks clarity of requirements. When the buyer is unclear of the requirements
or lacks consensus, it is difficult to enforce a clear rubric to evaluate the supplier. Lack of clarity
102

combined with supplier opportunistic behavior can create further confusion resulting in
deterioration of outsourcing performance.
The relationship between the misalignment measure and outsourcing performance is
statistically significant and negative. This result corroborates the arguments made in this research
that a deviation from the ideal profile will negatively influence the outsourcing performance. The
interaction between the misalignment measure and strategic importance of the outsourcing
engagement is not statistically significant. The results indicate that the deviation from ideal
profile can deteriorate performance but it is not different for strategic and non-strategic (i.e., core
vs. peripheral) outsourcing engagements. One possible explanation is that outsourcing
performance is primarily related to the project management attributes (i.e., cost, time and scope
performance). These metrics are not different for strategic or non-strategic engagements.
6.1.1.2 Results for learning outcomes
In this section, the results of the analysis with learning outcomes as dependent variables
are discussed. As in the previous section, the results for each risk configuration are discussed
before the results for the misalignment measure are discussed.
In a routine outsourcing engagement, both project and relational risk are low. That is, the
supplier is cooperative and the outsourcing engagement has clarity of goals that need to be
achieved. The team members have knowledge of the actions that need to be taken and the
priorities of the tasks are known. Interestingly, the only governance mechanism that is
statistically significantly related to learning outcomes is transaction-specific investments. Studies
have shown that transaction-specific investments result in lowered opportunistic behavior from
the supplier (e.g., Jap & Anderson, 2003; Nyaga et al., 2010). In this dissertation, transaction-

103

specific investments were operationalized as intangible investments such as upfront time spent in
understanding the problem. Through the process of investing time upfront, the buyer can learn
new knowledge that may be applied to other projects.
Project uncertainty is high in outsourcing engagements characterized by unstable risk
configuration. The supplier is cooperative and may work with the buyer to ensure that the
requirements are clear and the priorities of the tasks are set appropriately. Interestingly, in this
risk profile, none of the governance mechanisms increased the learning outcomes of the buyer.
Outsourcing engagements with uncooperative risk configuration predominantly
encounter relational risk due to supplier ex post opportunism. The supplier’s actions are not
always toward the best interests of the buyer. Hence, the buyer may not be able to capitalize on
the relationship to gain new capabilities. Interestingly, the results show that information
exchange is statistically significantly related to learning outcomes gained from the outsourcing
engagement with uncooperative risk configuration. Information exchange is positively related to
learning outcomes. Through continued information exchange with the supplier, the buyer is able
to acquire knowledge that can be used to improve the overall capabilities.
Finally, consider the outsourcing engagements in the high-risk configuration. The
outsourcing engagements with this risk profile experience high levels of both project and
relational risk. The results show that shared understanding and transaction-specific investments
are related to the learning outcomes. In addition, cultural distance is also negatively related to
learning outcomes. One possible explanation for the significance of transaction-specific
investments to be related to outsourcing performance is the level of effort that the buyer expends
in ensuring that the outsourcing engagement is executed well. In the long-run, these efforts pay

104

off and the buyer may experience an improvement in the overall capabilities. In addition, through
shared understanding the buyer is able to gain additional knowledge from the outsourcing
engagement.
The relationship between misalignment measure and learning outcomes is negative and
statistically significant. The results indicate that when firms deviate from the ideal profile, they
experience deterioration in the learning outcomes gained from the outsourcing engagement. The
interaction term between learning outcomes and strategic importance is not statistically
significantly related to learning outcomes. The strategic importance of outsourcing engagement
does not have a bearing on the learning outcomes gained from the outsourcing engagements.
6.1.2

Fit as gestalts
Previous section presented the results of the analysis using fit as profile deviation. In this

section, the results of the analysis using fit as gestalts are discussed. In this dissertation, it was
argued that fit as profile deviation has some limitations. Mainly, the ideal profile may not be
readily applicable to any one outsourcing engagement. Instead, it is a “theoretical construct” that
provides an understanding of the governance mechanisms associated with better performance for
different risk profiles. In contrast, fit as gestalts are developed based on theoretical arguments but
are corroborated using the actual set of governance mechanisms used in managing the
outsourcing engagements. This discussion will present the findings from the analysis using fit as
gestalts.
First consider the strategic partnership between the buyer and the supplier. Activities
outsourced by buyers in this relationship configuration are strategic in nature. In addition, the
relationship quality of the engagement is high because supplier ex post opportunism is low. The
105

results show that majority of the outsourcing engagements used hybrid governance mechanisms.
That is, the outsourcing engagements deployed both transactional and relational governance
mechanisms with high intensity. Interestingly, the comparison of outsourcing performance did
not yield any significant differences in performance. One explanation is that about 25% of the
firms (N=8) in this relationship configuration relied on relational governance. Post-hoc analysis
was conducted by pooling outsourcing engagements with hybrid and relation-dominant
governance. The results indicate that the outsourcing performance of the engagements with these
governance configurations outperformed the outsourcing engagements that relied on contractdominant or minimal governance (p < 0.10). This finding lends support to the arguments made in
the literature for the importance of relational governance mechanisms (Handley & Benton Jr,
2009; Li et al., 2010; Liu et al., 2009). Furthermore, the results indicate that the argument for
transactional and relational governance mechanisms being complements is not supported.
In an adversarial relationship configuration, the buyer has outsourced activities that are
strategic in nature but the supplier is not cooperative (i.e., exhibits high level of ex post
opportunistic behavior). The data lends support to the theoretical argument that contractdominant governance is best suited to manage outsourcing engagements in this relationship
configuration (p < 0.05). Interestingly, the results show that many firms utilized minimal
governance in managing their outsourcing engagements. One possible explanation is that buyers
may disengage from the supplier when the supplier behaves in an opportunistic manner. The
results lend support to the argument that contract-dominant governance is best suited to manage
adversarial relationships. These results corroborate the argument by (Jap & Ganesan, 2000) that
transactional governance mechanisms are better suited to curb supplier opportunism.

106

Now consider the arm’s length relationship configuration. Outsourcing engagements in
this configuration have outsourced activities that are peripheral in nature. In addition, the
supplier is exhibiting ex post opportunistic behavior. The results support the theoretical argument
that the firms will use either minimal governance or contract-dominant governance
configurations. Interestingly, the results showed that outsourcing engagements that used relationdominant governance experienced superior outsourcing performance in comparison to
outsourcing engagements using either contract-dominant or minimal governance. Whereas this
result is counter to the expected results, it provides support to the importance of relational
governance mechanisms when managing an outsourcing engagement.
Finally, consider the selective partnership configuration. The outsourcing engagements in
this configuration have outsourced peripheral activities. In addition, the suppliers are cooperative
and do not exhibit high levels of ex post opportunistic behavior. The results lend support that
many of the outsourcing engagements utilized relation-dominant governance. Contrary to
expectations, the results also indicate that a vast majority of the outsourcing engagements
employed hybrid governance. Interestingly, the results did not provide support to the argument
that relation-dominant governance is best suited to manage the outsourcing engagements when
pursuing selective partnership. Instead, the results indicate that the performance of the firms
employing hybrid and relation-dominant governance are equivalent and not statistically
significantly different. These results again demonstrate the importance of relational governance
mechanisms but do not support the argument that relational and transactional governance
mechanisms are complementary.

107

6.1.3

Theoretical implications
Prior subsections discussed the results of the analysis from this dissertation. In this

subsection, the overall implications to buyer-supplier relationship literature are presented.
In this study, multiple theoretical perspectives were used to examine the use of
appropriate governance mechanisms in the presence of opportunism and project uncertainty.
Specifically, Transaction cost economics (Coase, 1937; Williamson, 1979, 1981; Williamson,
1985), Agency theory (Eisenhardt, 1989) and relational norms (Heide & John, 1992; MacNeil,
1980) were used to identify governance mechanisms and theoretical arguments were used to
identify configurations of governance mechanisms that best mitigate the influence of risk in
outsourcing engagements. Many researchers have argued for the importance of using multiple
theories in examining research questions. Through this approach a richer understanding of the
phenomenon is achieved. For example, (McIvor, 2009) used transaction cost economics and
resource-based view to argue that the theoretical lenses converged under certain conditions but
diverged in other conditions. Increasingly, there have been calls to examine buyer-supplier
relationships using multiple theoretical perspectives, especially the use of transactional and
relational perspectives (Li et al., 2010; Liu et al., 2009). This research attempts to answer this
call for multi-theoretic view of examining outsourcing engagements by considering both
transactional and relational governance mechanisms in examining effective governance of
outsourcing engagements.
One key research question that was asked in this dissertation is if transactional and
relational governance mechanisms act as complements or substitutes. A big picture view
provides clarity and ability to answer this question. Transactional and relational governance

108

mechanisms can be considered substitutes if both are equally effective, under different risk
conditions. On the other hand, they can be considered complements if one set of mechanisms is
better suited under some conditions but other set is better suited under different conditions.
Based on the overall results from this research, it can be argued that transactional and relational
governance mechanisms act as complements to each other. Both transactional and relational
governance mechanisms generally seem to have a positive impact on outsourcing performance.
When the supplier is cooperative, as in strategic partnership and selective partnership
configurations, the use of relational governance mechanism resulted in superior outsourcing
performance. The use of transactional governance mechanisms in conjunction with relational
governance mechanisms (i.e., hybrid governance) does not seem to provide any additional
benefits. Conversely, when the supplier is uncooperative (i.e., opportunistic), the results seem to
diverge based on the strategic nature of the outsourcing engagements. When the outsourcing
engagements are strategic in nature (i.e., adversarial relationship configuration), the use of
transactional governance mechanism is beneficial over the use of relational governance
mechanism or hybrid governance. In contrast, when the outsourcing engagement is not strategic
in nature (i.e., arm’s length relationship configuration), the use of relational governance
mechanism seems to provide superior outsourcing performance. These results provide evidence
that transactional and relational governance mechanisms are complements to each other and the
governance mechanisms need to be appropriately deployed based on the strategic nature and the
opportunism encountered in the outsourcing engagement.
The insights gained from this research address a gap in the literature. Whereas
researchers have addressed the effectiveness of transactional governance and relational
governance in governing outsourcing engagement, very few studies have examined both sets of
109

governance in conjunction (Li et al., 2010; Liu et al., 2009). This research addresses this gap in
the literature and examines the effectiveness of both transactional and relational governance
mechanisms. Furthermore, this research answers the question whether transactional and
relational governance mechanisms are complements or substitutes.

110

6.2

Managerial insights
In this section, the managerial insights that are gained from this dissertation are

presented. Insights are drawn from both fit as profile deviation results and fit as gestalts results.
6.2.1

Fit as profile deviation
Using fit as profile deviation, the effective governance mechanisms that influence

outsourcing performance and learning outcomes were examined. Literature has highlighted
governance mechanisms that can be utilized to manage the buyer-supplier relationships (Handley
& Benton Jr, 2009; Li et al., 2010; Liu et al., 2009; Paulraj et al., 2008). Consistent with the
findings in the literature, this dissertation expected that transactional and relational governance
mechanisms will have a positive influence on outsourcing performance. The contribution of this
dissertation, however, is in examining the effectiveness of these governance mechanisms in the
presence of risk.
Two main risks were considered. First, primary risk in the form of “uncertainty of state”
(Sutcliffe & Zaheer, 1998) was conceptualized as project uncertainty. Literature has shown that
lack of clarity of requirements, inability to anticipate issues and the inability to prioritize tasks
have been shown to impede superior outsourcing performance. Second, very few researchers
have considered supplier opportunism in buyer-supplier relationships. Koopmans (1957)
characterized this risk as secondary uncertainty. In particular, very few studies have considered
ex post opportunism exhibited by the supplier. This risk is considered moral hazard where the
supplier exhibits behaviors such as shirking that impede the ability of the buyer to fully engage
with the supplier.

111

This research provides direction to managers in instituting governance mechanisms that
foster a virtuous-cycle of cooperation rather than vicious-cycle of negative behavior.
Furthermore, the research shows that reliance on transactional mechanisms alone will not result
in superior outsourcing performance. In particular, the buyer should institute governance
mechanisms that foster shared values. Regular information exchange with the supplier is
particularly important to improve outsourcing performance, especially when the supplier is
uncooperative. In addition, developing a shared understanding with the supplier where the
partners proactively resolve any misunderstanding has been shown to be consistently related to
outsourcing performance.
6.2.2

Fit as gestalts
The results of the analysis using fit as gestalts are discussed in this subsection. Typically,

managers face the challenge of identifying appropriate governance mechanisms that act as levers
providing superior outsourcing performance. In this dissertation, outsourcing engagements were
classified into four relationship configurations - strategic partnership, selective partnership,
arm’s length relationship and adversarial relationship. This provides a framework for the
managers to apply the findings from this dissertation.
The results indicate that relational governance mechanisms provide benefits irrespective
of the nature of the engagement except when the buyer is in an adversarial relationship. This
provides an important insight to the managers that they should start developing shared values
with their supplier. Engaging the supplier through information exchange and joint problem
solving through shared understanding provides benefits that can be translated to superior

112

outsourcing performance. Use of transactional mechanisms, such as monitoring and contingency
planning, provides marginal benefits and at times is counter-productive.

113

CHAPTER 7: CONCLUSION
In this section, the summary of this dissertation is presented. In this summary, the
background, the hypotheses, methodology, findings and implications for research are discussed.
The limitations and future research that can extend from this dissertation are discussed in the
subsections following the summary.

