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Electronic Theses, Treatises and Dissertations
The Graduate School
2005
Transaction Attributes and Governance
Choice: A Meta-Analytic Examination of
Key Transaction Cost Theory Predictions
Thomas Russell Crook
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THE FLORIDA STATE UNIVERSITY
COLLEGE OF BUSINESS
TRANSACTION ATTRIBUTES AND GOVERNANCE CHOICE: A META-ANALYTIC
EXAMINATION OF KEY TRANSACTION COST THEORY PREDICTIONS
By
THOMAS RUSSELL CROOK
A Dissertation submitted to the
Department of Management
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Degree Awarded:
Summer Semester, 2005
The members of the Committee approve the dissertation of Thomas Russell Crook defended on
June 14, 2005.
_____________________________
David J. Ketchen
Professor Co-Directing Dissertation
_____________________________
James G. Combs
Professor Co-Directing Dissertation
_____________________________
Larry C. Giunipero
Outside Committee Member
_____________________________
William Anthony
Committee Member
Approved:
_____________________________
E. Joe Nosari, Interim Dean, College of Business
The Office of Graduate Studies has verified and approved the above named committee members.
ii
ACKNOWLEDGEMENTS
To be at this point, where writing acknowledgments is necessary, is a very exciting, yet
humbling moment. I am excited because this signals that I have persevered through the most
challenging undertaking of my life. At the same time, it is humbling because I could not have
gotten here without the guidance and support of many special people.
First, I want to acknowledge the support of my dissertation co-chairpersons Jim Combs
and Dave Ketchen. Words cannot express how grateful I am for the help each has offered over
the last four years. Jim Combs has been very patient, and has not only served as my dissertation
co-chair, but as an invaluable mentor. He has given unselfishly and has patiently helped me craft
the dissertation through many conversations and a tremendous amount of written and timely
feedback. Dave Ketchen has also played an invaluable role in helping me craft the dissertation.
Equally important, though, he has mentored me, and taught me about scholarship and life. To
both of you, I am grateful, and humbled that you have helped me get to this point.
Second, I want to acknowledge Bill Anthony and Larry Giunipero for their support in my
four years at Florida State University and their contributions to the Dissertation Committee. It
was a great privilege to learn from each of them, and also getting to them as great human beings.
Also, I am grateful for the help of my expert panel - Garry Adams (Auburn University), Todd
Alessandri (Syracuse University), and Taco Reus (Florida Atlantic University) – who generously
offered their time.
Third, I want to acknowledge those that helped in other ways, including Jerry Ferris,
Bruce Lamont, Kathy Duval, and Scheri Martin. I am grateful for all that each of them has done.
Fourth, I want to thank all my friends that have been there along the way. The list is far
too long to mention each of them by name, but I could not have done it without them.
Fifth, I want to thank my family, especially my wife Tamara, for both loving and putting
up with me, and never giving me a hard time when I needed to work. I would also be remiss if I
did not thank my parents, who have supported me from day one. My mother and father, Babette
and Johnny LaPonzina, have been there for every step along the way, especially when I needed it
most. Thanks for those football analogies such as “you are on the 30 yard line. You are so close
to the goal line. Keep driving.” I needed that. Also, thanks to my father, Tom Crook, for his
iii
support. I think this process has gotten us all closer. Last, but certainly not least, I want to thank
God for all the blessings along the way. There were many ‘rough patches’ that looking back
were invaluable lessons, and I needed. Indeed, looking back there was a reason for those rough
patches, and as a result, I feel better equipped to be a successful research, teacher, and human
being.
iv
TABLE OF CONTENTS
List of Tables
List of Figures
Abstract
vi
vii
viii
CHAPTER 1: INTRODUCTION
Justification For The Study
Significance Of The Study
Summary Of Remaining Chapters
1
3
8
10
CHAPTER 2: LITERATURE REVIEW
Transaction Attributes And The Degree Of Integration
Moderating Effects Among Transaction Attributes
Exogenous Moderating Effects
Summary
11
22
32
35
36
CHAPTER 3: HYPOTHESES
Main Effect Hypotheses
Interactions Among TCT Variables
Exogenous Moderating Effects
Performance Implications
Summary
37
38
44
56
61
63
CHAPTER 4: METHOD
Sample
Coding
Measures
Meta-Analytic Procedures
Summary
64
64
65
66
76
84
CHAPTER 5: RESULTS
Post Hoc Robustness Test Results
Summary
85
88
90
CHAPTER 6: DISCUSSION AND CONCLUSION
Limitations
Hypotheses With Significant Results
Hypotheses Lacking Significant Results
Broader Implications Of Results
Conclusion
92
92
95
100
105
111
APPENDICES
112
REFERENCES
119
BIOGRAPHICAL SKETCH
140
v
LIST OF TABLES
Table 1: Summaries of Key TCT Foundational Works
Table 2: Transaction Attributes and Governance Choice
Table 3: Key Differences Between TCT and Real Options Theory
Table 4: TCT’s Core Construct Definitions
Table 5: Primary Study Measures and Content Validity Ratings
Table 6: Degree of Integration Measures
Table 7: Primary Study Uncertainty Rankings
Table 8: Primary Study Transaction Frequency Classifications
Table 9: Collectivism and Property Protection Rankings
Table 10: Hypothesis Test Results
Table 11: Post Hoc Test Results
Table 12: Asset Specificity Measures Strongly Related To Performance
vi
12
22
29
67
68
75
81
82
83
86
91
110
LIST OF FIGURES
Figure 1: TCT’s Theoretical Logic
Figure 2: Degree of Integration
Figure 3: Hypothesized Main Effects
Figure 4: Relationships Between Asset Specific Investments and Integration
Figure 5: Relationships Between Uncertainty and Integration
Figure 6: Interactions Among Different Transaction Attributes
Figure 7: Exogenous Moderating Factors
Figure 8: Discriminating Alignment Performance Implications
vii
13
16
39
45
45
53
57
62
ABSTRACT
Since its introduction by Williamson (1975), transaction cost theory (TCT) has become
one of the most influential theoretical perspectives used for explaining how economic activity is
governed. TCT asserts that the nature of a transaction drives governance decisions such that asset
specificity, uncertainty, and frequent transactions give rise to the threat of opportunism, which
increases transaction costs, and leads firms towards more integration. Although three decades
have passed since its introduction and over 100 empirical journal articles have been published,
more recent theoretical developments as well as contradictory findings have called TCT’s
empirical validity into question. By aggregating findings via meta-analysis, I take a step toward
resolving these contradictory findings. Specifically, I found minimal evidence supporting the
relationships between asset specificity or frequency and governance choice, and no relationship
between environmental or behavioral uncertainty and governance choice. Further, there was
evidence supporting the notion that matching transactions to the appropriate degree of integration
impacts firm performance. Thus, I conclude by suggesting that although TCT helps explain
governance choice decisions, the inclusion of other theoretical perspectives is also needed to
provide a more comprehensive understanding of how firms govern economic activity.
viii
CHAPTER 1: INTRODUCTION
Since its introduction by Williamson in the mid-1970s, transaction cost theory (TCT) has
become one of the most influential perspectives used for explaining how economic activity is
governed. Indeed, organization theory, strategic management, economics, and marketing
scholars, among others, have published hundreds of articles using this perspective to explain why
firms choose market, hierarchical, or hybrid modes of governance (Boerner & Macher, 2002).