7.1

Summary of research
Following the increase in outsourcing activity to India and China, research on governing

outsourcing engagements has increased in prominence in recent years. Numerous researchers
have examined the ex ante decisions leading to outsourcing activities to suppliers as well as ex
post governance of outsourcing engagements (e.g., Aron et al., 2008; Balakrishnan et al., 2008;
Handley & Benton Jr, 2009; Li et al., 2010; Liu et al., 2009; Tangpong et al., 2010). In addition,
researchers have argued for the importance of risk in buyer-supplier relationships (Jap &
Anderson, 2003; Nidumolu, 1995). However, few studies have examined the impact of risk and
governance mechanisms in buyer-supplier relationships simultaneously. In particular, project
uncertainty and supplier ex post opportunism, deemed as primary and secondary sources of risk
(Koopmans, 1957), have not been examined in-depth. This dissertation proposed a research
framework that explicitly takes both forms of risk into account and examines the effectiveness of
governance mechanisms on the success of the outsourcing engagements.
Buyer-supplier relationship literature and marketing channels literature were used to
examine commonly used governance mechanisms by managers. These governance mechanisms
were broadly classified into transactional and relational governance mechanisms. Furthermore,
114

literature on strategic management and fit was utilized to argue that fit as gestalts and fit as
profile deviation should be used to examine the research questions in this dissertation.
To find support for the research framework, a web survey methodology was used to
collect data. Sampling frame consisted of members belonging to two organizations, Project
Management Institute (PMI) and International Association of Outsourcing Professionals (IAOP).
The members of these organizations were invited to participate in the survey and interested
members were sent a link to the survey. Data were collected over a six week period.
Methodology suggested by (Dillman, 1978; Dillman et al., 2008) was used to ensure that the
survey instrument was developed, validated and administered correctly. Additional data
validation procedures were used to ensure convergent validity, discriminant validity and
reliability. Multiple regression, cluster analysis, t-tests were used to test the models and draw
inferences.
The findings from the data analysis were original and provide new insights to buyersupplier relationships, in particular outsourcing engagements. The findings from fit as profile
deviation analysis suggest that there are different configurations of effective governance
mechanisms for different configurations of risks. The findings from fit as gestalts analysis
suggests that there are sets of governance mechanisms that occur in a congruent manner based on
the relationship configuration. The implication of these findings is that managers should deploy
different governance mechanisms based on the nature of the risks and the relationship
configuration corresponding to their outsourcing relationship. The implications to researchers are
two-fold. First, the findings from this dissertation demonstrate that both transactional and
relational governance mechanisms are effective under different conditions of risk and nature of
relationships. Second, a broad conclusion can be drawn that transactional and relational
115

governance mechanisms are not substitutes. Rather, relational governance mechanisms
complement transactional governance mechanisms and provide higher relational-rents from
outsourcing engagements.

7.2

Limitations of study
All research studies have limitations and this dissertation is no exception. First, this study

relies on single respondent for both antecedent and dependent variables. Research has shown that
this can potentially cause common method bias (CMB). Although precautions were taken by
placing the items corresponding to dependent variable far away from the items corresponding to
antecedent constructs in the survey, there is still a potential for CMB. Second, fit as profile
deviation is a methodology driven by data. There are potential limitations in generalizing the
findings from this analysis. There is a potential that the findings may change when the data used
to analyze the model is different. This limitation has to be taken in to consideration along with
the findings from this analysis. Finally, there were measurement issues with some constructs.
The indices for discriminant validity for contract flexibility and information exchange are lower
than threshold values suggested in literature (Fornell & Larcker, 1981). Adequate precautions
were taken to ensure that other less conservative thresholds were met. Notwithstanding these
limitations, the dissertation makes valuable contributions to the literature on buyer-supplier
relationships. Potential future research that can be pursued after this dissertation are discussed in
the next section.

116

7.3

Future research
Multiple avenues of research can be pursued that extend the findings from this research.

First, additional governance mechanisms that impinge on the success of an outsourcing
engagement should be explored. This research utilized transaction cost economics, agency theory
and relational norms perspective to examine governance of outsourcing engagements. Other
theoretical perspectives may bring forth governance mechanisms that were not considered in this
research. For example, social exchange theory (Emerson, 1962; Homans, 1958) and social
capital theory (Nahapiet & Ghoshal, 1998) have the potential to provide additional insights on
governance mechanisms in outsourcing engagements. Specifically, these theories can be utilized
to map the importance of different governance mechanisms over the lifecycle of the relationship.
Second, research can be pursued to examine the inter-linkage between complexity of an
outsourcing engagement and the opportunistic behavior exhibited by the supplier. Specifically,
researchers have argued that complexity dimensions such as geographical location and
interconnectedness can hinder the ability of the buyer to ensure compliance from suppliers,
especially suppliers that are further upstream and away from the customers. Governance
mechanisms that address not only the first-tier supplier but also suppliers further upstream can be
examined to generate additional insights that are valuable to both researcher and practitioner
communities.
Finally, opportunism has been considered as a monolithic construct. There have been
attempts made by some researchers to provide a nuanced understanding of this construct. For
example, Wathne and Heide (2000) attempt to create a distinction between suppliers that display
active opportunism and passive opportunism. They define active opportunism as overt breach of

117

contract that can result in legal dispute between partners. In contrast, passive opportunism is
defined as a breach of the “moral contract”. That is, the supplier has not breached any contract
terms but rather exhibited behavior that can be considered as withholding effort. Further research
on providing clarity on opportunism construct can result in better understanding the risk in
buyer-supplier relationships.

118

APPENDIX

119

APPENDIX A

Correlation matrix
Correlations
Notes
Output Created
Comments
Input

17-Feb-2012 10:10:56

C:\Users\Ravi\Dropbox\Research\Outsourci
ng\Analysis\Fit_as_Profile_Deviation\DISSERT_
DATA.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working
218
Data File
Missing Value Handling
Definition of Missing
User-defined missing values are treated as
missing.
Cases Used
Statistics for each pair of variables are based on
all the cases with valid data for that pair.
Syntax
CORRELATIONS
/VARIABLES=ZOPPORTUNISM
ZPROJECT_UNCERTAINTY ZFLEXIBILITY
ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING
ZINFO_EXCHANGE ZPERFORMANCE
ZLEARNING_OUTCOMES
/PRINT=TWOTAIL NOSIG
/MISSING=PAIRWISE.
Resources

Data

Processor Time
Elapsed Time

00:00:00.031
00:00:00.015

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\DISSERT_D
ATA.sav

120

Correlations
ZOPPORTUNISM

Pearson Correlation

ZOPPORTUNISM
1

Sig. (2-tailed)
218

**

.514
.000

Sig. (2-tailed)
N
Pearson Correlation

218

*

-.142
.036

Sig. (2-tailed)
ZMONITORING

N
Pearson Correlation

218

**

-.251
.000
218
-.034
.615
218

Sig. (2-tailed)
N
ZTS_INVESTMENTS
Pearson Correlation
Sig. (2-tailed)
N
ZSHARED_UNDERSTANDIN Pearson Correlation

**

-.530
.000
218

G

ZINFO_EXCHANGE

Sig. (2-tailed)
N
Pearson Correlation

ZPERFORMANCE

Sig. (2-tailed)
N
Pearson Correlation

ZLEARNING_OUTCOMES

**

.514

ZFLEXIBILITY

*

-.142

.000

N
ZPROJECT_UNCERTAINTY Pearson Correlation

ZFLEXIBILITY

ZPROJECT_UNC
ERTAINTY

Sig. (2-tailed)
N
Pearson Correlation

**

-.479
.000
218

**

-.657
.000
218

**

.036

218
1

218
-.131
.053

218
-.131
.053
218

**

-.338
.000
218
-.091
.180
218

**

-.413
.000
218

**

-.384
.000
218

**

.280
.000
218
.122
.071
218

**

.292
.000
218

**

**

**

.091
218
.127

-.483
.000
218
-.201

.002
218

121

218

.202
.003
218
.115

-.204

Sig. (2-tailed)
N
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

218
1

.003
218

.060
218

Correlations
ZTS_INVESTME
NTS
-.034

ZSHARED_UN
DERSTANDING

.000
218

**

.615
218
-.091

**

.180
218
.122

ZMONITORING
ZOPPORTUNISM

**

Pearson Correlation

-.251

Sig. (2-tailed)
N
ZPROJECT_UNCERTAINTY Pearson Correlation

ZFLEXIBILITY

-.338
.000
218

Sig. (2-tailed)
N
Pearson Correlation

ZMONITORING

.000
218

Sig. (2-tailed)
N
Pearson Correlation

.280
.000
218
1

Sig. (2-tailed)
ZTS_INVESTMENTS

N
Pearson Correlation

218

**

.365
.000

Sig. (2-tailed)
N
ZSHARED_UNDERSTANDIN
G

218

**

Pearson Correlation

.222
.001

Sig. (2-tailed)
ZINFO_EXCHANGE

N
Pearson Correlation

ZPERFORMANCE

Sig. (2-tailed)
N
Pearson Correlation

ZLEARNING_OUTCOMES

218

Sig. (2-tailed)
N
Pearson Correlation

**

.404
.000
218

**

.245
.000
218
.120

Sig. (2-tailed)
N
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

.076
218

122

.071
218

**

**

-.530

**

-.413
.000
218

**

.292
.000
218

**

.365
.000

.222
.001

218
1

218
.082
.228

218
.082

218
1

.228
218

*

.133
.049
218
.091
.179
218

**

.224

.001
218

218

**

.548
.000
218

**

.524
.000
218

**

.327

.000
218

ZOPPORTUNISM

Correlations
ZINFO_EXCHAN ZPERFORMANC
GE
E
**
**
Pearson Correlation
-.479
-.657

ZPROJECT_UNCERTAINTY

Sig. (2-tailed)
N
Pearson Correlation

ZFLEXIBILITY

Sig. (2-tailed)
N
Pearson Correlation

ZMONITORING

Sig. (2-tailed)
N
Pearson Correlation

ZTS_INVESTMENTS

Sig. (2-tailed)
N
Pearson Correlation

ZSHARED_UNDERSTANDIN
G

ZINFO_EXCHANGE

.000
218

**

-.384
.000
218

**

.202
.003
218

**

.404
.000
218

*

.133
.049
218

Sig. (2-tailed)
N
Pearson Correlation

**

.002
218

**

-.483
.000
218
.115

-.201
.003
218
.127

.091
218

.060
218
.120

**

.245
.000
218
.091
.179
218

**

.076
218

**

.224
.001
218

**

.524
.000
218
.541
.000

.342
.000

218

Sig. (2-tailed)
N
Pearson Correlation

N
Pearson Correlation

218
1

218

**

.541
.000

Sig. (2-tailed)
ZLEARNING_OUTCOMES

**

**

-.204

.548
.000
218
1

Sig. (2-tailed)
ZPERFORMANCE

.000
218

ZLEARNING_O
UTCOMES

N
Pearson Correlation

218

**

.342

Sig. (2-tailed)

**

218

**

.399

.000

N
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

123

218

**

**

.399
.000
218
1

.000

218

.327
.000
218

218

APPENDIX B

Fit as profile deviation – Analyses for outsourcing performance
APPENDIX B-1
Salient governance mechanisms analysis – Unstable risk profile

Regression
Notes
Output Created
Comments
Input

Missing Value Handling

16-Feb-2012 11:40:42
Data

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\
Fit_as_Profile_Deviation\FIRSTORDER\CONDITIONAL
_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL
_MEDIAN_PROFDEV_PERF.sav
DataSet1
CONDITIONAL_MEDIAN_RISK_GROUP = 1 (FILTER)
<none>
<none>
48

Active Dataset
Filter
Weight
Split File
N of Rows in Working
Data File
Definition of Missing
Cases Used

User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PERFORMANCE
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZFLEXIBILITY ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Syntax

Resources

Processor Time
Elapsed Time
Memory Required
Additional Memory
Required for Residual
Plots

00:00:00.032
00:00:00.026
6244 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\FIRSTORD
ER\CONDITIONAL_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL_MEDIAN_P
ROFDEV_PERF.sav
124

Variables Entered/Removed
Variables
Variables
Entered
Removed
1
ZSHARED_U
.
NDERSTANDI
NG
a. Dependent Variable: PERFORMANCE

a

Model

Method
Stepwise (Criteria: Probability-of-F-to-enter <=
.050, Probability-of-F-to-remove >= .100).

d
i
m
e
n
s
i
o
n
0

Model Summary
Model
R
1

d
i
m
e
n
s
i
o
n
0

a

.355

R Square
.126

Adjusted R
Square
.107

Std. Error of the
Estimate
1.964

Change Statistics
R Square
Change
F Change
.126
6.643

df1
1

a. Predictors: (Constant), ZSHARED_UNDERSTANDING

Model
d
i
m
e
n
s
i
o
n
0

1

Model Summary
Change Statistics
df2
Sig. F Change
46
.013

b

ANOVA
Sum of Squares
25.620

Df
1

Mean Square
25.620

Residual

177.403

46

3.857

Total

1

Model
Regression

203.023

F
6.643

Sig.

47

a

.013

a. Predictors: (Constant), ZSHARED_UNDERSTANDING
b. Dependent Variable: PERFORMANCE

a

Coefficients
Model

1

(Constant)

ZSHARED_UNDERSTANDI
NG
a. Dependent Variable: PERFORMANCE

Unstandardized Coefficients
B
Std. Error
15.254
.284
.882

.342

125

Standardized
Coefficients
Beta
.355

t
53.730

Sig.
.000

2.577

.013

a

Coefficients
Model
1

Collinearity Statistics
Tolerance
VIF

(Constant)

ZSHARED_UNDERSTANDING
a. Dependent Variable: PERFORMANCE

1.000

1.000

b

Excluded Variables
Model

a

T
.080

Sig.
.936

Partial
Correlation
.012

a

1.196

.238

.176

a

1.560

.126

.227

a

.307

.760

.046

a

-.459

.649

-.068

a

.693

.492

.103

a

.607

.547

.090

a

-.176

.861

-.026

a

.070

.945

.010

Beta In
1

ZCRITICALITY

.012

ZLN_OUTSOURCING_EXP

.165

ZLN_RELN_LENGTH

.225

ZLN_FIRM_SIZE

.043

ZCULTURAL_DISTANCE

-.064

ZFLEXIBILITY

.102

ZMONITORING

.084

ZTS_INVESTMENTS

-.025

ZINFO_EXCHANGE

.011

a. Predictors in the Model: (Constant), ZSHARED_UNDERSTANDING
b. Dependent Variable: PERFORMANCE

b

Excluded Variables
Model

1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZINFO_EXCHANGE

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.942
1.061
.942
.992
1.008
.992
.886
1.128
.886
.994
1.006
.994
.991
1.009
.991
.880
1.136
.880
.998
1.002
.998
.998
1.002
.998
.738
1.356
.738

b. Dependent Variable: PERFORMANCE

126

a

Collinearity Diagnostics
Model

Dimension

Eigenvalue
Condition Index
1
1.057
1.000
2
.943
1.058
a. Dependent Variable: PERFORMANCE
d
i
m
e
n
s
i
o
n
0

1

dimension1

Variance Proportions
ZSHARED_UN
DERSTANDIN
(Constant)
G
.47
.47
.53
.53

127

APPENDIX B-2
Salient governance mechanisms analysis – Uncooperative risk profile

Regression
Notes
Output Created
Comments
Input

16-Feb-2012 11:20:54
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analy
sis\Fit_as_Profile_Deviation\FIRSTORDER\CONDIT
IONAL_MEDIAN\PERFORMANCE\DiSSERT_CON
DITIONAL_MEDIAN_PROFDEV_PERF.sav
DataSet1
CONDITIONAL_MEDIAN_RISK_GROUP = 2 (FILTER)
<none>
<none>
56
User-defined missing values are treated as missing.
Statistics are based on cases with no missing
values for any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PERFORMANCE
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZFLEXIBILITY ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.015
00:00:00.014
6244 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\FIRSTORDE
R\CONDITIONAL_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL_MEDIAN_PRO
FDEV_PERF.sav

128

Variables Entered/Removed
Model
1

Variables Entered
ZINFO_EXCHANGE

Variables
Removed
.

a

Method
Stepwise (Criteria: Probability-of-F-toenter <= .050, Probability-of-F-toremove >= .100).

dim
ensi
on0

a. Dependent Variable: PERFORMANCE

Model Summary
Model
R
dim
ensi
on0

1

a

.422

Adjusted R
Square
.163

R Square
.178

Std. Error of the
Estimate
2.047

Change Statistics
R Square
Change
F Change
.178
11.720

a. Predictors: (Constant), ZINFO_EXCHANGE

Model
dim
ensi
on0

1

Model Summary
Change Statistics
df2
Sig. F Change
54
.001

b

ANOVA
Sum of Squares
49.086

Df
1

Mean Square
49.086

Residual

226.164

54

4.188

Total

1

Model
Regression

275.249

F
11.720

Sig.

55

a

.001

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Dependent Variable: PERFORMANCE

a

Coefficients
Model

1

(Constant)

Unstandardized Coefficients
B
Std. Error
13.899
.283

ZINFO_EXCHANGE
.980
a. Dependent Variable: PERFORMANCE

.286

129

Standardized
Coefficients
Beta
.422

T
49.131

Sig.
.000

3.423

.001

df1
1

a

Coefficients
Model
1

Collinearity Statistics
Tolerance
VIF

(Constant)

ZINFO_EXCHANGE
1.000
a. Dependent Variable: PERFORMANCE

1.000

b

Excluded Variables
Model

a

T
.091

Sig.
.928

Partial
Correlation
.013

a

.323

.748

.044

a

.646

.521

.088

a

.350

.728

.048

a

.044

.965

.006

a

-.211

.834

-.029

a

.399

.691

.055

a

.997

.324

.136

a
ZSHARED_UNDERSTANDIN
.963
.135
G
a. Predictors in the Model: (Constant), ZINFO_EXCHANGE
b. Dependent Variable: PERFORMANCE

.340

.131

Beta In
1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

.011
.040
.082
.044
.006

-.026
.054
.125

b

Excluded Variables
Model

1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDIN
G

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.995
1.005
.995
.992
1.008
.992
.957
1.045
.957
.998
1.002
.998
.976
1.024
.976
.993
1.007
.993
.857
1.167
.857
.960
1.042
.960
.772
1.295
.772

b. Dependent Variable: PERFORMANCE

130

a

Collinearity Diagnostics
Model

Dimension

Eigenvalue
Condition Index
1
1.256
1.000
2
.744
1.299
a. Dependent Variable: PERFORMANCE
dime
nsio
n0

1

dimension1

131

Variance Proportions
ZINFO_EXCHAN
(Constant)
GE
.37
.37
.63
.63

APPENDIX B-3
Salient governance mechanisms analysis – Routine risk profile

Regression
Notes
Output Created
Comments
Input

16-Feb-2012 11:25:40
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysi
s\Fit_as_Profile_Deviation\FIRSTORDER\CONDITIO
NAL_MEDIAN\PERFORMANCE\DiSSERT_CONDITI
ONAL_MEDIAN_PROFDEV_PERF.sav
DataSet1
CONDITIONAL_MEDIAN_RISK_GROUP = 3 (FILTER)
<none>
<none>
61
User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PERFORMANCE
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZFLEXIBILITY ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.031
00:00:00.027
6244 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\FIRSTORDE
R\CONDITIONAL_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL_MEDIAN_PRO
FDEV_PERF.sav

132

Variables Entered/Removed
Model
1

Variables Entered
ZINFO_EXCHANGE

Variables
Removed
.

2

ZCULTURAL_DISTANCE

.

dim
ensi
on0

a

Method
Stepwise (Criteria: Probability-of-F-to-enter
<= .050, Probability-of-F-to-remove >=
.100).
Stepwise (Criteria: Probability-of-F-toenter <= .050, Probability-of-F-to-remove
>= .100).

a. Dependent Variable: PERFORMANCE

Model Summary
Model

a

R Square
.107

Adjusted R
Square
.091

b

.166

.137

R
1
dim
ensi
on0

2

.326
.407

Std. Error of the
Estimate
1.550
1.511

Change Statistics
R Square
Change
F Change
.107
7.034
.059

4.113

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZCULTURAL_DISTANCE

Model
1
2

dim
ensi
on0

Model Summary
Change Statistics
df2
Sig. F Change
59
.010
58
.047

c

ANOVA
Sum of Squares
16.910

Df
1

Mean Square
16.910

Residual

1

Model
Regression

141.838

59

2.404

Total

158.748
26.303

2

13.151

132.446

58

2.284

Total

158.748

60

Sig.

a

.010

60

Regression
Residual

2

F
7.034

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZCULTURAL_DISTANCE
c. Dependent Variable: PERFORMANCE

133

5.759

b

.005

df1
1
1

a

Coefficients
Model

1

(Constant)

2

Unstandardized Coefficients
B
Std. Error
16.497
.239

ZINFO_EXCHANGE
(Constant)

Sig.
.000

.235
.233

.326

2.652
70.804

.010
.000

.597
-.364

.229
.179

.313
-.244

2.604
-2.028

.012
.047

a

Coefficients
Model

ZINFO_EXCHANGE
(Constant)

Collinearity Statistics
Tolerance
VIF

(Constant)

2

t
68.983

.623
16.516

ZINFO_EXCHANGE
ZCULTURAL_DISTANCE
a. Dependent Variable: PERFORMANCE

1

Standardized
Coefficients
Beta

ZINFO_EXCHANGE
ZCULTURAL_DISTANCE
a. Dependent Variable: PERFORMANCE

1.000

1.000

.997
.997

1.003
1.003

134

c

Excluded Variables
Model

a

T
1.216

Sig.
.229

Partial
Correlation
.158

a

-.416

.679

-.055

a

.286

.776

.037

a

-1.401

.167

-.181

a

-2.028

.047

-.257

a

.144

.886

.019

a

-.954

.344

-.124

a

-.819

.416

-.107

a

1.779

.081

.227

b

1.407

.165

.183

b

-.383

.703

-.051

b

.360

.720

.048

b

-1.235

.222

-.161

b

.066

.947

.009

b

-.663

.510

-.087

b

-.300

.765

-.040

Beta In
1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

2

ZSHARED_UNDERSTANDIN
G
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

.149

-.052
.035

-.173
-.244
.018

-.122
-.101
.230
.168

-.046
.044

-.150
.008

-.084
-.038

b
ZSHARED_UNDERSTANDIN
1.951
.056
.250
.245
G
a. Predictors in the Model: (Constant), ZINFO_EXCHANGE
b. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZCULTURAL_DISTANCE
c. Dependent Variable: PERFORMANCE

135

c

Excluded Variables
Model

1

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDIN
G
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDIN
G

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.999
1.001
.999
1.000
1.000
1.000
1.000
1.000
1.000
.973
1.028
.973
.997
1.003
.997
.976
1.024
.976
.935
1.070
.935
.992
1.008
.992
.871
1.148
.871
.994
.999
.999
.963
.975
.911
.918
.869

1.006
1.001
1.001
1.038
1.026
1.098
1.089
1.151

.992
.996
.996
.963
.974
.911
.918
.867

c. Dependent Variable: PERFORMANCE

a

Collinearity Diagnostics
Model

Dimension

Variance Proportions
ZINFO_EXCHAN ZCULTURAL_DI
GE
STANCE
.22

1

Eigenvalue
1.558

Condition Index
1.000

(Constant)
.22

2

1

.442

1.876

.78

.78

1.000
1.247
1.882

.22
.00
.78

.22
.00
.78

dimension1

dim
ensi
on0

2

1
1.558
2
1.001
3
.440
a. Dependent Variable: PERFORMANCE
dimension1

136

.00
.99
.01

APPENDIX B-4
Salient governance mechanisms analysis – High-Risk risk profile

Regression
Notes
Output Created
Comments

16-Feb-2012 13:55:42

Input

Data

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as
_Profile_Deviation\FIRSTORDER\CONDITIONAL_MEDIAN\P
ERFORMANCE\DiSSERT_CONDITIONAL_MEDIAN_PROF
DEV_PERF.sav
Active Dataset
DataSet1
Filter
CONDITIONAL_MEDIAN_RISK_GROUP = 4 (FILTER)
Weight
<none>
Split File
<none>
N of Rows in Working Data
53
File
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics are based on cases with no missing values for any
variable used.
Syntax
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PERFORMANCE
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE ZFLEXIBILITY
ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.
Resources

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.016
00:00:00.039
6244 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\FIRSTORDE
R\CONDITIONAL_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL_MEDIAN_PRO
FDEV_PERF.sav

137

Variables Entered/Removed
Model
1

Variables
Removed
.

2

ZSHARED_UNDERSTANDING

.

3

dim
ensi
on0

Variables Entered
ZINFO_EXCHANGE

ZFLEXIBILITY

.

a

Method
Stepwise (Criteria: Probability-of-F-to-enter <=
.050, Probability-of-F-to-remove >= .100).
Stepwise (Criteria: Probability-of-F-to-enter <=
.050, Probability-of-F-to-remove >= .100).
Stepwise (Criteria: Probability-of-F-to-enter <=
.050, Probability-of-F-to-remove >= .100).

a. Dependent Variable: PERFORMANCE

Model Summary
Model

a

R Square
.354

b

.403

.379

1.887

.050

4.160

1

c

.456

.423

1.819

.053

4.760

1

R
1
dim
ensi
on0

2
3

.595
.635

.675

Std. Error of the
Estimate
1.944

Change Statistics
R Square
Change
F Change
.354
27.893

Adjusted R
Square
.341

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING
c. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING, ZFLEXIBILITY

Model

dim
ensi
on0

1
2
3

Model Summary
Change Statistics
df2
Sig. F Change
51
.000
50
.047
49
.034

d

ANOVA
Df
1

Mean Square
105.437

192.782

51

3.780

Total

298.219

52

Regression

120.244

2

60.122

Residual

2

Sum of Squares
105.437

Residual

1

Model
Regression

177.975

50

3.559

Total

298.219
136.002

3

45.334

162.216

49

298.219

a

.000

b

.000

3.311

Total

16.891

Sig.

52

Regression
Residual

3

F
27.893

52

13.694

c

.000

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING
c. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING, ZFLEXIBILITY

138

df1
1

Model

dim
ensi
on0

1
2
3

Model Summary
Change Statistics
df2
Sig. F Change
51
.000
50
.047
49
.034

d

ANOVA
Df
1

Mean Square
105.437

192.782

51

3.780

Total

298.219

52

Regression

120.244

2

60.122

Residual

2

Sum of Squares
105.437

Residual

1

Model
Regression

177.975

50

3.559

Total

298.219
136.002

3

45.334

162.216

49

298.219

a

.000

b

.000

3.311

Total

16.891

Sig.