TCT is heavily influenced by Ronald Coase (1937), who first sought to explain why firms
existed when markets were, at the time, perceived to be optimally efficient. In short, Coase
believed that markets and firms possess different capacities for managing economic exchange,
and that markets were not always optimally efficient due at least in part to transaction costs.
Transaction costs arise from an economic actor’s efforts to identify fair market prices, negotiate,
and carry out market contracts. These costs differ from production costs, which are held constant
in TCT (Williamson, 1985). In cases where firms could attenuate transaction costs arising from
the process of identifying fair prices, negotiating, and carrying out contracts, Coase argued that
firms could govern economic activity more efficiently than markets. In such cases, hierarchical
governance (i.e., firms) would be chosen over market governance.
Following Commons (1934), transactions are the central unit of analysis in TCT
(Williamson, 1975, 1985). Transactions are defined as goods or service transfers across
technologically separable workgroups wherein one stage of economic activity ends and another
begins. According to TCT, transactions are governed by three alternative modes: 1) markets, 2)
hierarchies, and 3) hybrids, such as alliances, franchises, or joint ventures. Although Williamson
(1975) recognized that market transactions take several forms, he was principally concerned with
markets characterized by ongoing “arms-length” relationships, and placed less emphasis on onetime or occasional exchange. In TCT, markets are thus described as transactions between buyers
and sellers from different firms wherein buyers and sellers transact on an ongoing basis using
formal, negotiated contracts. Hierarchies refer to transactions within the same firm. Hybrids take
on characteristics of both markets and hierarchies. These relationships are typically closer than
arms-length market contracts, but the buyers and sellers remain legally distinct. Each mode
differs in its ability to reduce transaction costs and adapt to environmental disturbances,
1
depending on the nature of the transaction (Williamson, 1991). Accordingly, each mode has
different advantages and disadvantages. TCT posits that matching transactions with the
appropriate governance mode leads to improved firm performance through lower costs and
enhanced adaptability (Williamson, 1981).
TCT relies on two main behavioral assumptions. First, TCT assumes that economic
actors are boundedly rational (Simon, 1945; Williamson, 1975). Thus, transacting parties cannot
anticipate and specify all exchange contingencies before they surface. Second, TCT assumes that
economic actors are opportunistic (Williamson, 1975). The assumption of opportunism suggests
that transacting parties will seek advantages at each other’s expense. Consequently, bounded
rationality and the threat of opportunism create exchange hazards for transacting parties.
Exchange hazards are defined as situations wherein one transactor has the ability to take
advantage of contractual agreements by exploiting unwritten or unenforceable parts of the
contract (Klein, 1980). Exchange hazards give rise to adaptation problems. Adaptation problems
refer to a firm’s inability to respond to environmental disturbances. To guard against potential
exchange hazards and subsequent adaptation problems, transactors can either craft more
complete agreements that protect each party’s long-term interests, which increase transaction
costs, or hierarchies can be used.
Williamson (1975,1979) delineates three main transaction attributes: asset specificity,
uncertainty, and frequency. Asset specificity refers to the degree of investment required or that is
uniquely dedicated to support a transaction (Klein, Crawford, & Alchian, 1978; Williamson,
1979). Uncertainty relates to unforeseeable disturbances or the inability to measure outcomes
efficiently (Hayek, 1945; Williamson, 1985). Frequency refers to how often a transaction occurs
(Williamson, 1981). These attributes influence whether a transaction will be governed by a
market (i.e., buy decision), hierarchy/firm (i.e., make decision), or hybrid (e.g., joint venture).
TCT predicts that asset specificity, uncertainty, and frequency are positively related to
hierarchical governance (Williamson, 1975, 1979, 1985). More specifically, as asset specificity
and uncertainty increase, exchange hazards are created that result in higher transaction costs in
exchange relations. Because of the higher transaction costs, TCT predicts that asset specificity
and uncertainty are positively related to hierarchical governance. TCT also predicts that as
2
transaction frequency increases, fixed and variable production and transaction costs can be
reduced through hierarchies.
Justification For The Study
Although TCT has been widely used to explain governance choice, theoretical
developments, contradictory findings, and criticisms have called its empirical validity into
question. The most prominent questions surround uncertainty and asset specificity. In contrast to
TCT’s prediction that uncertainty is positively related to hierarchical governance, real options
theory (Myers, 1977, 1984) purports that firms may seek to avoid commitments (e.g., asset
specific investments) during periods of uncertainty. The logic of real options theory asserts that
such commitments can be costly and irreversible, which can reduce a firm’s adaptability and
threaten its survival (Folta, 1998). When firms commit large sums to asset specific investments,
their ability to change and adapt to environmental disturbances diminishes. This logic might
explain Schilling and Steensma’s (2002) finding that managers choose to contract (buy) rather
than acquire (develop internally or make) management information systems technological
expertise. They found that managers ‘bought’ or outsourced technological expertise, rather than
develop these skills internally, when they perceived high uncertainty caused by fast-paced
change in the underlying technology. Unlike TCT’s prediction that uncertainty leads to
hierarchical governance, Schilling and Steensma’s findings, which are consistent with real
options theory, suggest that uncertainty may in fact lead some firms to avoid hierarchical
governance.
Beyond the contradictory logic real options theory provides regarding TCT’s uncertainty
prediction, several researchers, including Schilling and Steensma (2002), have not found a
linkage between uncertainty and hierarchical governance (e.g., Anderson & Schmittlein, 1984;
Balakrishnan and Wernerfelt, 1986; Harrigan, 1985, 1986). In support of TCT, however, several
studies indicate that uncertainty leads to hierarchical governance (e.g., John & Weitz, 1988;
Masten, 1984; Masters & Miles, 2002; Walker & Weber, 1984). To resolve this ambiguity,
David and Han (2004) conducted a literature review and found that hypotheses using TCT logic
were supported only 24 percent of the time. Taken together, these theoretical developments and
contradictory findings cast doubt on the notion that uncertainty leads to hierarchical governance.
3
TCT also predicts that transactions involving asset specific investments are positively
related to hierarchical governance (Williamson, 1979, 1981). Asset specific investments lose
value outside the focal relationship because they are difficult to redeploy without significant
costs. Because these investments lose value outside the focal relationship and are difficult to
redeploy, TCT’s assumptions imply that they create exchange hazards, resulting in higher
transaction costs and subsequent adaptation problems. Exchange hazards are created when
opportunistic economic actors can exploit other transactors once asset specific investments are
made (Williamson, 1985). In essence, boundedly rational managers cannot predict all
contingencies and specify appropriate contractual resolutions that protect market exchange from
opportunism, thereby creating exchange hazards. Thus, when asset specific investments support
a transaction, TCT posits that firms will “make” rather than “buy” products to avoid exchange
hazards and the potential resulting adaptation problems (Williamson, 1981).
In support of TCT’s predictions, several scholars have shown that asset specific
investments positively influence hierarchical governance (e.g., Monteverde & Teece, 1982;
Masten, 1984; Masters and Miles, 2002). Diverging from TCT’s predictions, however, asset
specific investments have also been shown to reduce transaction costs between exchange
partners (Dyer, 1997). One explanation is provided by Dyer and Singh (1998) who argue that
transaction specific investments are necessary for partners to accrue “relational rents.” Relational
rents refer to synergistic benefits, or higher profits, created by close exchange relationships, such
as strategic alliances, that exceed what firms could generate through arms-length exchange.