52

Regression
Residual

3

F
27.893

52

13.694

c

.000

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING
c. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING, ZFLEXIBILITY
d. Dependent Variable: PERFORMANCE

a

Coefficients
Model

Unstandardized Coefficients
B
Std. Error
13.517
.298

1

(Constant)

2

ZINFO_EXCHANGE
(Constant)

1.441
13.835

.273
.329

1.152
.649

.300
.318

3

ZINFO_EXCHANGE
ZSHARED_UNDERSTANDIN
G
(Constant)

13.925

.290
.340

-.571

.262

t
45.360

Sig.
.000

.595

5.281
42.106

.000
.000

.476
.253

3.838
2.040

.000
.047

43.577

.000

.473
.378

3.961
2.851

.000
.006

-.261

-2.182

.034

.320

1.147
.970

Standardized
Coefficients
Beta

ZINFO_EXCHANGE
ZSHARED_UNDERSTANDIN
G
ZFLEXIBILITY
a. Dependent Variable: PERFORMANCE

139

a

Coefficients
Model

Collinearity Statistics
Tolerance
VIF

1

(Constant)
ZINFO_EXCHANGE
(Constant)

1.000

1.000

2

.778
.778

1.286
1.286

3

ZINFO_EXCHANGE
ZSHARED_UNDERSTANDIN
G
(Constant)

.778
.632

1.286
1.583

.774

1.292

ZINFO_EXCHANGE
ZSHARED_UNDERSTANDIN
G
ZFLEXIBILITY
a. Dependent Variable: PERFORMANCE

140

d

Excluded Variables
Model

a

T
.634

Sig.
.529

Partial
Correlation
.089

a

-.809

.422

-.114

a

-1.304

.198

-.181

a

.447

.657

.063

a

-.361

.719

-.051

a

-.983

.331

-.138

a

-.127

.899

-.018

a

.877

.385

.123

a

2.040

.047

.277

b

.819

.417

.116

b

-.416

.679

-.059

b

-1.484

.144

-.207

b

.900

.373

.127

b

-.318

.752

-.045

b

-2.182

.034

-.298

b

-.444

.659

-.063

b

1.198

.237

.169

c

1.235

.223

.176

c

-.336

.738

-.048

c

-1.488

.143

-.210

c

.764

.449

.110

c

-.151

.880

-.022

c

-.249

.804

-.036

c

1.120

.268

.160

Beta In
1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

2

ZSHARED_UNDERSTANDIN
G
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

3

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMONITORING
ZTS_INVESTMENTS

.072

-.091

-.147
.051

-.041
-.113
-.016
.099
.253
.090

-.047

-.161
.102

-.035
-.261
-.053
.132

.132

-.037

-.156
.084

-.016
-.029
.120

a. Predictors in the Model: (Constant), ZINFO_EXCHANGE
b. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING
c. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING,
ZFLEXIBILITY
d. Dependent Variable: PERFORMANCE

141

d

Excluded Variables
Model

1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDIN
G
2
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
3
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMONITORING
ZTS_INVESTMENTS
d. Dependent Variable: PERFORMANCE

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
1.000
1.000
1.000
.998
1.002
.998
.991
1.009
.991
.982
1.018
.982
.988
1.012
.988
.953
1.050
.953
.856
1.168
.856
.992
1.008
.992
.778
1.286
.778
.993
.955
.987
.941
.988
.774
.836
.974
.966
.953
.986
.935
.981
.828
.971

1.007
1.048
1.013
1.063
1.013
1.292
1.196
1.027
1.035
1.050
1.014
1.069
1.020
1.208
1.030

.773
.744
.768
.745
.772
.632
.716
.762
.619
.605
.630
.617
.630
.626
.626

a

Collinearity Diagnostics
Model

Dimension

Eigenvalue
Condition Index
1
1.444
1.000
2
.556
1.611
2
1
2.107
1.000
2
.556
1.946
3
.336
2.503
3
1
2.360
1.000
2
.814
1.702
3
.550
2.072
4
.276
2.925
a. Dependent Variable: PERFORMANCE
1

dimension1

dimension1

dime
nsio
n0

dimension1

142

Variance Proportions
ZINFO_EXCHAN
(Constant)
GE
.28
.28
.72
.72
.09
.09
.55
.57
.35
.34
.06
.07
.17
.04
.43
.68
.35
.21

a

Collinearity Diagnostics
Model

Dimension

1

Variance Proportions
ZSHARED_UND
ERSTANDING ZFLEXIBILITY

1
dimension1

2
2

1

.09

2

.00

3

dimension1

.91

dim
ensi
on0

3

1
.06
2
.00
3
.00
4
.94
a. Dependent Variable: PERFORMANCE
dimension1

.05
.66
.02
.27

143

APPENDIX B-5
Salient governance mechanisms analysis – Total Sample

Regression
Notes
Output Created
Comments
Input

16-Feb-2012 14:37:04
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\
Fit_as_Profile_Deviation\FIRSTORDER\CONDITIONAL
_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL_
MEDIAN_PROFDEV_PERF.sav
DataSet1
<none>
<none>
<none>
218
User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PERFORMANCE
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE ZFLEXIBILITY
ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.015
00:00:00.028
6244 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\FIRSTORDE
R\CONDITIONAL_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL_MEDIAN_PRO
FDEV_PERF.sav

144

Variables Entered/Removed
Model
1

Variables Entered
ZINFO_EXCHANGE

Variables
Removed
.

Method
Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).
Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).

dim
ensi
on0

2

ZSHARED_UNDERS
TANDING
a. Dependent Variable: PERFORMANCE

a

.

Model Summary
Model

a

R Square
.293

Adjusted R
Square
.290

b

.367

.361

R
1

.541

dim
ensi
on0

2

.606

Std. Error of the
Estimate
2.203

Change Statistics
R Square
Change
F Change
.293
89.604

2.090

.074

25.112

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING

Model

dim
ensi
on0

1
2

Model Summary
Change Statistics
df2
Sig. F Change
216
.000
215
.000

c

ANOVA
df
1

Mean Square
435.028

1048.678

216

4.855

Total

1483.706

217

Regression

544.704

2

272.352

Residual

2

Sum of Squares
435.028

Residual

1

Model
Regression

939.003

215

4.367

1483.706

217

Total

F
89.604

62.359

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING
c. Dependent Variable: PERFORMANCE

145

Sig.

a

.000

b

.000

df1
1
1

a

Coefficients
Model

1

(Constant)

2

Unstandardized Coefficients
B
Std. Error
14.706
.149

ZINFO_EXCHANGE
(Constant)

Sig.
.000

.150
.142

.541

9.466
103.899

.000
.000

.950
.850

.170
.170

.363
.325

5.600
5.011

.000
.000

a

Coefficients
Model

ZINFO_EXCHANGE
(Constant)

Collinearity Statistics
Tolerance
VIF

(Constant)

2

t
98.545

1.416
14.706

ZINFO_EXCHANGE
ZSHARED_UNDERSTANDIN
G
a. Dependent Variable: PERFORMANCE

1

Standardized
Coefficients
Beta

ZINFO_EXCHANGE
ZSHARED_UNDERSTANDIN
G
a. Dependent Variable: PERFORMANCE

1.000

1.000

.699
.699

1.430
1.430

146

c

Excluded Variables
Model

a

T
.626

Sig.
.532

Partial
Correlation
.043

a

.926

.355

.063

a

.337

.736

.023

a

-.992

.323

-.067

a

-1.330

.185

-.090

a

.095

.924

.006

a

.492

.623

.034

a

.338

.736

.023

a

5.011

.000

.323

b

1.090

.277

.074

b

.387

.699

.026

b

.702

.484

.048

b

.146

.884

.010

b

-1.595

.112

-.108

b

-1.031

.304

-.070

b

.518

.605

.035

b

.303

.762

.021

Beta In
1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

2

ZSHARED_UNDERSTANDIN
G
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

.036
.053
.019

-.057
-.076
.006
.031
.020
.325
.059
.021
.038
.008

-.086
-.059
.031
.017

a. Predictors in the Model: (Constant), ZINFO_EXCHANGE
b. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZSHARED_UNDERSTANDING
c. Dependent Variable: PERFORMANCE

147

c

Excluded Variables
Model

1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDIN
G
2
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
c. Dependent Variable: PERFORMANCE

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.999
1.001
.999
.995
1.005
.995
.996
1.004
.996
.997
1.003
.997
.998
1.002
.998
.959
1.043
.959
.836
1.195
.836
.982
1.018
.982
.699
1.430
.699
.992
.981
.991
.941
.997
.912
.836
.982

1.008
1.020
1.009
1.063
1.003
1.096
1.195
1.018

.694
.690
.696
.660
.698
.665
.615
.692

a

Collinearity Diagnostics
Model

Dimension

Variance Proportions
ZINFO_EXCHAN ZSHARED_UND
GE
ERSTANDING
.50

1

Eigenvalue
1.000

Condition Index
1.000

(Constant)
.50

2

1

1.000

1.000

.50

.50

1.000
1.244
1.851

.00
1.00
.00

.23
.00
.77

dimension1

dim
ensi
on0

2

1
1.548
2
1.000
3
.452
a. Dependent Variable: PERFORMANCE
dimension1

148

.23
.00
.77

APPENDIX B-6
Hypotheses tests for fit as profile deviation – outsourcing performance

Regression
Notes
Output Created
Comments

16-Feb-2012 15:10:58

Input

Data

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analy
sis\Fit_as_Profile_Deviation\FIRSTORDER\CONDIT
IONAL_MEDIAN\PERFORMANCE\DiSSERT_CON
DITIONAL_MEDIAN_PROFDEV_PERF.sav
Active Dataset
DataSet1
Filter
<none>
Weight
<none>
Split File
<none>
N of Rows in Working Data File
218
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics are based on cases with no missing values
for any variable used.
Syntax
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT PERFORMANCE
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
/METHOD=ENTER ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZMISALIGN_PERF ZSTRAT_IMPORT
/METHOD=ENTER ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZSTRAT_IMPORT ZMISALIGN_PERF
INT_STRATIMPORT_MISALIGNPERF.
Resources

Processor Time
Elapsed Time
Memory Required
Additional Memory Required for
Residual Plots

00:00:00.015
00:00:00.030
5764 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\FIRSTORDE
R\CONDITIONAL_MEDIAN\PERFORMANCE\DiSSERT_CONDITIONAL_MEDIAN_PRO
FDEV_PERF.sav

149

Warnings
No variables were entered into the equation.

Variables Entered/Removed
Model
1

dime
nsion
0

2

Variables
Removed
.

Method
Enter

.

Variables Entered
ZSTRAT_IMPORT,
ZCULTURAL_DISTANCE,
ZMISALIGN_PERF,
ZLN_OUTSOURCING_EXP,
ZLN_FIRM_SIZE,
ZCRITICALITY,
ZLN_RELN_LENGTH

Enter

a

INT_STRATIMPORT_MISALI
GNPERF

b

a

a. All requested variables entered.
b. Dependent Variable: PERFORMANCE

Model Summary
Model

Change Statistics

a

R Square
.106

Adjusted R
Square
.068

b

.106

.063

R
1
dime
nsion
0

2

.326
.326

Std. Error of the
Estimate
1.867

R Square
Change
.106

F Change
2.786

df1
7

1.873

.000

.033

1

a. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZMISALIGN_PERF,
ZLN_OUTSOURCING_EXP, ZLN_FIRM_SIZE, ZCRITICALITY, ZLN_RELN_LENGTH
b. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZMISALIGN_PERF,
ZLN_OUTSOURCING_EXP, ZLN_FIRM_SIZE, ZCRITICALITY, ZLN_RELN_LENGTH,
INT_STRATIMPORT_MISALIGNPERF

Model

dime
nsion
0

1
2

Model Summary
Change Statistics
df2
Sig. F Change
164
.009
163
.855

150

c

ANOVA
Df
7

Mean Square
9.714

571.894

164

3.487

Total

639.893

171

Regression

68.117

8

8.515

Residual

571.776

163

3.508

Total

2

Sum of Squares
67.999

Residual

1

Model
Regression

639.893

F
2.786

Sig.

171

a

.009

b

2.427

.017

a. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZMISALIGN_PERF,
ZLN_OUTSOURCING_EXP, ZLN_FIRM_SIZE, ZCRITICALITY, ZLN_RELN_LENGTH
b. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZMISALIGN_PERF,
ZLN_OUTSOURCING_EXP, ZLN_FIRM_SIZE, ZCRITICALITY, ZLN_RELN_LENGTH,
INT_STRATIMPORT_MISALIGNPERF
c. Dependent Variable: PERFORMANCE

a

Coefficients
Model

1

(Constant)

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMISALIGN_PERF
ZSTRAT_IMPORT
(Constant)

Standardized
Unstandardized Coefficients Coefficients
B
Std. Error
Beta
14.771
.143
.022
.198
-.142
-.120
-.093
-.522
.240
14.770

.146
.157
.156
.151
.141
.145
.150
.143

.012
.101
-.073
-.060
-.049
-.270
.124

ZCRITICALITY
.024
.147
.013
ZLN_OUTSOURCING_EXP
.196
.158
.100
ZLN_RELN_LENGTH
-.139
.157
-.072
ZLN_FIRM_SIZE
-.118
.151
-.059
ZCULTURAL_DISTANCE
-.094
.142
-.049
ZMISALIGN_PERF
-.535
.162
-.277
ZSTRAT_IMPORT
.240
.150
.124
INT_STRATIMPORT_MISALIG
.029
.159
.015
NPERF
a. Dependent Variable: PERFORMANCE

151

t
103.497

Sig.
.000

.150
1.262
-.910
-.800
-.658
-3.609
1.601
103.099

.881
.209
.364
.425
.511
.000
.111
.000

.163
1.238
-.881
-.779
-.663
-3.312
1.598
.183

.871
.218
.380
.437
.508
.001
.112
.855

a

Coefficients
Model

Collinearity Statistics
Tolerance
VIF

1

(Constant)

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMISALIGN_PERF
ZSTRAT_IMPORT
(Constant)

.903
.852
.841
.960
.991
.973
.907

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMISALIGN_PERF
ZSTRAT_IMPORT
INT_STRATIMPORT_MISALIG
NPERF
a. Dependent Variable: PERFORMANCE

1.107
1.173
1.188
1.041
1.009
1.027
1.102

.898
.846
.830
.954
.989
.785
.907
.796

1.114
1.182
1.204
1.049
1.011
1.274
1.102
1.257

b

Excluded Variables

Model
Beta In
t
Sig.
Partial Correlation
a
INT_STRATIMPORT_MISALIG
.183
.855
.014
.015
NPERF
a. Predictors in the Model: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE,
ZMISALIGN_PERF, ZLN_OUTSOURCING_EXP, ZLN_FIRM_SIZE, ZCRITICALITY,
ZLN_RELN_LENGTH
b. Dependent Variable: PERFORMANCE
1

b

Excluded Variables
Model

1

INT_STRATIMPORT_MISALIG
NPERF

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.796
1.257
.785

b. Dependent Variable: PERFORMANCE

152

a

Collinearity Diagnostics
Model

Dimension

Eigenvalue
1
1.496
2
1.244
3
1.067
4
1.018
5
.990
6
.921
7
.661
8
.604
2
1
1.509
2
1.431
3
1.198
4
1.057
5
1.011
6
.970
7
.679
8
.655
9
.489
a. Dependent Variable: PERFORMANCE
1

dimension1

dime
nsio
n0

dimension1

Condition Index
1.000
1.097
1.184
1.212
1.230
1.274
1.505
1.574
1.000
1.027
1.123
1.195
1.222
1.247
1.491
1.518
1.756

153

(Constant)
.01
.00
.00
.37
.60
.00
.02
.00
.01
.00
.00
.00
.71
.25
.01
.01
.00

Variance Proportions
ZLN_OUTSOURCI
ZCRITICALITY
NG_EXP
.07
.20
.27
.02
.00
.05
.02
.00
.03
.01
.04
.10
.57
.00
.00
.62
.04
.17
.04
.02
.31
.02
.01
.08
.00
.01
.00
.02
.06
.33
.51
.14
.02
.21

a

Collinearity Diagnostics
Model

Dimension

Variance Proportions
ZLN_RELN_LENG
ZCULTURAL_DIS ZMISALIGN_PER
TH
ZLN_FIRM_SIZE
TANCE
F
1
1
.19
.01
.00
.00
2
.07
.07
.03
.19
3
.04
.48
.11
.01
4
.01
.11
.38
.06
5
.01
.06
.21
.08
6
.00
.00
.23
.58
7
.00
.27
.01
.01
8
.69
.01
.03
.06
2
1
.17
.00
.00
.05
2
.01
.01
.00
.22
3
.07
.21
.03
.00
4
.06
.26
.20
.01
5
.00
.07
.18
.00
6
.00
.12
.55
.01
7
.25
.21
.00
.10
8
.12
.11
.01
.05
9
.31
.00
.02
.56
a. Dependent Variable: PERFORMANCE
dimension1

dime
nsion
0

dimension1

a

Collinearity Diagnostics
Model

Dimension

1

1

Variance Proportions
INT_STRATIMPO
RT_MISALIGNPE
ZSTRAT_IMPORT
RF
.11

2

.09

3

.18

4

.02

5

.00

6

.08

7

.51

8

.00

dimension1

dime
nsion
0

2

1
.07
2
.06
3
.05
4
.24
5
.00
6
.05
7
.16
8
.36
9
.00
a. Dependent Variable: PERFORMANCE
dimension1

.04
.20
.05
.00
.00
.01
.14
.04
.52

154

APPENDIX C
Fit as profile deviation – Analyses for learning outcomes
APPENDIX C-1
Salient governance mechanisms analysis – Unstable risk profile

Regression
Notes
Output Created
Comments
Input

Missing Value Handling

17-Feb-2012 14:13:03
Data

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\F
it_as_Profile_Deviation\DISSERT_DATA.sav
DataSet1
CONDITIONAL_MEDIAN_RISK_GROUP = 1 (FILTER)
<none>
<none>
48

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT LEARNING_OUTCOMES
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
/METHOD=ENTER ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZFLEXIBILITY ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Syntax

Resources

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.047
00:00:00.024
6204 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\DISSERT_D
ATA.sav
Warnings
No variables were entered into the equation.

155

Variables Entered/Removed
Model
1

dim
ensi
on0

Variables Entered
ZINFO_EXCHANGE,
ZLN_RELN_LENGTH,
ZCRITICALITY,
ZCULTURAL_DISTANCE,
ZTS_INVESTMENTS,
ZLN_FIRM_SIZE,
ZLN_OUTSOURCING_EXP,
ZMONITORING, ZFLEXIBILITY,
ZSHARED_UNDERSTANDING

b

Variables
Removed
.

Method
Enter

a

a. All requested variables entered.
b. Dependent Variable: LEARNING_OUTCOMES
Model Summary
Model
R
dim
ensi
on0

1

a

.500

R Square
.250

Adjusted R
Square
.048

Std. Error of the
Estimate
2.68962842

Change Statistics
R Square
Change
F Change
.250
1.235

df1
10

a. Predictors: (Constant), ZINFO_EXCHANGE, ZLN_RELN_LENGTH, ZCRITICALITY, ZCULTURAL_DISTANCE,
ZTS_INVESTMENTS, ZLN_FIRM_SIZE, ZLN_OUTSOURCING_EXP, ZMONITORING, ZFLEXIBILITY,
ZSHARED_UNDERSTANDING

Model
dim
ensi
on0

1

Model Summary
Change Statistics
df2
Sig. F Change
37
.302

b

ANOVA
Sum of Squares
89.336

Df
10

Mean Square
8.934

Residual

267.662

37

7.234

Total

1

Model
Regression

356.998

F
1.235

Sig.

47

a

.302

a. Predictors: (Constant), ZINFO_EXCHANGE, ZLN_RELN_LENGTH, ZCRITICALITY,
ZCULTURAL_DISTANCE, ZTS_INVESTMENTS, ZLN_FIRM_SIZE, ZLN_OUTSOURCING_EXP,
ZMONITORING, ZFLEXIBILITY, ZSHARED_UNDERSTANDING
b. Dependent Variable: LEARNING_OUTCOMES

156

a

Coefficients
Model

1

Unstandardized Coefficients
B
Std. Error
12.765
.407

(Constant)

ZCRITICALITY
.669
ZLN_OUTSOURCING_EXP
.630
ZLN_RELN_LENGTH
.084
ZLN_FIRM_SIZE
.887
ZCULTURAL_DISTANCE
.571
ZFLEXIBILITY
.070
ZMONITORING
-.383
ZTS_INVESTMENTS
-.406
ZSHARED_UNDERSTANDIN
.910
G
ZINFO_EXCHANGE
-.128
a. Dependent Variable: LEARNING_OUTCOMES

1.642
1.599
.198
1.899
1.110
.137
-.680
-.801
1.246

.109
.118
.844
.065
.274
.892
.501
.428
.221

.648

-.040

-.197

.845

Collinearity Statistics
Tolerance
VIF

(Constant)

ZCRITICALITY
.849
ZLN_OUTSOURCING_EXP
.722
ZLN_RELN_LENGTH
.639
ZLN_FIRM_SIZE
.745
ZCULTURAL_DISTANCE
.912
ZFLEXIBILITY
.534
ZMONITORING
.626
ZTS_INVESTMENTS
.754
ZSHARED_UNDERSTANDIN
.412
G
ZINFO_EXCHANGE
.487
a. Dependent Variable: LEARNING_OUTCOMES

Sig.
.000

.254
.268
.035
.313
.165
.027
-.122
-.131
.276

a

1

t
31.399

.407
.394
.426
.467
.514
.512
.564
.507
.731

Coefficients
Model

Standardized
Coefficients
Beta

1.177
1.386
1.566
1.342
1.097
1.874
1.597
1.326
2.428
2.053

157

a

Collinearity Diagnostics
Model

Dimension

Eigenvalue
Condition Index
2.368
1.000
2
1.695
1.182
3
1.484
1.263
4
1.156
1.431
5
.881
1.640
6
.851
1.668
7
.819
1.700
8
.674
1.874
9
.461
2.266
10
.408
2.410
11
.203
3.416
a. Dependent Variable: LEARNING_OUTCOMES
1

dim
ensi
on0

1

dimension1

(Constant)
.01
.01
.05
.19
.00
.41
.24
.01
.00
.09
.00

Variance Proportions
ZLN_OUTSOUR
ZCRITICALITY
CING_EXP
.00
.01
.04
.05
.06
.10
.23
.01
.04
.19
.00
.00
.20
.01
.13
.09
.26
.03
.01
.44
.02
.07

a

Collinearity Diagnostics
Model Dimension

Variance Proportions
ZLN_RELN_LEN
ZCULTURAL_DI
GTH
ZLN_FIRM_SIZE
STANCE
ZFLEXIBILITY
1
1
.00
.03
.00
.05
2
.13
.00
.00
.01
3
.02
.04
.12
.02
4
.05
.04
.11
.00
5
.00
.15
.44
.00
6
.01
.06
.14
.00
7
.01
.05
.00
.07
8
.02
.35
.00
.11
9
.29
.00
.02
.26
10
.34
.05
.16
.07
11
.13
.22
.00
.41
a. Dependent Variable: LEARNING_OUTCOMES
dim
ensi
on0

dimension1

a

Collinearity Diagnostics
Model

Dimension

1

dim
ensi
on0

1

dimension1

2
3
4
5
6
7
8
9
10
11

Variance Proportions
ZTS_INVESTME ZSHARED_UND ZINFO_EXCHAN
NTS
ERSTANDING
GE
.04
.01
.05
.01
.03
.07
.01
.20
.14
.00
.32
.14
.03

.07
.02
.00
.03
.00
.00
.00
.06
.00
.80

.01
.02
.00
.00
.01
.08
.06
.01
.27
.51

158

ZMONITORING
.04
.03
.00
.00
.00
.11
.10
.22
.16
.03
.30

a

Collinearity Diagnostics
Model

Dimension

1

1

Variance Proportions
ZTS_INVESTME ZSHARED_UND ZINFO_EXCHAN
NTS
ERSTANDING
GE
.04
.01
.05

2
.01
3
.03
4
.07
5
.01
6
.20
7
.14
8
.00
9
.32
10
.14
11
.03
a. Dependent Variable: LEARNING_OUTCOMES
dim
ensi
on0

dimension1

.07
.02
.00
.03
.00
.00
.00
.06
.00
.80

.01
.02
.00
.00
.01
.08
.06
.01
.27
.51

159

APPENDIX C-2
Salient governance mechanisms analysis – Uncooperative risk profile

Regression
Notes
Output Created
Comments

17-Feb-2012 14:15:59

Input

Data

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysi
s\Fit_as_Profile_Deviation\DISSERT_DATA.sav
Active Dataset
DataSet1
Filter
CONDITIONAL_MEDIAN_RISK_GROUP = 2
(FILTER)
Weight
<none>
Split File
<none>
N of Rows in Working Data
56
File
Missing Value Handling
Definition of Missing
User-defined missing values are treated as missing.
Cases Used
Statistics are based on cases with no missing values
for any variable used.
Syntax
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT LEARNING_OUTCOMES
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZFLEXIBILITY ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.
Resources

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.016
00:00:00.026
6148 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\DISSERT_D
ATA.sav
Variables Entered/Removed
Model
1

Variables Entered
ZINFO_EXCHANGE

2

Variables
Removed

ZLN_RELN_LENGTH

dim
ensi
on0

a

Method
. Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).
. Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).

a. Dependent Variable: LEARNING_OUTCOMES

160

Model Summary
Model

a

R Square
.152

Adjusted R
Square
.136

b

.281

.254

R
1

.390

dim
ensi
on0

2

.530

Change Statistics
R Square
Change
F Change
.152
9.668

Std. Error of the
Estimate
2.96927275
2.75933926

.129

9.529

df1
1
1

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZLN_RELN_LENGTH

Model

dim
ensi
on0

1
2

Model Summary
Change Statistics
df2
Sig. F Change
54
.003
53
.003

c

ANOVA
Df
1

Mean Square
85.242

476.095

54

8.817

Total

561.338

55

Regression

157.798

2

78.899

Residual

403.540

53

7.614

Total

2

Sum of Squares
85.242

Residual

1

Model
Regression

561.338

F
9.668

55

10.362

Sig.

a

.003

b

.000

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZLN_RELN_LENGTH
c. Dependent Variable: LEARNING_OUTCOMES

a

Coefficients
Model

1

(Constant)

2

ZINFO_EXCHANGE
(Constant)

Unstandardized Coefficients
B
Std. Error
12.749
.410
1.291
12.956

ZINFO_EXCHANGE
1.543
ZLN_RELN_LENGTH
1.236
a. Dependent Variable: LEARNING_OUTCOMES

Standardized
Coefficients
Beta

T
31.060

Sig.
.000

.415
.387

.390

3.109
33.452

.003
.000

.394
.400

.466
.367

3.911
3.087

.000
.003

161

a

Coefficients
Model
1

(Constant)

2

Collinearity Statistics
Tolerance
VIF

ZINFO_EXCHANGE
(Constant)

1.000

1.000

ZINFO_EXCHANGE
.957
ZLN_RELN_LENGTH
.957
a. Dependent Variable: LEARNING_OUTCOMES

1.045
1.045

c

Excluded Variables
Model

a

T
-.749

Sig.
.457

Partial
Correlation
-.102

a

.940

.351

.128

a

3.087

.003

.390

a

-.598

.553

-.082

a

.350

.728

.048

a

-.250

.803

-.034

a

-1.443

.155

-.194

a

1.660

.103

.222

a

.726

.471

.099

b

-.978

.333

-.134

b

.264

.793

.037

b

-1.445

.154

-.197

b

.112

.912

.015

b

.010

.992

.001

b

-1.019

.313

-.140

b

1.780

.081

.240

Beta In
1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

2

ZSHARED_UNDERSTANDIN
G
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS

-.095
.118
.367

-.075
.045

-.032
-.193
.209
.104

-.114
.032

-.172
.013
.001

-.130
.207

b
ZSHARED_UNDERSTANDIN
.784
.437
.104
G
a. Predictors in the Model: (Constant), ZINFO_EXCHANGE
b. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZLN_RELN_LENGTH
c. Dependent Variable: LEARNING_OUTCOMES

162

.108

c

Excluded Variables
Model

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.995
1.005
.995
.992
1.008
.992
.957
1.045
.957
.998
1.002
.998
.976
1.024
.976
.993
1.007
.993
.857
1.167
.857
.960
1.042
.960
.772
1.295
.772

1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDIN
G
2
ZCRITICALITY
.992
ZLN_OUTSOURCING_EXP
.933
ZLN_FIRM_SIZE
.940
ZCULTURAL_DISTANCE
.969
ZFLEXIBILITY
.985
ZMONITORING
.831
ZTS_INVESTMENTS
.960
ZSHARED_UNDERSTANDIN
.772
G
c. Dependent Variable: LEARNING_OUTCOMES

1.008
1.072
1.063
1.032
1.016
1.203
1.042
1.295

.954
.900
.902
.930
.949
.831
.920
.747

a

Collinearity Diagnostics
Model

Dimension

Variance Proportions
ZINFO_EXCHAN ZLN_RELN_LEN
GE
GTH
.37

Eigenvalue
1.256

Condition Index
1.000

(Constant)
.37

.744

1.299

.63

.63

1
1.261
1.000
2
1.108
1.067
3
.630
1.415
a. Dependent Variable: LEARNING_OUTCOMES

.30
.18
.52

.39
.04
.57

1

1
dimension1

2
dim
ensi
on0

2

dimension1

163

.02
.63
.35

APPENDIX C-3
Salient governance mechanisms analysis – Routine risk profile

Regression
Notes
Output Created
Comments
Input

17-Feb-2012 14:22:06
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\
Fit_as_Profile_Deviation\DISSERT_DATA.sav
DataSet1
CONDITIONAL_MEDIAN_RISK_GROUP = 3 (FILTER)
<none>
<none>
61
User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT LEARNING_OUTCOMES
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZFLEXIBILITY ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.016
00:00:00.015
6148 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\DISSERT_D
ATA.sav
Variables Entered/Removed
Model
dim
ensi
on0

1

Variables Entered
ZTS_INVESTMENTS

Variables
Removed
.

a

Method
Stepwise (Criteria: Probability-of-F-to-enter <=
.050, Probability-of-F-to-remove >= .100).
a. Dependent Variable: LEARNING_OUTCOMES

164

Model Summary
Model
R
dim
ensi
on0

1

a

.312

Adjusted R
Square
.082

R Square
.097

Std. Error of the
Estimate
2.76504239

Change Statistics
R Square
Change
F Change
.097
6.367

df1
1

a. Predictors: (Constant), ZTS_INVESTMENTS

Model
dim
ensi
on0

1

Model Summary
Change Statistics
df2
Sig. F Change
59
.014

b

ANOVA
Sum of Squares
48.680

Df
1

Mean Square
48.680

Residual

451.082

59

7.645

Total

1

Model
Regression

499.762

F
6.367

Sig.