These rents are created when transactors work closely together to leverage each other’s assets,
thereby resulting in synergies that could not be achieved when working autonomously. In order
for such rents to be created, however, transactors must first invest in asset specific investments to
support efficient exchange. The need for exchange partners to invest in specific assets, however,
contradicts TCT’s prediction that asset specific investments create exchange hazards and
adaptability problems that give rise to hierarchical governance (Williamson, 1979, 1981).
A second reason why transactions supported by asset specific investments might not be
governed by hierarchy is offered by resource-based theory (RBT). RBT adherents assert that it is
capabilities, not just transaction costs, that determine how a particular transaction is governed
4
(e.g., Argyres, 1996). Even Williamson (1999: 1103) acknowledged this; he asserted that the
capability view “should help to reduce the unexplained variance in simple tests of the generic
alignment hypothesis.” Some studies offer support for this notion. Combs and Ketchen (1999),
for example, showed that brand and human asset capabilities led firms toward more integration.
Argyres (1996) asserted that heterogeneously distributed firm capabilities shape governance
decisions; superior capabilities lead to more integration. Poppo and Zenger (1998) found that
specialized skills led to more integration and as a consequence, better firm performance.
Nevertheless, when it is suggested that capabilities are the key driver of integration decisions or
when questions surface about the importance of transaction costs (e.g., Ghoshal & Moran, 1996),
Williamson (1996: 55) responds that TCT is an “empirical success story”, and thus, should
continue to be used to explain governance choice decisions. Yet this might not be the case.
A third explanation why asset specific investments might not lead to hierarchical
governance is provided by real options theory. According to real options theory, firms are
concerned about the tradeoff between committing resources (i.e., making asset specific
investments) too early versus too late (Miller & Folta, 2002). When firms invest resources too
early, these investments commit the firm to an irreversible course of action, and therefore can
limit future flexibility and adaptability (Folta, 1998). Instead, firms may want to adopt hybrids
such as licensing agreements or take an equity stake in a partner. These provide viable options
that increase the firm’s long-term flexibility and adaptability (Miller & Folta, 2002). In short,
real options theory indicates that there might not be a positive linear relationship between asset
specific investments and hierarchical governance as TCT predicts.
A forth and final reason why asset specific investments might not lead to hierarchical
governance is that opportunism might not be the pervasive condition described by Williamson
(1985). Some researchers have admonished Williamson for the prominent position opportunism
holds in TCT because people often place others’ needs above their own (e.g., Ghoshal, 2005;
Ghoshal & Moran, 1996; Perrow, 1986). Camerer and Thaler (1995), for example, showed that
even when one party was in a position to demand more (i.e., act opportunistically) from another
party, the party in the more favorable position usually does not take advantage of the other party.
This evidence suggests that people consider fairness in addition to their self-interest. Beyond the
5
notion that many people act fairly, opportunism is constrained because firms and individuals are
embedded in rich social contexts; the benefits obtained through opportunistic actions are often
outweighed by the negatives associated with taking advantage of others (Granovetter, 1985; Hill,
1990; Perrow, 1986). In other words, acting opportunistically might often be shortsighted. Thus
even if opportunism exists as Williamson (1985) suggests, it might not matter that much because
many people act fairly and those with a propensity toward acting opportunistically are
constrained by the potentially negative fallout.
Because of competing theoretical explanations, contradictory findings, criticisms, and the
widespread application of TCT, a definitive empirical test seems needed. Although researchers
have conducted numerous qualitative literature reviews in an effort to explain extant empirical
findings and to assess TCT’s predictive validity, they reveal mixed empirical findings (e.g.,
Boerner & Macher, 2002; Mahoney, 1992; Masten, 1995; Rindfleisch & Heide, 1997). David
and Han’s (2004: 39) recent analysis, for example, analyzed TCT’s cumulative evidence and
reported that “results are mixed: while we found some support in some areas, we also found
relatively low levels of empirical support in other core areas.” Due to a lack of data availability,
they did not report results for the relationship between frequency and governance choice.
However, when examining 107 empirical tests of the asset specificity construct, they found that
asset specificity is positively related to the degree of integration in 60% of the cases they
examined. When examining 87 empirical tests of the uncertainty construct, they found that
uncertainty is positively related to the degree of integration in 24% of the cases they examined.
Such reviews not only reveal ambiguous findings surrounding TCT’s core predictions, but these
types of reviews have also been criticized for being unreliable (Hunter & Schmidt, 1990).
Qualitative reviews are unreliable because they use vote count procedures wherein
reviewers (e.g., David & Han, 2004) sum the number of times a hypotheses is supported versus
found lacking support. However, these procedures fail to account for the effect of a study’s
research design on its results, and thus do not account for sampling and measurement error
present in primary studies (Hunter & Schmidt, 1990). Sampling error is the difference between
the size of the relationship reported in a primary study’s sample and the actual population
relationship. These errors are present in most studies because samples are smaller than the
6
population from which they are drawn. Measurement error refers to the effect of unreliable
measures on statistical relationships; it reduces the size of the relationship that can be found
(Hunter & Schmidt, 1990). When sampling and measurement errors are present, a study’s
findings can be entirely artifactual (Hunter & Schmidt, 1990). That is, the results might be driven
by design characteristics and might not capture the true relationship of interest.
Vote counting significance tests can actually create more problems than they solve. For
illustration purposes, Hunter and Schmidt (1990) generate Monte Carlo data wherein the true
effect size is known to be .33. They then draw 30 random samples that contained an average of
40 observations and found effect sizes ranging from -.10 to .56. Although the true effect size is
.33, the correlations were statistically significant only 19 out of 30 times. In other words, the
conclusions were wrong 37% of the time. The reason is that significance tests typically hold
Type I errors to .05 (i.e., alpha), but Type II error, which depends on the sample size and the size
of the population effect, is generally not emphasized (Cohen, 1969; Hunter & Schmidt, 1990).
When the null hypothesis is not true, error rates can be quite high. Such error rates have
been estimated at around 50% in the personnel selection literature (see Hunter & Schmidt, 1990).
Thus, reviewing the literature by counting the number of significance tests “leads to terrible
errors” because such counts often sum Type II errors (Hunter & Schmidt, 1990: 31). Moreover,
most reviews conclude that more research is needed to resolve seemingly contradictory findings
even though the apparent conflict is likely due to vote counting significance tests (Hunter &
Schmidt, 1990). This appears to be the case in the TCT literature. David and Han’s (2004)
review of significance tests, for example, found mixed evidence for all of TCT’s key predictions.
Meta-analysis can resolve several problems in qualitative reviews. Because of this,
researchers often use meta-analysis to resolve ambiguity surrounding relationships when a large
number of empirical findings accumulate (e.g., Combs & Ketchen, 2003; Dalton, Daily,
Ellstrand, & Johnson, 1998; Palich, Cardinal, & Miller, 2000). First, meta-analytic techniques
extend knowledge garnered from primary studies by allowing researchers to quantitatively
aggregate prior empirical findings (Hunter & Schmidt, 1990). Meta-analysis can thus resolve the
seemingly contradictory results shown in TCT research by estimating the effect size between
transaction attributes and governance choice. Stated differently, meta-analytic techniques make it
7
possible to explain how much TCT’s core transaction attributes affect whether managers choose
to use market, hierarchical, or hybrid governance. Moreover, determining the actual size of the
relationship is an important outcome of interest because it allows researchers to state how well a
theory predicts a specific phenomenon (Eden, 2002).