60

a

.014

a. Predictors: (Constant), ZTS_INVESTMENTS
b. Dependent Variable: LEARNING_OUTCOMES

a

Coefficients
Model

1

Unstandardized Coefficients
B
Std. Error
13.742
.354

(Constant)

ZTS_INVESTMENTS
.868
a. Dependent Variable: LEARNING_OUTCOMES

.344

a

Coefficients
Model
1

Collinearity Statistics
Tolerance
VIF

(Constant)

ZTS_INVESTMENTS
1.000
a. Dependent Variable: LEARNING_OUTCOMES

1.000

165

Standardized
Coefficients
Beta
.312

T
38.814

Sig.
.000

2.523

.014

b

Excluded Variables
Model

a

T
1.175

Sig.
.245

Partial
Correlation
.153

a

.216

.830

.028

a

.722

.473

.094

a

-.060

.953

-.008

a

1.327

.190

.172

a

1.642

.106

.211

a

.537

.593

.070

a

1.687

.097

.216

a

1.358

.180

.176

Beta In
1

ZCRITICALITY

.159

ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH

.027
.091

ZLN_FIRM_SIZE

-.008

ZCULTURAL_DISTANCE
ZFLEXIBILITY

.169
.200

ZMONITORING

.076

ZSHARED_UNDERSTANDIN
G
ZINFO_EXCHANGE

.207
.167

a. Predictors in the Model: (Constant), ZTS_INVESTMENTS
b. Dependent Variable: LEARNING_OUTCOMES

b

Excluded Variables
Model

1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZSHARED_UNDERSTANDIN
G
ZINFO_EXCHANGE

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.826
1.211
.826
.999
1.001
.999
.974
1.027
.974
.969
1.032
.969
.930
1.076
.930
1.000
1.000
1.000
.775
1.290
.775
.986
1.014
.986
.992

1.008

.992

b. Dependent Variable: LEARNING_OUTCOMES

a

Collinearity Diagnostics
Model Dimension
Eigenvalue
Condition Index
1
1.008
1.000
2
.992
1.008
a. Dependent Variable: LEARNING_OUTCOMES
dime
nsio
n0

1

dimension1

166

Variance Proportions
ZTS_INVESTME
(Constant)
NTS
.50
.50
.50
.50

APPENDIX C-4
Salient governance mechanisms analysis – High-risk risk profile

Regression
Notes
Output Created
Comments
Input

17-Feb-2012 14:26:20
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analys
is\Fit_as_Profile_Deviation\DISSERT_DATA.sav
DataSet1
CONDITIONAL_MEDIAN_RISK_GROUP = 4 (FILTER)
<none>
<none>
53
User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT LEARNING_OUTCOMES
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZFLEXIBILITY ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.047
00:00:00.040
6148 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\DISSERT_D
ATA.sav
Variables Entered/Removed
Model

2

Variables Entered
ZSHARED_UNDERSTAND
ING
ZCULTURAL_DISTANCE

3

ZTS_INVESTMENTS

1
dim
ensi
on0

Variables
Removed
.
.
.

a

Method
Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).
Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).
Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).

a. Dependent Variable: LEARNING_OUTCOMES

167

Model Summary
Model

a

R Square
.159

b

.262

.232

2.25019060

.103

6.943

1

c

.319

.277

2.18315439

.057

4.118

1

R
1
dim
ensi
on0

2
3

.399
.511

.565

Std. Error of the
Estimate
2.37768606

Change Statistics
R Square
Change
F Change
.159
9.643

Adjusted R
Square
.143

a. Predictors: (Constant), ZSHARED_UNDERSTANDING
b. Predictors: (Constant), ZSHARED_UNDERSTANDING, ZCULTURAL_DISTANCE
c. Predictors: (Constant), ZSHARED_UNDERSTANDING, ZCULTURAL_DISTANCE, ZTS_INVESTMENTS

Model

dim
ensi
on0

1
2
3

Model Summary
Change Statistics
df2
Sig. F Change
51
.003
50
.011
49
.048

d

ANOVA

Mean Square
54.518

288.323

51

5.653

342.841

52

Regression

89.673

2

44.837

Residual

253.168

50

5.063

Total

342.841

52

Regression

109.299

3

36.433

Residual

233.542

49

4.766

Total

3

Df
1

Total
2

Sum of Squares
54.518

Residual

1

Model
Regression

342.841

F
9.643

52

8.855

7.644

a. Predictors: (Constant), ZSHARED_UNDERSTANDING
b. Predictors: (Constant), ZSHARED_UNDERSTANDING, ZCULTURAL_DISTANCE
c. Predictors: (Constant), ZSHARED_UNDERSTANDING, ZCULTURAL_DISTANCE,
ZTS_INVESTMENTS
d. Dependent Variable: LEARNING_OUTCOMES

168

Sig.

a

.003

b

.001

c

.000

df1
1

a

Coefficients
Model

Unstandardized Coefficients
B
Std. Error
13.006
.411

1

(Constant)

2

ZSHARED_UNDERSTANDIN
G
(Constant)

3

ZSHARED_UNDERSTANDIN
G
ZCULTURAL_DISTANCE
(Constant)

Standardized
Coefficients
Beta

Sig.
.000

3.105

.003

32.139

.000

1.098

.353

12.786

.398

1.031

.335

.375

3.075

.003

-1.189
12.959

.451
.395

-.321

-2.635
32.789

.011
.000

ZSHARED_UNDERSTANDIN
1.084
G
ZCULTURAL_DISTANCE
-1.158
ZTS_INVESTMENTS
.615
a. Dependent Variable: LEARNING_OUTCOMES

.326

.394

3.319

.002

.438
.303

-.313
.240

-2.644
2.029

.011
.048

a

Coefficients
Model

Collinearity Statistics
Tolerance
VIF

1

(Constant)
1.000

1.000

2

ZSHARED_UNDERSTANDIN
G
(Constant)

.994

1.006

.994

1.006

3

ZSHARED_UNDERSTANDIN
G
ZCULTURAL_DISTANCE
(Constant)

ZSHARED_UNDERSTANDIN
.988
G
ZCULTURAL_DISTANCE
.993
ZTS_INVESTMENTS
.993
a. Dependent Variable: LEARNING_OUTCOMES

1.012
1.007
1.007

169

.399

t
31.638

d

Excluded Variables
Model

a

T
-.009

Sig.
.992

Partial
Correlation
-.001

a

.935

.354

.131

a

.640

.525

.090

a

-.512

.611

-.072

a

-2.635

.011

-.349

a

-.189

.851

-.027

a

-.283

.779

-.040

a

2.005

.050

.273

a

1.524

.134

.211

b

-.493

.624

-.070

b

.058

.954

.008

b

.377

.708

.054

b

-.983

.330

-.139

b

.017

.987

.002

b

-.470

.640

-.067

b

2.029

.048

.278

b

1.389

.171

.195

c

-.616

.540

-.089

c

.080

.937

.012

c

.689

.494

.099

c

-.871

.388

-.125

c

.125

.901

.018

c

-.848

.401

-.122

c

1.146

.257

.163

Beta In
1

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZINFO_EXCHANGE

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZINFO_EXCHANGE

3

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZFLEXIBILITY
ZMONITORING
ZINFO_EXCHANGE

-.001
.123
.083

-.068
-.321
-.028
-.038
.251
.219

-.062
.008
.047

-.125
.002

-.061
.240
.190

-.075
.010
.083

-.108
.017

-.107
.155

a. Predictors in the Model: (Constant), ZSHARED_UNDERSTANDING
b. Predictors in the Model: (Constant), ZSHARED_UNDERSTANDING,
ZCULTURAL_DISTANCE
c. Predictors in the Model: (Constant), ZSHARED_UNDERSTANDING,
ZCULTURAL_DISTANCE, ZTS_INVESTMENTS
d. Dependent Variable: LEARNING_OUTCOMES

170

d

Excluded Variables
Model

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
1
ZCRITICALITY
.994
1.006
.994
ZLN_OUTSOURCING_EXP
.958
1.044
.958
ZLN_RELN_LENGTH
1.000
1.000
1.000
ZLN_FIRM_SIZE
.941
1.062
.941
ZCULTURAL_DISTANCE
.994
1.006
.994
ZFLEXIBILITY
.774
1.292
.774
ZMONITORING
.908
1.101
.908
ZTS_INVESTMENTS
.994
1.006
.994
ZINFO_EXCHANGE
.778
1.286
.778
2
ZCRITICALITY
.961
1.041
.961
ZLN_OUTSOURCING_EXP
.838
1.194
.838
ZLN_RELN_LENGTH
.987
1.013
.981
ZLN_FIRM_SIZE
.917
1.091
.917
ZFLEXIBILITY
.769
1.301
.765
ZMONITORING
.904
1.106
.904
ZTS_INVESTMENTS
.993
1.007
.988
ZINFO_EXCHANGE
.772
1.295
.772
3
ZCRITICALITY
.958
1.044
.958
ZLN_OUTSOURCING_EXP
.838
1.194
.838
ZLN_RELN_LENGTH
.966
1.035
.966
ZLN_FIRM_SIZE
.912
1.096
.912
ZFLEXIBILITY
.766
1.305
.764
ZMONITORING
.878
1.140
.878
ZINFO_EXCHANGE
.757
1.321
.757
d. Dependent Variable: LEARNING_OUTCOMES

a

Collinearity Diagnostics
Model Dimension
Eigenvalue
Condition Index
1
1.607
1.000
2
.393
2.023
2
1
1.665
1.000
2
.959
1.317
3
.376
2.103
3
1
1.718
1.000
2
.984
1.321
3
.938
1.353
4
.360
2.186
a. Dependent Variable: LEARNING_OUTCOMES
1

dimension1

dimension1

dime
nsio
n0

dimension1

171

Variance Proportions
ZSHARED_UND
(Constant)
ERSTANDING
.20
.20
.80
.80
.17
.17
.00
.07
.82
.77
.16
.14
.00
.00
.01
.12
.84
.74

a

Collinearity Diagnostics
Model Dimension

1

Variance Proportions
ZCULTURAL_DI ZTS_INVESTME
STANCE
NTS

1
dimension1

2
2

1
2

.89

3

dimension1

.05

.07

dime
nsio
n0

3

1
.04
2
.49
3
.40
4
.07
a. Dependent Variable: LEARNING_OUTCOMES
dimension1

.04
.47
.42
.07

172

APPENDIX C-5
Salient governance mechanisms analysis – Total Sample

Regression
Notes
Output Created
Comments
Input

17-Feb-2012 14:45:50
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\
Fit_as_Profile_Deviation\DISSERT_DATA.sav
DataSet1
<none>
<none>
<none>
218
User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT LEARNING_OUTCOMES
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE ZFLEXIBILITY
ZMONITORING ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING ZINFO_EXCHANGE.