Second, meta-analysis is more accurate than vote count procedures because it accounts
for methodological problems, such as sampling and measurement error (Hunter & Schmidt,
1990). Moreover, by aggregating findings, meta-analytic techniques increase the sample size,
which reduces error. According to the central limit theorem, the size of errors shrinks as the
sample size grows larger and it disappears entirely when the population is used.
Third, meta-analysis enables researchers to detect potential moderating influences that
impact relationships of interest (Aguinis & Pierce, 1998). Primary studies cannot account for
potential moderators that are present in the study’s design, such as the country or industry in
which the sample was drawn. However, meta-analytic researchers can test whether effect sizes
found are homogenous across studies. If effect sizes vary, moderators might be present. Only
meta-analysis allows researchers to test whether variance in effect sizes is due to study-specific
moderators.
Because of the large number of empirical studies and the capabilities of meta-analysis,
meta-analysis seems to be the best approach to empirically assess the TCT literature. Thus, this
study will aggregate empirical findings related to TCT’s core transaction attributes, including
asset specificity, uncertainty, and frequency, and how these attributes relate to governance
choice. This study will also investigate how important moderating variables might influence the
relationship between TCT’s core transaction attributes and governance choice.
Significance of the Study
This study’s overarching objective is to increase our understanding of governance choice
by assessing TCT’s empirical validity. Extant literature reviews assess relationships specified by
TCT, however, the large number of contradictory studies, the methodological problems inherent
in prior attempts to review extant research, and recent theoretical developments indicate that a
more systematic study is needed. Thus, the present study has three overarching objectives. The
first objective is to estimate an effect size by systematically assessing TCT’s core predictions
8
(e.g. asset specificity, uncertainty, and frequency are positively related to hierarchical
governance) using meta-analysis. By aggregating the literature’s empirical tests, this assessment
helps resolve the numerous contradictory findings shown in qualitative reviews, determine
TCT’s empirical validity, and improve our understanding of how managers make firm
governance decisions.
The second objective is to identify and test moderating influences expressed in prior
theory development that impact the relationships described by Williamson (1985), but that have
been given scant empirical attention. For example, does uncertainty have a positive linear
relationship with hierarchical governance or do asset specific investments moderate this
relationship? Examining such moderating effects are important because they provide different
interpretations of the same phenomena (Hunter & Schmidt, 1990). Thus, although there might
not be a positive linear relationship between uncertainty and hierarchical governance, this
relationship might exist if asset specific investments are present. Therefore, potential moderating
influences are examined.
The third objective is to develop new theory suggesting how other factors might
moderate TCT’s predicted relationships. Dyer’s (1997) study, for example, revealed that asset
specific investments between exchange partners reduce rather than increase transaction costs.
But his study relied heavily on information from Japanese automotive firms. The Japanese
culture differs significantly from the US culture (Sobek, Liker, & Ward, 1998) and these cultural
differences might impact how governance decisions are made. Thus, other factors that have not
been explicitly examined might provide boundary conditions for TCT. These are also assessed.
Based on these objectives, this study seeks to contribute to the growing body of literature
concerned with managers’ governance decisions. By providing more accurate estimates of the
size of the relationship between transaction attributes and governance choice as well as
examining potential moderating influences impacting these relationships, this study takes a step
towards reconciling contradictory findings and theory and helps researchers better understand
how governance decisions are made.
9
Summary of Remaining Chapters
To accomplish these objectives, this dissertation is structured in the following way. The
second chapter builds on chapter one and contains a literature review of extant TCT research,
including relevant articles from organization theory, strategic management, economics, and
marketing. This chapter outlines the relationships of interest and the quantitative evidence
relating TCT’s main transaction attributes to governance choice. Moreover, the chapter takes
stock of potential moderating variables that impact the core relationships asserted by Williamson
(1985, 1991) After identifying potential moderators, the third chapter outlines a proposed model
and specifies testable hypotheses. These hypotheses will concern TCT’s main attributes, asset
specificity, uncertainty, and frequency, and governance choice. In addition, this chapter develops
specific moderator hypotheses. The fourth chapter outlines the study’s method, including the
sampling procedure, data coding, and meta-analytic technique. The fifth chapter presents the
quantitative findings derived from the analysis. The sixth and final chapter discusses the
findings, conclusions, and theoretical implications of the study, and includes suggestions for
potentially fruitful avenues of further inquiry.
10
CHAPTER 2: LITERATURE REVIEW
This chapter provides a more detailed review of TCT. First, it outlines Williamson’s
(1975) assertions that transactions are the basic unit of analysis, and that adaptation is a central
problem of economic organization. Second, it is shown how markets, hierarchies, and hybrids
differ in their adaptation abilities. Third, TCT’s core behavioral assumptions – bounded
rationality and opportunism - are linked to exchange hazards, transaction costs, and
environmental adaptation problems. Fourth, a review of asset specificity, uncertainty, and
frequency, and their impact on the effectiveness of market, hierarchical, and hybrid governance
is offered. This section examines how these three transaction attributes are expected to impact
exchange hazards and transaction costs in each governance mode, and reviews how
organizations use different governance mechanisms to adapt and effectively manage
transactions. Fifth, some factors that appear to moderate the relationships described in
Williamson’s (1985, 1991, 1999) work are outlined. The chapter concludes with a summary.
Several foundational works shaped Williamson’s (1975) conception of TCT. These
works, particularly those that make significant theoretical contributions as well as those that
make influential empirical contributions, are summarized chronologically in Table 1 below.
Applying Commons (1934) belief, transactions are considered the basic unit of analysis in TCT
(Williamson, 1975). Transactions are goods or service transfers across technologically separable
workgroups in which one stage of economic activity ends and another begins. TCT’s central
concern is how firms govern transactions, and it outlines markets, hierarchies, and hybrids as
alternative governance modes, each of which is capable of managing transactions (Williamson,
1991).
Figure 1 depicts TCT’s theoretical logic and the main factors influencing governance
choice. This figure illustrates that transaction attributes are the starting point for governance
choice. Because of a transaction’s attributes, exchange hazards arising from bounded rationality
and the threat of opportunism create transaction costs, or frictions in exchange. These costs
impact governance choice, or the mode of governance chosen. Although TCT postulates several
factors that impact governance choice, the majority of empirical research focuses on and
11
TABLE 1 – Summaries of Key TCT Foundational Works and Major Accomplishments
Author(s)
Llewellyn, 1931
Barnard, 1938
Commons, 1934
Coase, 1937
Simon, 1945
Hayek, 1945
Chandler, 1963
Knight, 1965
Macneil, 1974
Arrow, 1969
Alchian &
Demsetz, 1972
Klein, Crawford,
& Alchian, 1978
Monteverde &
Teece, 1982
Anderson &
Schmittlein, 1984
Masten, 1984
Walker & Weber,
1984
Joskow, 1985
Jones, 1987
Heide & John,
1988
Dyer, 1996, 1997
Key Contribution to TCT
Disputes can be resolved via courts or other means, such as mediation.
Firms can coordinate economic activity. Authority relations, such as fiat, can be used to improve
internal coordination. Formal controls should be used as needed.
Organizations must cope with conflict, mutual dependence on others, and order. Proposed that
transactions are the central unit of analysis related with these issues.
Describes firms and markets as alternative means to coordinate economic activity. Questions why
firm exist if markets are optimally efficient. Asserts that firms exist because of market transaction
costs, but that there is also a limit to firms.
Economic actors are boundedly rational. That is, people intend to act rationally, but are limited in
their abilities to do so. Thus, all contracts are incomplete.
Asserts that society’s core economic problem is adapting to changes. Two key implications
emerge. First, organizations must contend with environmental uncertainty. The second, which is
related, is that organizations must adapt to their environments to survive and prosper.
Firm structure impacts performance.
Economic actors may act in a predatory fashion. Provided conceptual roots for opportunism and
its subsequent inclusion in TCT. Posits people might not always act honestly. Also suggests that
there diminishing returns to management and sheds light into the importance of incentives.
When contractual disputes arise, they can be costly to resolve. Contracting can be categorized as
1) classical market contracts (standard form), 2) relational contracts (non-standard form), and 3)
neoclassical classical (internal transfers). Each type of contract uses a different dispute resolution
mechanism. Asserts that when disputes arise, they can be difficult to resolve.
Recognized that there are transaction costs to running the economic system, which are distinct
from production costs. Emphasizes that firms and market are alternative means for coordinating
economic activity.
Some tasks cannot be separated, making individual performance difficult to measure. Contributed
to concept of behavioral uncertainty.
Asset specific investments impact governance choice. Through the GM and Fisher Auto Body
example, they showed that transactors can act opportunistically, which can increase transaction
costs and cause adaptation problems in market exchange. This paper facilitated the explicit
inclusion of asset specificity into TCT.
Present first empirical evidence showing how asset specific investments (e.g., engineering knowhow) influence backward integration decisions.
Show that human assets (i.e., salespersons’ knowledge) impact forward integration decisions.
Indicates that performance measurement ability affects such decisions.
Asset specific and uncertain transactions are positively related to hierarchical governance.
Environmental uncertainty leads to hierarchy. Outline different types of uncertainty.
Demonstrates that site specificity influences vertical integration decisions. Study also reveals that
when hierarchy is not used in conjunction with site specific assets, safeguards such as long-term
contract with pricing provisions, are often used to protect firms against the threat of opportunism.
Environmental and behavioral uncertainty influence how transactions are governed.
Transactors use offsetting investments to manage exchange. Specifically, transactors with more
specific assets invest in offsetting investments to bond themselves more closely, and safeguard
assets.
Asserts that asset specific investments lower, rather than increase, transaction costs in hybrid
exchange.
12
measures transaction attributes and their impact on governance choice (e.g., David & Han,
2004). Other constructs, such as exchange hazards or transaction costs, are usually implied, but
rarely measured (Boerner & Macher, 2002). Thus, transaction attributes and governance choice
are the main relationships established in the literature and accordingly, these two boxes are
highlighted in Figure 1 and are the focus of this study. As the figure also shows, once a firm
chooses a governance mode, this inevitably shapes the firm’s adaptive abilities. A key
implication is that adaptation is the result of several factors, each of which impacts a firm’s
adaptation, and hence, performance, in a meaningful way. Because of this, the discussion begins
by examining performance, and then each of the other factors is discussed.
Transaction
Attribute
Exchange
Hazards
Transaction
Costs
Degree of
Integration
- Asset Spec.
- Uncertainty
- Frequency
- Opportunism
- Bounded
Rationality
- Drafting
- Negotiating
- Renegotiating
- Market
- Hybrid
- Hierarchy
Adaptive
Abilities
Performance
FIGURE 1 – TCT’s Theoretical Logic
PERFORMANCE
Strategy scholars have long understood the importance of firm governance decisions
(e.g., Chandler, 1962; Jones, 1987; Walker & Weber, 1984). Indeed, choosing the appropriate
degree of integration has significant performance implications (Mahoney, 1992; Poppo &
Zenger, 1998). TCT sheds light into firm performance by proposing the notion of discriminating
alignment and efficient boundaries (Ouchi, 1980; Williamson, 1981). Specifically, by aligning
transactions with the appropriate degree of integration, firms create efficient boundaries through
lower transaction costs. Moreover, creating efficient boundaries improves a firm’s adaptive
abilities by allowing firms to leverage markets and hybrids when appropriate.
A key implication, therefore, is that drawing efficient boundaries enhances firm
performance through lower transaction costs and improved adaptability (Williamson, 1981).
Transaction costs are lowered because firms ‘match’ the transaction to the appropriate degree of
13
integration. When transactions are supported by increased asset specific investments, for
example, these investments are worth less outside of the focal transaction (Klein et al., 1978).
Therefore, to protect such investments and lower bargaining expenses related to these
transactions, firms should choose hierarchical governance. Hierarchical governance can reduce
such expenses, and increase a firm’s adaptive abilities. Following this logic, firms improve
performance (e.g., become more efficient) when transactions are matched to the appropriate
degree of integration in a discriminating way (Williamson, 1981).
ADAPTIVE ABILITIES
Following Barnard (1938) and Hayek (1945), a major focus of TCT is the comparative
costs of adaptation under alternative governance modes (Williamson, 1975, 1999). Recall that
market governance is characterized by “arms-length” relationships between buyers and sellers;
hierarchical governance is when buyers and sellers transact within the same firm; and hybrid
arrangements take on characteristics of both markets and hierarchies but the buyers and sellers
remain distinct. The advantages of one mode over another depend on its adaptation ability, which
refers to its capabilities to adjust to environmental disturbances.
Environmental disturbances can be categorized as inconsequential, consequential, and
highly consequential (Williamson, 1991). Inconsequential disturbances have little impact on the
firm, and thus, firms simply absorb the disturbance. For example, if the cost of copier paper
increases slightly, and is a small fraction of the focal firm’s operating costs, it would be
inefficient and unnecessary for the firm to seek alternative suppliers as the search costs would
likely exceed the benefits of finding a new paper supplier. Moreover, because paper is a
commodity, other suppliers should offer similar prices. Consequential disturbances, on the other
hand, have a larger impact on the firm, and refer to conditions that can have an adverse impact.
In an example used by Williamson (1991), he suggests that consequential disturbances are
situations such as when market prices for a supplier’s products increase by over ten percent
while previously negotiated contracts require the supplier sell at a lower price. The supplier
would face a hardship by selling product significantly below market price.1 Because of situations
1
This discussion describes consequential disturbances and is not intended to dispute the use of agreements that guard firms
against large price fluctuations. Simkins, Carter, & Rogers (2004) review why airlines use “hedging” agreements.
14
such as this, firms need mechanisms to adapt. Highly consequential disturbances are even more
severe, and can impede a firm’s adaptation ability and even place its survival at stake
(Williamson, 1991). Suppose that market prices for the supplier’s products increased by twenty
percent and that the negotiated price no longer covered costs. In this case, the supplier would be
selling at a loss, which could place its survival at stake. Thus, firms need to have appropriate
contractual mechanisms in place to adapt efficiently, and these needs are greater when
disturbances become more consequential.
In addition to the degree of environmental disturbance, a firm’s adaptation ability is also
shaped by the type of adaptation needed (Williamson, 1985, 1991). Autonomous environmental
adaptations require little or no coordination between transactors. Instead, the price system
efficiently governs the transaction as transactors can take correct actions based upon supply and
demand information (Hayek, 1945). Alternatively, coordinated adaptations require more
complex coordination between transactors. Coordinated adaptations are transactions that pass
through stages that are more dependent on one another, thereby presenting greater adaptation
problems (Williamson, 1985). In such instances, the price mechanism is inefficient as supply and
demand information might not enable close coordination (Malmgren, 1961). Despite the
availability of supply and demand information, transactors may read and react to information
differently, which can lead to less efficient coordination (Williamson, 1991). Because of this,
greater adaptation problems result because a firm might not be able to easily adjust to changing
environmental circumstances (Hayek, 1945). However, firms can choose governance modes that
are capable of responding to differing degrees of environmental disturbances that provide the
requisite adaptation mechanisms needed to survive and prosper.