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

00:00:00.016
00:00:00.034
6148 bytes
0 bytes

[DataSet1]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\DISSERT_D
ATA.sav

173

Variables Entered/Removed
Model

Variables
Removed

1

Variables Entered
ZINFO_EXCHANGE

2

ZTS_INVESTMENTS

3

ZSHARED_UNDERSTANDIN
G
ZLN_RELN_LENGTH

dim
ensi
on0

4

a

Method
. Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).
. Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).
. Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).
. Stepwise (Criteria: Probability-of-F-to-enter <= .050,
Probability-of-F-to-remove >= .100).

a. Dependent Variable: LEARNING_OUTCOMES
Model Summary
Model

a

R Square
.117

b

.149

.141

2.69787733

.032

8.197

1

c

.176

.165

2.66084139

.027

7.027

1

d

.203

.188

2.62433502

.026

6.995

1

R
1
2

.342
.386

dim
ensi
on0

3
4

.420
.450

Std. Error of the
Estimate
2.74245645

Change Statistics
R Square
Change
F Change
.117
28.595

Adjusted R
Square
.113

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS
c. Predictors: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS, ZSHARED_UNDERSTANDING
d. Predictors: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS, ZSHARED_UNDERSTANDING,
ZLN_RELN_LENGTH

Model

dim
ensi
on0

1
2
3
4

Model Summary
Change Statistics
df2
Sig. F Change
216
.000
215
.005
214
.009
213
.009

174

df1
1

e

ANOVA

1624.551

216

7.521

1839.615

217

Regression

274.728

2

137.364

1564.887

215

7.279

Total

1839.615

217

Regression

324.478

3

108.159

Residual

1515.136

214

7.080

Total

1839.615

217

Regression

372.655

4

93.164

Residual

1466.960

213

6.887

Total

4

Mean Square
215.064

Residual
3

Df
1

Total
2

Sum of Squares
215.064

Residual

1

Model
Regression

1839.615

F
28.595

217

18.872

15.277

13.527

Sig.

a

.000

b

.000

c

.000

d

.000

a. Predictors: (Constant), ZINFO_EXCHANGE
b. Predictors: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS
c. Predictors: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS,
ZSHARED_UNDERSTANDING
d. Predictors: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS,
ZSHARED_UNDERSTANDING, ZLN_RELN_LENGTH
e. Dependent Variable: LEARNING_OUTCOMES

a

Coefficients
Model

Unstandardized Coefficients
B
Std. Error
12.809
.186

Standardized
Coefficients
Beta

t
68.962

Sig.
.000

.342

5.347
70.102

.000
.000

.185
.185
.180

.318
.182

5.005
2.863
71.077

.000
.005
.000

.612
.524
.573

.217
.182
.216

.210
.180
.197

2.817
2.875
2.651

.005
.004
.009

12.809

.178

72.066

.000

1

(Constant)

2

ZINFO_EXCHANGE
(Constant)

.996
12.809

.186
.183

3

ZINFO_EXCHANGE
ZTS_INVESTMENTS
(Constant)

.925
.529
12.809

4

ZINFO_EXCHANGE
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDIN
G
(Constant)

ZINFO_EXCHANGE
.616
ZTS_INVESTMENTS
.558
ZSHARED_UNDERSTANDIN
.611
G
ZLN_RELN_LENGTH
.474
a. Dependent Variable: LEARNING_OUTCOMES

.214
.180
.214

2.874
3.098
2.862

.004
.002
.005

.179

175

.211
.192
.210
.163

2.645

.009

a

Coefficients
Model

Collinearity Statistics
Tolerance
VIF

1

(Constant)
ZINFO_EXCHANGE
(Constant)

1.000

1.000

2

.982
.982

1.018
1.018

3

ZINFO_EXCHANGE
ZTS_INVESTMENTS
(Constant)

.692
.982
.699

1.446
1.018
1.430

4

ZINFO_EXCHANGE
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING
(Constant)

ZINFO_EXCHANGE
.692
ZTS_INVESTMENTS
.977
ZSHARED_UNDERSTANDING
.696
ZLN_RELN_LENGTH
.986
a. Dependent Variable: LEARNING_OUTCOMES

1.446
1.024
1.437
1.014

e

Excluded Variables
1

Model
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZTS_INVESTMENTS
ZSHARED_UNDERSTANDING

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING
ZSHARED_UNDERSTANDING

Beta In

a

T
1.051

Sig.
.294

Partial Correlation
.071

a

1.581

.115

.107

a

2.156

.032

.145

a

.107

.915

.007

a

1.066

.288

.072

a

.929

.354

.063

a

-.305

.761

-.021

a

2.863

.005

.192

a

2.637

.009

.177

b

.399

.690

.027

b

1.572

.117

.107

b

2.415

.017

.163

b

-.215

.830

-.015

b

.719

.473

.049

b

.666

.506

.045

b

-1.378

.170

-.094

b

2.651

.009

.178

.067
.101
.137
.007
.068
.061

-.021
.182
.199
.026
.099
.151

-.014
.046
.043

-.101
.197

176

e

3

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING

4

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZFLEXIBILITY
ZMONITORING

Excluded Variables cont’d
c
.645
.520
.041
c
1.287
.200
.080
c
2.645
.009
.163
c
.428
.669
.028
c
.632
.528
.040
c
.093
.926
.006
c
-1.388
.167
-.100
d
.754
.452
.048

.044
.088
.178
.029
.043
.006
-.095
.052

d

.252

.802

.017

d

.304

.761

.021

d

.550

.583

.038

d

-.061

.951

-.004

d

-1.670

.096

-.114

.017
.019
.034

-.004
-.119

a. Predictors in the Model: (Constant), ZINFO_EXCHANGE
b. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS
c. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS,
ZSHARED_UNDERSTANDING
d. Predictors in the Model: (Constant), ZINFO_EXCHANGE, ZTS_INVESTMENTS,
ZSHARED_UNDERSTANDING, ZLN_RELN_LENGTH
e. Dependent Variable: LEARNING_OUTCOMES

177

e

Excluded Variables
Model

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
1
ZCRITICALITY
.999
1.001
.999
ZLN_OUTSOURCING_EXP
.995
1.005
.995
ZLN_RELN_LENGTH
.996
1.004
.996
ZLN_FIRM_SIZE
.997
1.003
.997
ZCULTURAL_DISTANCE
.998
1.002
.998
ZFLEXIBILITY
.959
1.043
.959
ZMONITORING
.836
1.195
.836
ZTS_INVESTMENTS
.982
1.018
.982
ZSHARED_UNDERSTANDING
.699
1.430
.699
2
ZCRITICALITY
.943
1.061
.927
ZLN_OUTSOURCING_EXP
.994
1.006
.977
ZLN_RELN_LENGTH
.991
1.009
.977
ZLN_FIRM_SIZE
.984
1.016
.970
ZCULTURAL_DISTANCE
.981
1.019
.966
ZFLEXIBILITY
.950
1.053
.947
ZMONITORING
.738
1.355
.738
ZSHARED_UNDERSTANDING
.699
1.430
.692
3
ZCRITICALITY
.935
1.069
.692
ZLN_OUTSOURCING_EXP
.981
1.020
.690
ZLN_RELN_LENGTH
.986
1.014
.692
ZLN_FIRM_SIZE
.928
1.078
.659
ZCULTURAL_DISTANCE
.980
1.020
.692
ZFLEXIBILITY
.904
1.107
.665
ZMONITORING
.738
1.355
.615
4
ZCRITICALITY
.934
1.071
.690
ZLN_OUTSOURCING_EXP
.819
1.220
.679
ZLN_FIRM_SIZE
.925
1.081
.657
ZCULTURAL_DISTANCE
.979
1.022
.692
ZFLEXIBILITY
.900
1.111
.661
ZMONITORING
.732
1.367
.614
e. Dependent Variable: LEARNING_OUTCOMES

178

a

Collinearity Diagnostics
Model Dimension

Variance Proportions
ZINFO_EXCHAN ZTS_INVESTME
GE
NTS
.50

1

Eigenvalue
1.000

Condition Index
1.000

(Constant)
.50

2

1.000

1.000

.50

.50

1
1.133
1.000
2
1.000
1.065
3
.867
1.144
3
1
1.588
1.000
2
1.000
1.260
3
.963
1.284
4
.449
1.880
4
1
1.614
1.000
2
1.017
1.259
3
1.000
1.270
4
.921
1.324
5
.448
1.898
a. Dependent Variable: LEARNING_OUTCOMES

.00
1.00
.00
.00
1.00
.00
.00
.00
.00
1.00
.00
.00

.43
.00
.57
.21
.00
.01
.78
.19
.03
.00
.00
.77

1
dimension1

2

dimension1

dim
ensi
on0

dimension1

dimension1

a

Collinearity Diagnostics
Model Dimension
1

Variance Proportions
ZSHARED_UNDERSTANDING
ZLN_RELN_LENGTH

1
dimension1

2
2

1
dimension1

2
3

3

1

.20

2

.00

3

.04

4

dime
nsio
n0

.76

dimension1

4

1
.19
2
.04
3
.00
4
.01
5
.76
a. Dependent Variable: LEARNING_OUTCOMES

.02
.52
.00
.45
.00

dimension1

179

.43
.00
.57
.04
.00
.95
.01
.04
.34
.00
.60
.01

APPENDIX C-6
Hypotheses tests for fit as profile deviation – learning outcomes

Regression
Notes
Output Created
Comments
Input

17-Feb-2012 18:10:29
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

Processor Time
Elapsed Time
Memory Required
Additional Memory Required
for Residual Plots

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysi
s\Fit_as_Profile_Deviation\FIRSTORDER\CONDITIO
NAL_MEDIAN\LEARNING_OUTCOMES\DISSERT_C
ONDITIONALMEDIAN_PROFDEV_LEARNOUTCOM
ES.sav
DataSet2
SAMPLE_TYPE = 'S' (FILTER)
<none>
<none>
131
User-defined missing values are treated as missing.
Statistics are based on cases with no missing values for
any variable used.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
CHANGE
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT LEARNING_OUTCOMES
/METHOD=STEPWISE ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
/METHOD=ENTER ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZMISALIGN_OUTC ZSTRAT_IMPORT
/METHOD=ENTER ZCRITICALITY
ZLN_OUTSOURCING_EXP ZLN_RELN_LENGTH
ZLN_FIRM_SIZE ZCULTURAL_DISTANCE
ZMISALIGN_OUTC ZSTRAT_IMPORT
INT_STRATIMPORT_LRNOUTC.
00:00:00.015
00:00:00.020
5700 bytes
0 bytes

[DataSet2]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Profile_Deviation\FIRSTORDE
R\CONDITIONAL_MEDIAN\LEARNING_OUTCOMES\DISSERT_CONDITIONALMEDIA
N_PROFDEV_LEARNOUTCOMES.sav

180

Warnings
No variables were entered into the equation.

Variables Entered/Removed
Model
1

dim
ensi
on0

2

Variables
Removed

Variables Entered
ZSTRAT_IMPORT,
ZCULTURAL_DISTANCE,
ZLN_FIRM_SIZE,
ZLN_OUTSOURCING_EXP,
ZMISALIGN_OUTC, ZCRITICALITY,
ZLN_RELN_LENGTH

b

.

Method
Enter

.

Enter

a

INT_STRATIMPORT_LRNOUTC

a

a. All requested variables entered.
b. Dependent Variable: LEARNING_OUTCOMES

Model Summary
Model

a

R Square
.055

Adjusted R
Square
.002

b

.070

.009

R
1

.235

dim
ensi
on0

2

.265

Std. Error of the
Estimate
1.797
1.790

Change Statistics
R Square
Change
F Change
.055
1.030
.015

1.961

a. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZLN_FIRM_SIZE,
ZLN_OUTSOURCING_EXP, ZMISALIGN_OUTC, ZCRITICALITY, ZLN_RELN_LENGTH
b. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZLN_FIRM_SIZE,
ZLN_OUTSOURCING_EXP, ZMISALIGN_OUTC, ZCRITICALITY, ZLN_RELN_LENGTH,
INT_STRATIMPORT_LRNOUTC

Model

dim
ensi
on0

1
2

Model Summary
Change Statistics
df2
Sig. F Change
123
.414
122
.164

c

ANOVA
Df
7

Mean Square
3.327

397.378

123

3.231

Total

420.670

130

Regression

29.579

8

3.697

Residual

391.091

122

3.206

Total

2

Sum of Squares
23.292

Residual

1

Model
Regression

420.670

F
1.030

130

1.153

Sig.

a

.414

a. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZLN_FIRM_SIZE,
ZLN_OUTSOURCING_EXP, ZMISALIGN_OUTC, ZCRITICALITY, ZLN_RELN_LENGTH

181

b

.333

df1
7
1

b. Predictors: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE, ZLN_FIRM_SIZE,
ZLN_OUTSOURCING_EXP, ZMISALIGN_OUTC, ZCRITICALITY, ZLN_RELN_LENGTH,
INT_STRATIMPORT_LRNOUTC
c. Dependent Variable: LEARNING_OUTCOMES

a

Coefficients
Model

Unstandardized Coefficients
B
Std. Error
13.029
.158

1

(Constant)

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMISALIGN_OUTC
ZSTRAT_IMPORT
(Constant)

-.103
.107
.234
-.060
.117
-.286
.124
13.037

t
82.211

Sig.
.000

.174
.181
.184
.166
.156
.164
.165
.158

-.055
.058
.127
-.032
.067
-.159
.069

-.592
.594
1.273
-.362
.751
-1.741
.755
82.533

.555
.553
.206
.718
.454
.084
.452
.000

.174
.181
.184
.166
.155
.164
.164
.159

ZCRITICALITY
-.094
ZLN_OUTSOURCING_EXP
.129
ZLN_RELN_LENGTH
.211
ZLN_FIRM_SIZE
-.054
ZCULTURAL_DISTANCE
.115
ZMISALIGN_OUTC
-.295
ZSTRAT_IMPORT
.122
INT_STRATIMPORT_LRNOU
.222
TC
a. Dependent Variable: LEARNING_OUTCOMES

-.050
.070
.114
-.029
.066
-.164
.068
.123

-.541
.714
1.146
-.328
.744
-1.802
.744
1.400

.590
.477
.254
.744
.458
.074
.458
.164

a

Coefficients
Model

Standardized
Coefficients
Beta

Collinearity Statistics
Tolerance
VIF

1

(Constant)
.896
.809
.773
.957
.967
.920
.916

1.116
1.235
1.294
1.045
1.034
1.087
1.092

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMISALIGN_OUTC
ZSTRAT_IMPORT
(Constant)
ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMISALIGN_OUTC
ZSTRAT_IMPORT
INT_STRATIMPORT_LRNOU
TC

.894
.804
.767
.956
.967
.918
.916
.983

1.118
1.244
1.305
1.046
1.034
1.089
1.092
1.017

182

a

Coefficients
Model

Collinearity Statistics
Tolerance
VIF

1

(Constant)

2

ZCRITICALITY
ZLN_OUTSOURCING_EXP
ZLN_RELN_LENGTH
ZLN_FIRM_SIZE
ZCULTURAL_DISTANCE
ZMISALIGN_OUTC
ZSTRAT_IMPORT
(Constant)

.896
.809
.773
.957
.967
.920
.916

1.116
1.235
1.294
1.045
1.034
1.087
1.092

ZCRITICALITY
.894
ZLN_OUTSOURCING_EXP
.804
ZLN_RELN_LENGTH
.767
ZLN_FIRM_SIZE
.956
ZCULTURAL_DISTANCE
.967
ZMISALIGN_OUTC
.918
ZSTRAT_IMPORT
.916
INT_STRATIMPORT_LRNOU
.983
TC
a. Dependent Variable: LEARNING_OUTCOMES