DEGREE OF INTEGRATION
The degree of integration refers to the choice between market, hybrid, or hierarchical
modes of governance. Figure 2 shows that governance modes differ in degrees of integration.
Specifically, market and hierarchical governance are polar governance modes, with hybrids,
sometimes referred to as relational governance modes, situated in between (Williamson, 1985).
Markets refer to ‘arms length’ exchanges and represent the lowest degree of integration.
Hierarchies refer to situations in which firms make internal exchanges and represent the highest
15
degree of integration. Yet, as indicated in the figure, many transactions are governed by hybrids,
such as licenses or joint ventures. Hennart (1993) asserts that most transactions are governed by
hybrids. In short, markets, hybrids, and hierarchies are alternative transaction governance modes,
where markets and hierarchies represent the lowest and highest degrees of integration, and
hybrids, although different, are situated in between.
Low
Market
Moderate
Hybrids
High
Hierarchy
FIGURE 2 – Degree of Integration
According to TCT, choosing the most efficient degree of integration is contingent on
governance mode differences and the capabilities that each mode possesses to economize on
transaction costs. These differing capabilities also influence a firm’s ability to adapt to
environmental disturbances (Williamson, 1991). These differing capabilities therefore influence
both transaction costs and a firm’s adaptive abilities. However, a key determinant of which
governance mode improves adaptive abilities (i.e., flexibility) is severity of environmental
disturbances and the type of response required. Although technological advancements have
reshaped the way markets, hierarchies, and hybrids adapt to environmental disturbances (Zenger
& Hesterly, 1997), these governance modes differ in their ability to manage transactions and
adapt in three specific ways – formal controls, dispute resolution mechanisms, and incentive
intensity.”
First, hierarchies possess more powerful ways to cope with exchange hazards through
formal control mechanisms (Williamson, 1975, 1991). Monitoring capabilities, such as internal
audits, allow hierarchies to scrutinize transactions more completely than hybrid or market
governance modes, and thereby to detect opportunism more quickly. These capabilities can help
a firm’s decision-makers access more relevant information and thus reduce uncertainty
surrounding transactions. Another formal control mechanism is a hierarchy’s internal reward
structure. Hierarchies can reward employees through compensation, promotions, and stock
16
options, which can also reduce opportunistic behaviors by rewarding individuals based on their
total contribution to the firm. Hybrids have less formal control mechanisms than hierarchies, but
more than markets. Hybrid exchange, for example, is often facilitated by safeguards or
information disclosure requirements (Williamson, 1991). Safeguards include investing in
relationship specific assets, such as mutual trust, financial and specialized investment hostages,
full payment upon order cancellation clauses, and reciprocal buying arrangements (Dyer, 1997;
Dyer & Singh, 1998; Williamson, 1985).2 These types of safeguards attenuate some problems
associated with market exchange, are more powerful control mechanisms than those in arms
length market exchange, and provide more efficient adaptation over the long run (Dyer, 1997.)
Whereas such arrangements reduce flexibility compared to markets, they provide greater
flexibility than markets. In sum, hybrids have more powerful control mechanisms than markets,
but less than hierarchies, to control opportunism that can arise in transactions. Thus, as
environmental disturbances increase and require more coordinated adaptation, transactions
governed by hierarchies and to a lesser extent, hybrids, reduce transaction costs and provide
firms with greater flexibility.
Second, hierarchies possess more efficient dispute resolution mechanisms than hybrids or
markets (Williamson, 1985). Although escalation and de-escalation clauses can be specified in
contracts, disputes will arise, regardless of the governance mode used to govern transactions.
Unlike market contracts that use the court system as a last resort (Llewellyn, 1931), hierarchies
resolve disputes privately (Williamson, 1975). When environments change, disputes may arise in
market contracting and thereby restrict a firm’s ability to adapt quickly. Adjusting market
contracts can be time consuming and increase costs associated with conflict resolution (e.g.,
haggling or seeking court assistance) (Llewellyn, 1931; Williamson, 1985, 1991). In contrast,
hierarchies can resolve disputes and facilitate adaptation more efficiently via fiat when
disturbances occur (Macaulay, 1963). Fiat refers to a managerial intervention wherein managers
2
TCT highlights the role of safeguards as an effective means of coping with the threat of opportunism. However,
some safeguards highlighted in the literature (e.g., reciprocal buying) are inconsistent with the business-to-business
buying literature. Perhaps more importantly, safeguards such as cancellation clauses and reciprocal buying
arrangements might be impractical. The former requires mutual assent and the latter can be considered illegal.
Moreover, such safeguards might actually provide firms with less flexibility and hurt performance because they do
not allow firms to promptly switch governance modes when appropriate. Further, such safeguards might weaken
market incentives.
17
use formal authority to settle internal disputes. Unlike markets, hybrids typically have dispute
resolution mechanisms in place that are less costly than the court system (Williamson, 1985).
These mechanisms are geared towards resolving disputes efficiently and equitably. If immediate
resolution between transactors is not possible, these mechanisms typically require the parties to
use a neutral party (e.g., arbitrator or mediator) to resolve the dispute. Based on this, hierarchies,
followed by hybrids, are better equipped than markets to resolve disputes when unanticipated
environmental disturbances occur.
Third, hierarchies have low-powered incentives relative to markets (Williamson, 1985,
1991). In markets, an owner’s compensation increases in proportion to their firm’s performance.
When compensation is contingent on how successful a business performs, economic actors are
generally willing to expend greater efforts (Williamson, 1991). Once those transactions shift to
hierarchical governance, however, employees share less in the rewards and therefore have fewer
incentives. More specifically, compensation for employees is often comprised of salary plus
bonuses and possible stock options, where at least some compensation is guaranteed. Because of
this, hierarchical governance lowers performance incentives below optimal levels. Although
hybrids represent closer exchange relations than markets, they preserve greater incentive
intensity than hierarchies because the two firms remain distinct. In sum, hybrids are suggested to
have less powerful incentives than markets, but more powerful than hierarchies.
The foregoing discussion reveals that because of formal control, dispute resolution, and
incentive intensity differences between markets, hierarchies, and hybrids, each governance mode
varies in its ability to manage transactions and adapt to environmental disturbances. A central
premise of TCT, however, is that governance choice, or the degree of integration, is contingent
on the type of adaptation required (Williamson, 1985, 1991). As noted above, autonomous
environmental adaptations use the price mechanism to coordinate transactions because such
transactions have little need for formal controls or dispute resolution mechanisms. Moreover,
markets preserve high-powered incentives and therefore can efficiently govern transactions that
require autonomous adaptation (Williamson, 1991). In contrast, more coordinated adaptations,
which require complex responses, rely more on formal controls and dispute resolution
mechanisms. In such cases, market contracts inhibit a firm’s adaptation ability. Hybrids and
18
hierarchies, on the other hand, have greater controls and more effective dispute resolution
mechanisms in place. Therefore, when coordinated responses are required, the need for
coordination, which is enhanced through increased controls and more efficient dispute resolution
mechanisms, can outweigh the disadvantages of lower incentives. Thus, Williamson (1985)
posits that autonomous adaptations are best governed by markets, but as adaptations require
more complex responses, hybrids and hierarchies are better equipped to govern such
transactions, reduce transaction costs, and improve a firm’s adaptive abilities.
TRANSACTION COSTS
Williamson (1985) asserts that, holding the nature (e.g., quality and delivery) of goods
constant, it is essentially the sum of production costs and transaction costs that determine
governance choice. But due to the complexity of the phenomenon under investigation, TCT also
holds production costs constant. That is, production costs are assumed to be homogeneous across
different governance modes. This assumption allows TCT to focus on transaction costs, how
they differ across governance modes, and how these differences shape governance choice
decisions, or the degree of integration chosen.
Transaction costs occur both ex ante and ex post and can broadly be conceived of as
exchange frictions that cause significant market contracting problems (Arrow, 1969). Ex ante
transaction costs result from searching for contracting partners, as well as from crafting,
negotiating, and safeguarding agreements (Williamson, 1985). These costs are incurred to protect
the firm’s interests should environmental disturbances occur. Ex post transaction costs include
the setup and operating expenses of dispute resolution mechanisms, haggling expenses resulting
from joint efforts to realign contracts when conditions change, and ongoing bonding expenses
that secure commitments between the parties (Williamson, 1985). The implications are that ex
post transaction costs can be costly and the underlying disputes can be time consuming. Both of
these factors can inhibit a firm’s adaptation capability.
In sum, transaction costs create adaptation problems when transactions are not matched
to the appropriate governance mode (Williamson, 1985). Thus, Williamson (1991) contends that
transactions, which differ in the attributes (e.g., uncertainty or asset specificity), must be matched
to the most efficient governance mode, which differ in their costs and capabilities in protecting
19
firms from exchange hazards. Accordingly, because transaction costs impact a firm’s adaptation
capability, they will shape the decision to use market, hierarchical, or hybrid governance.
EXCHANGE HAZARDS
Exchange hazards were previously described as situations where one transactor had the
ability to exploit unwritten or unenforceable parts of the contract (Klein, 1980). In other words,
exchange hazards are created when economic actors can cheat, renege, or fail to modify
agreements to reflect changing economic realities. According to TCT, these hazards arise
because economic actors are boundedly rational and opportunistic (Williamson, 1975).
Bounded Rationality
The term bounded rationality was coined by Herbert Simon (1957). In contrast to
neoclassical economic theory, which assumes that human beings are completely rational, Simon
(1957: xxiv) argued that human beings are “intendedly rational, but only limitedly so.” This
suggests that human beings, as economic actors, intend to be rational by considering all relevant
problems and solutions, but are limited in their capacity to do so. Specifically, economic actors
have a limited capacity to predict and solve complex problems (Williamson, 1975). When
complex organizational problems arise, the human mind’s capacity is relatively small compared
to the magnitude of such problems (Feldman & Kanter, 1965). Thus, the human mind is limited
in its ability to predict problems in advance, leading boundedly rational economic actors to
simplify choices and make compromises. Based on these notions, Williamson (1975, 1985)
contends that all complex decisions are less than completely rational, which has important
implications for the study of economic organization.
The bounded rationality assumption presents human beings as limited in their decisionmaking capacity. These limitations stem from an individual’s computational and language
capacity (Williamson, 1985, 1991). Computational capacity refers to an economic actor’s ability
to create decision trees that include all possible problems and solutions (Feldman & Kanter,
1965). Williamson (1975) uses a chess analogy to illustrate constrained computational capacity.
In chess, it is difficult to predict an opponent’s actions due to the number of board spaces, how
each piece moves, and the inability to precisely predict their strategy. Language capacity refers
to a person’s ability to efficiently articulate economic problems when exchange partners have no
20
prior experience or knowledge (Hart, 1995). When complex problems require coordinated
responses, economic actors may be unable to easily articulate the problems and solutions, as they
may be more tacit in nature (Polanyi, 1962). Given computational and language limitations,
economic actors cannot account for all possible problems and contingencies (i.e.,
macroeconomic changes) in market or hybrid contractual arrangements. Because of this, all
contracts are deemed to be unavoidably incomplete (Grossman & Hart, 1986; Williamson,
1975). In sum, bounded rationality makes it impossible to specify all contingencies in contractual
agreements, leading exchange partners to write incomplete contracts that could lead to
subsequent adaptability problems.
Opportunism
According to TCT, incomplete contracts resulting from bounded rationality are not
problematic unless the threat of opportunism is present. Only when the assumption of bounded
rationality is joined with opportunism do markets have difficulty managing transactions,
resulting in the existence of organizations (Williamson, 1993). While individuals do not act
opportunistically all the time (Davis, Schoorman, & Donaldson, 1997; Hendry, 2002; Perrow,
1986) and are constrained by reputational effects in markets (Hill, 1990), human opportunism is
a pervasive enough condition that it needs to be protected against (Knight, 1965). The concept of
human opportunism depicts individuals as not only self-serving, but describes them as selfserving with guile (Goffman, 1969; Williamson, 1975). This implies that some individuals make
false and empty promises, such as not honoring written or unwritten agreements, to achieve their
goals. Because it is difficult to predict ex ante who will act opportunistically, firms would be
foolish not to have control mechanisms in place (Barney & Hansen, 1994). But without bounded
rationality and incomplete contracts, opportunism would not pose exchange hazards as all
possible contingencies and adaptations would be specified in contracts. When unforeseen critical
contingencies arise while a contract is in place, however, some individuals might be prone to
take opportunistic action to achieve their goals (Williamson, 1975, 1985).
Markets and hybrid arrangements have less powerful mechanisms to deal with
opportunism. In contrast, hierarchies can use formal controls and fiat to monitor and control
opportunistic behaviors (Williamson, 1975). Therefore, when a firm is exposed to significant
21
exchange hazards, managers may conduct the transaction inside the firm to avoid high
transaction costs (e.g., haggling or court costs) and potential adaptation problems. To some
extent, however, transactors can use safeguards or incentives to reduce exchange hazards and
promote adaptation over the long run (Williamson, 1991). But safeguards and incentives are not
always effective. This led Williamson (1991) to suggest that markets and hybrids are illequipped to manage transactions requiring coordinated adaptation to complex problems.
When there are few exchange hazards and transaction costs are low, market contracts will
be optimally efficient (Williamson, 1975). However, when transaction costs are perceived as
high, hierarchical governance is more efficient as it allows the firm to adjust to changing
conditions with lower transaction costs, thereby improving its adaptation ability (Williamson,
1975). Therefore, the way firms govern transactions should account for exchange hazards,
transaction costs, and adaptation capabilities of each governance mode (Williamson, 1991).
However, it is the transaction’s attributes, some of which are more subject to bounded rationality
constraints and opportunism than others, that provide the starting point for decisions regarding
how a transaction will be governed.
TRANSACTION ATTRIBUTES AND THE DEGREE OF INTEGRATION
As described previously, there are three core transaction attributes that drive transaction
costs, thereby impacting a governance mode’s ability to adapt. Williamson (1975, 1979, 1985)
suggests that transactions principally differ in terms of 1) asset specificity, 2) uncertainty, and 3)
frequency, and he predicts a positive relationship between these transaction attributes and more
hierarchical governance modes. The predicted relationships are depicted in Table 2 below.