1.118
1.244
1.305
1.046
1.034
1.089
1.092
1.017

b

Excluded Variables
Model
Beta In
1

INT_STRATIMPORT_LRNOUTC

T
1.400

a

.123

Sig.
.164

Partial
Correlation
.126

a. Predictors in the Model: (Constant), ZSTRAT_IMPORT, ZCULTURAL_DISTANCE,
ZLN_FIRM_SIZE, ZLN_OUTSOURCING_EXP, ZMISALIGN_OUTC, ZCRITICALITY,
ZLN_RELN_LENGTH
b. Dependent Variable: LEARNING_OUTCOMES

b

Excluded Variables
Model

1

INT_STRATIMPORT_LRNOUTC

Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
.983
1.017
.767

b. Dependent Variable: LEARNING_OUTCOMES

183

a

Collinearity Diagnostics
Model Dimension
Eigenvalue
Condition Index
1
1.624
1.000
2
1.186
1.170
3
1.122
1.203
4
1.073
1.230
5
.914
1.333
6
.873
1.364
7
.710
1.513
8
.498
1.806
2
1
1.627
1.000
2
1.188
1.170
3
1.143
1.193
4
1.075
1.230
5
1.020
1.263
6
.914
1.334
7
.835
1.396
8
.706
1.518
9
.492
1.818
a. Dependent Variable: LEARNING_OUTCOMES
1

dimension1

dim
ensi
on0

dimension1

(Constant)
.00
.01
.09
.43
.36
.02
.07
.02
.00
.02
.10
.38
.03
.36
.02
.08
.02

Variance Proportions
ZLN_OUTSOUR
ZCRITICALITY
CING_EXP
.06
.15
.31
.01
.00
.02
.02
.02
.00
.14
.03
.08
.44
.08
.14
.49
.06
.15
.30
.02
.00
.04
.01
.02
.00
.01
.00
.14
.05
.04
.42
.08
.14
.51

a

Collinearity Diagnostics
Model Dimension

Variance Proportions
ZLN_RELN_LEN
ZCULTURAL_DI ZMISALIGN_OU
GTH
ZLN_FIRM_SIZE
STANCE
TC
1
1
.16
.00
.00
.04
2
.05
.26
.01
.04
3
.00
.00
.42
.30
4
.01
.26
.07
.01
5
.04
.09
.01
.10
6
.02
.09
.40
.30
7
.11
.24
.00
.00
8
.60
.06
.08
.21
2
1
.16
.00
.00
.04
2
.05
.25
.01
.02
3
.00
.01
.25
.30
4
.01
.24
.14
.02
5
.02
.04
.21
.00
6
.04
.09
.01
.10
7
.00
.06
.29
.33
8
.10
.26
.00
.00
9
.62
.05
.07
.19
a. Dependent Variable: LEARNING_OUTCOMES
dimension1

dim
ensi
on0

dimension1

184

a

Collinearity Diagnostics
Model Dimension

1

1

Variance Proportions
ZSTRAT_IMPOR INT_STRATIMP
T
ORT_LRNOUTC
.09

2

.08

3

.00

4

.07

5

.26

6

.11

7

.33

8

.06

dimension1

dime
nsio
n0

2

1
.09
2
.08
3
.01
4
.06
5
.03
6
.26
7
.11
8
.31
9
.06
a. Dependent Variable: LEARNING_OUTCOMES
dimension1

.00
.01
.12
.01
.60
.00
.21
.02
.02

185

APPENDIX D

Governance mechanisms – Cluster analysis
Quick Cluster
Notes
Output Created
Comments
Input

06-Jul-2011 18:35:36
Data

Missing Value Handling

Active Dataset
Filter
Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

Syntax

Resources

Variables Created or
Modified

Processor Time
Elapsed Time
Workspace Required
QCL_1

C:\Users\Ravi\Dropbox\Outsourcing\DATA\DATA
_FOR_ANALYSIS\OVERAL_DATA\DISSERT_DA
TA.sav
DataSet1
<none>
<none>
<none>
218
User-defined missing values are treated as missing.
Statistics are based on cases with no missing values
for any clustering variable used.
QUICK CLUSTER ZFLEXIBILITY ZMONITORING
ZCOMMITMENT ZSHARED_UNDERSTANDING
ZINFO_EXCHANGE
/MISSING=LISTWISE
/CRITERIA=CLUSTER(4) MXITER(20)
CONVERGE(0)
/METHOD=KMEANS(NOUPDATE)
/SAVE CLUSTER
/PRINT INITIAL ANOVA.
00:00:00.078
00:00:00.057
1344 bytes
Cluster Number of Case

[DataSet1]
C:\Users\Ravi\Dropbox\Outsourcing\DATA\DATA_FOR_ANALYSIS\OVERAL_DATA\DISS
ERT_DATA.sav
Initial Cluster Centers

ZFLEXIBILITY
ZMONITORING
ZCOMMITMENT
ZSHARED_UNDERSTANDING
ZINFO_EXCHANGE

1
.42968
.78508
-2.49943
-3.07292
-.44300

Cluster
2
3
-1.52917
.91940
-2.15608
1.52038
-3.07943
1.56055
.70614
2.12329
-1.65968
.36812

186

4
-2.01888
.04979
1.56055
-3.07292
-3.28193

a

Iteration History
Iteration

Change in Cluster Centers
1
2
3
4
1
2.706
2.704
2.421
2.745
2
.182
.175
.114
.261
3
.160
.134
.095
.184
4
.095
.085
.047
.200
5
.057
.109
.018
.193
6
.052
.170
.035
.192
7
.123
.064
.039
.106
8
.071
.070
.014
.178
9
.061
.123
.027
.243
10
.033
.137
.024
.161
11
.088
.090
.054
.132
12
.115
.083
.078
.109
13
.153
.106
.038
.307
14
.152
.071
.057
.084
15
.124
.018
.071
.080
16
.110
.039
.016
.107
17
.080
.022
.036
.047
18
.023
.000
.017
.000
19
.029
.035
.018
.000
20
.000
.000
.000
.000
a. Convergence achieved due to no or small change in cluster
centers. The maximum absolute coordinate change for any center
is .000. The current iteration is 20. The minimum distance between
initial centers is 5.347.
dimensio
n0

Final Cluster Centers

ZFLEXIBILITY
ZMONITORING
ZCOMMITMENT
ZSHARED_UNDERSTANDING
ZINFO_EXCHANGE

1
.05701
.28097
.33629
-.53532
-.36724

Cluster
2
3
-.32179
.58189
-.59179
.74037
-.62402
.51533
.46082
.71914
.25756
.72542

4
-.85586
-1.16864
-.73754
-1.30486
-1.30047

ANOVA
Cluster
Error
Mean Square
df
Mean Square
df
F
Sig.
ZFLEXIBILITY
18.721
3
.752
214
24.909
.000
ZMONITORING
36.934
3
.496
214
74.427
.000
ZCOMMITMENT
21.703
3
.710
214
30.578
.000
ZSHARED_UNDERSTANDING
41.771
3
.428
214
97.497
.000
ZINFO_EXCHANGE
36.446
3
.503
214
72.445
.000
The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize
the differences among cases in different clusters. The observed significance levels are not corrected for this and
thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.

187

Number of Cases in each
Cluster
Cluster
1
58.000
2
51.000
3
74.000
4
35.000
Valid
218.000
Missing
.000

188

APPENDIX E

Gestalts – T-tests – Outsourcing Performance
T-Test
Notes
Output Created
Comments
Input

03-Dec-2011 17:31:05
Data

Active Dataset
Filter

Missing Value Handling

Weight
Split File
N of Rows in Working
Data File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit
_as_Gestalts\FIT_GESTALTS.sav
DataSet3
RELATIONSHIP_GROUP =
"STRATEGIC_PARTNERSHIP" (FILTER)
<none>
<none>
32
User defined missing values are treated as missing.
Statistics for each analysis are based on the cases with
no missing or out-of-range data for any variable in the
analysis.
T-TEST GROUPS=MATCH(1 0)
/MISSING=ANALYSIS
/VARIABLES=PERFORMANCE
/CRITERIA=CI(.95).

Processor Time
Elapsed Time

00:00:00.000
00:00:00.000

[DataSet3]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Gestalts\FIT_GESTALTS.sav
Group Statistics
MATCH
PERFORMANCE
dimens
ion1

PERFORMANCE

1
0

N
19
13

Mean
17.26
16.86

Std. Deviation
1.408
2.125

Std. Error Mean
.323
.589

Independent Samples Test
Levene's Test for Equality of
Variances
F
Sig.
Equal variances assumed
4.824
.036
Equal variances not
assumed

189

t-test for Equality of Means
t
df
.646
30
.599
19.139

PERFORMANCE

PERFORMANCE

Independent Samples Test
t-test for Equality of Means
Std. Error
Sig. (2-tailed) Mean Difference
Difference
Equal variances assumed
.523
.402
.623
Equal variances not
.556
.402
.672
assumed
Independent Samples Test
t-test for Equality of Means
95% Confidence Interval of the
Difference
Lower
Upper
Equal variances assumed
-.870
1.675
Equal variances not
-1.004
1.808
assumed

T-Test
Notes
Output Created
Comments
Input

03-Dec-2011 17:39:11
Data

Active Dataset
Filter

Missing Value Handling

Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit
_as_Gestalts\FIT_GESTALTS.sav
DataSet3
RELATIONSHIP_GROUP =
"SELECTIVE_PARTNERSHIP" (FILTER)
<none>
<none>
26

Syntax

Resources

Processor Time
Elapsed Time

User defined missing values are treated as missing.
Statistics for each analysis are based on the cases with
no missing or out-of-range data for any variable in the
analysis.
T-TEST GROUPS=MATCH(1 0)
/MISSING=ANALYSIS
/VARIABLES=PERFORMANCE
/CRITERIA=CI(.95).
00:00:00.016
00:00:00.016

[DataSet3]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Gestalts\FIT_GESTALTS.sav

190

Group Statistics
MATCH
PERFORMANCE
dimens
ion1

PERFORMANCE

PERFORMANCE

PERFORMANCE

1
0

N
12
14

Mean
16.00
16.98

Std. Deviation
2.449
1.535

Std. Error Mean
.707
.410

Independent Samples Test
Levene's Test for Equality of
Variances
F
Sig.
Equal variances assumed
.878
.358
Equal variances not
assumed

t-test for Equality of Means
t
df
-1.241
24
-1.198
17.935

Independent Samples Test
t-test for Equality of Means
Std. Error
Sig. (2-tailed) Mean Difference
Difference
Equal variances assumed
.227
-.980
.789
Equal variances not
.246
-.980
.818
assumed
Independent Samples Test
t-test for Equality of Means
95% Confidence Interval of the
Difference
Lower
Upper
Equal variances assumed
-2.609
.650
Equal variances not
-2.698
.738
assumed

191

T-Test
Notes
Output Created
Comments
Input

03-Dec-2011 17:40:50
Data

Active Dataset
Filter

Missing Value Handling

Weight
Split File
N of Rows in Working
Data File
Definition of Missing
Cases Used

Syntax

Resources

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\F
it_as_Gestalts\FIT_GESTALTS.sav
DataSet3
RELATIONSHIP_GROUP = "ARM'S LENGTH
RELATIONSHIP" (FILTER)
<none>
<none>
28
User defined missing values are treated as missing.
Statistics for each analysis are based on the cases with
no missing or out-of-range data for any variable in the
analysis.
T-TEST GROUPS=MATCH(1 0)
/MISSING=ANALYSIS
/VARIABLES=PERFORMANCE
/CRITERIA=CI(.95).

Processor Time
Elapsed Time

00:00:00.000
00:00:00.015

[DataSet3]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Gestalts\FIT_GESTALTS.sav
Group Statistics
MATCH
PERFORMANCE
dimens
ion1

PERFORMANCE

PERFORMANCE

1
0

N
25
3

Mean
11.78
14.91

Std. Deviation
2.438
3.004

Std. Error Mean
.488
1.734

Independent Samples Test
Levene's Test for Equality of
Variances
F
Sig.
Equal variances assumed
.030
.864
Equal variances not
assumed

t-test for Equality of Means
t
df
-2.061
26
-1.738
2.328

Independent Samples Test
t-test for Equality of Means
Std. Error
Sig. (2-tailed) Mean Difference
Difference
Equal variances assumed
.049
-3.132
1.519
Equal variances not
.206
-3.132
1.802
assumed

192

PERFORMANCE

Independent Samples Test
t-test for Equality of Means
95% Confidence Interval of the
Difference
Lower
Upper
Equal variances assumed
-6.254
-.009
Equal variances not
-9.927
3.663
assumed

T-Test
Notes
Output Created
Comments

03-Dec-2011 18:04:54

Input

Data
Active Dataset
Filter

Missing Value Handling

Weight
Split File
N of Rows in Working Data
File
Definition of Missing
Cases Used

C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\F
it_as_Gestalts\FIT_GESTALTS.sav
DataSet3
RELATIONSHIP_GROUP =
"ADVERSARIAL_RELATIONSHIP" (FILTER)
<none>
<none>
24
User defined missing values are treated as missing.
Statistics for each analysis are based on the cases with
no missing or out-of-range data for any variable in the
analysis.
T-TEST GROUPS=MATCH(1 0)
/MISSING=ANALYSIS
/VARIABLES=PERFORMANCE
/CRITERIA=CI(.95).

Syntax

Resources

Processor Time
Elapsed Time

00:00:00.015
00:00:00.022

[DataSet3]
C:\Users\Ravi\Dropbox\Research\Outsourcing\Analysis\Fit_as_Gestalts\FIT_GESTALTS.sav
Group Statistics
MATCH
PERFORMANCE
dimens
ion1

PERFORMANCE

1
0

N
9
15

Mean
13.75
12.16

Std. Deviation
1.171
2.096

Std. Error Mean
.390
.541

Independent Samples Test
Levene's Test for Equality of
Variances
F
Sig.
Equal variances assumed
1.454
.241
Equal variances not
assumed

193

t-test for Equality of Means
t
df
2.070
22
2.374
21.956

PERFORMANCE

PERFORMANCE

Independent Samples Test
t-test for Equality of Means
Std. Error
Sig. (2-tailed) Mean Difference
Difference
Equal variances assumed
.050
1.584
.765
Equal variances not
.027
1.584
.667
assumed
Independent Samples Test
t-test for Equality of Means
95% Confidence Interval of the
Difference
Lower
Upper
Equal variances assumed
-.003
3.171
Equal variances not
.200
2.968
assumed

194

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