TABLE 2 – Transaction Attributes and Governance Choice
Predicted Governance Mode When Attribute Level is:
Transaction Attribute
Low
Intermediate
High
Asset Specificity
Market
Hybrid
Hierarchy
Uncertainty
Market
Hybrid
Hierarchy
Frequency
Market
Market/Hybrid
Hierarchy
22
Asset Specificity
Asset specificity is considered TCT’s “big locomotive”, and main determinant of
governance choice (Williamson, 1985: 56). Asset specificity refers to the level of unique
investment required to support a transaction (Klein, Crawford, & Alchian, 1978; Williamson,
1979). Asset specific investments are typically enduring and make transacting more efficient.
Highly specific investments are unique to a transaction and therefore, difficult to redeploy
without reducing the asset’s value outside the focal relationship (Williamson, 1991). Stated
differently, the more specific the investment, the greater the difference in value between its firstbest and second-best (i.e., outside the focal transaction) use (Mahoney, 1992). Because of this
reduction in value from the first-best to second-best use, highly specific investments create the
possibility for exchange hazards and subsequent adaptation problems.
There are six main types of asset specific investments – site, physical, human, dedicated,
brand name capital, and temporal (Williamson, 1979, 1981, 1985, 1996). First, site asset
specificity refers to assets that permit products or services to progress through successive stages
in close geographic proximity. When two transaction stages are close in geographic proximity, it
lowers transportation and inventory costs (e.g., Cook, 1997; Joskow, 1985). Transportation costs
are lower when the product travels shorter distances between successive stages. Inventory costs
are lower because inventory is purchased and consumed as needed, reducing inventory on hand
and the space required to store it, both of which reduce costs. Second, physical asset specificity
relates to specialized tooling and dies required to support a transaction. The increased use of
physical assets makes production more efficient because these specialized investments create
lower costs of production (e.g., Klein et al., 1978; Poppo, 1995). Third, human asset specificity
refers to the skills of people that are specific to a transaction. These specific skills can take
substantial time to acquire, and are thus, hard to replace in markets. Therefore, when specific
skills support a transaction, the use of market contracts or hybrids can create exchange hazards
and adaptation problems, which can be mitigated using hierarchical governance (e.g., Anderson,
1988; Coles & Hesterly, 1998). Fourth, dedicated assets relate to investments in generalized
production capacity or assets that are dedicated to serving a specific customer. These investments
could be manufacturing facilities or other assets that make transactions more efficient with one
23
customer, but they might not be easily redeployed (e.g., Globerman & Schwindt, 1986; Maltz,
1993). Fifth, brand name capital is an intangible asset that firms accumulate over time. When a
firm’s brand name is important, market contracting can expose brands to exchange hazards as
other economic actors may seek to benefit at the expense of the brand (e.g., Affuso, 2002;
Minkler & Park, 1994; Regan, 1997). Williamson (1985) describes how this can occur in
franchising relationships. Franchisees license brand names (e.g., McDonalds) from franchisors.
One hazard occurs when franchisees are located in places such as along major highways where
most consumers are not repeat buyers. Such franchisees might lower quality to improve their
profitability at the expense of the franchisors’ brand (Caves & Murphy, 1976). Sixth, temporal
asset specificity relates to coordinated transactions that are interdependent and require timely
responses. When transactions are interdependent and quick responses are needed, firms may
choose hierarchy over markets or hybrids to improve response times (e.g., Butler & Carney,
1983; Murris, Scheffman, & Spiller, 1992). Hierarchical governance is suggested to allow for
closer coordination through formal controls and fiat, which can increase response times
(Williamson, 1996).
As noted above, asset specific investments are by definition non-redeployable and are
therefore less valuable outside the focal transaction. This would be less problematic if not for
bounded rationality and the threat of opportunistic economic actors. In the presence of bounded
rationality, transactors write incomplete contracts and do not know ex ante if trade partners will
act opportunistically by refusing to honor, or by taking advantage of, written and unwritten
agreements (Williamson, 1985). Unlike transactions supported by non-specific assets,
transactions involving asset specific investments can cause financial harm to one party when the
other acts opportunistically. This stems from the difference between a specific assets first-best
and second-best use.
The case of Fisher Auto Body and General Motors (GM) is one well-known example
showing how asset specific investments increase exchange hazards, thereby increasing a firm’s
propensity to choose hierarchical governance over market contracting (Klein et al., 1978). In the
early 1900s, GM contracted with Fisher to manufacture closed bodies. In this exclusive dealing
long-term contract, GM agreed to pay the same markup over costs for the contract’s duration.
24
After Fisher made the initial asset specific investment during the contract’s infancy, GM’s sales
volumes increased substantially. The volume increases were an unanticipated environmental
disturbance. However, because of the increased volumes and the markup GM had negotiated, the
agreement provided Fisher with greater than expected sales revenues and profits and by
extension, an opportunity to take advantage of GM. When GM approached Fisher to renegotiate
the contract terms and conditions, Fisher declined. Fisher possessed asset specific investments
(e.g., tooling and dies for closed body designs) that GM needed to produce automobiles, which
limited GM’s adaptation ability. Dissatisfied with Fisher’s response, GM slowly began acquiring
Fisher’s stock in an effort to shift from market contracting to hierarchical governance. A short
time later, GM acquired majority ownership and eventually integrated Fisher into its operations.
TCT’s assumptions illustrate why GM experienced problems with Fisher. First, GM’s
boundedly rational purchasing representatives negotiated the agreement, and their computational
capacity limited their ability to specify ex ante how the agreement should change if adaptations
were needed. When a buyer’s purchase volumes increase, they typically receive volume
discounts from suppliers, thereby reducing markup on a per unit basis. However, GM’s
purchasing representatives mistakenly failed to specify this in the contract. Therefore, when
Fisher took advantage of GM’s failure to include and renegotiate certain contractual provisions,
Fisher’s managers clearly illustrated TCT’s opportunism assumption.
Another well-known study demonstrating how asset specificity impacts the degree of
integration is Joskow’s (1985) analysis of utility firms that owned power plants located near coal
mines. Specifically, he analyzed whether utility firms would choose market, hierarchical, or
hybrid governance when they built power plants near coal mines. He referred to power plants
located in close proximity to mines as mine mouths. Mine-mouth plants involve significant asset
specific investments, including site specific assets (e.g., locational advantages), physical specific
assets (e.g., specialized equipment), and dedicated assets (e.g., electric power facilities).
Joskow’s (1985) analysis illustrates how asset specificity creates exchange hazards. If a coal
mine-mouth site (firm A) offered incentives (e.g., volume discounts) to a utility (firm B) for
locating (e.g., build asset specific manufacturing facility) adjacent to the mine, a fundamental
transformation would occur once the investment was made. Before the investment, perhaps firm
25
A sold its coal on the market and thus wanted to ‘lock-in’ a customer to reduce demand and price
variability. Once firm B made the investment, firm A might no longer be just interested in
reducing their demand variability, but might also be interested in a recognized opportunity to
increase profits. Once firm B experiences lower transportation and inventory costs as a result of
their location, firm A might be tempted to extract higher prices from firm B. These prices could
not exceed what firm B would pay to an alternative source, including transportation costs.
Nevertheless, after firm B builds the manufacturing facility, it becomes vulnerable because it
could lose the expected gains from locating near the mine. Because of this, there is a
fundamental power shift and increased potential for opportunism arising from firm B’s asset
specific investment. To reduce these hazards, TCT adherents suggest that firm B use hierarchical
governance, and thus vertically integrate firm A into their operations. Not surprisingly, Joskow
(1985) found that while 85% of the utility industry’s coal supplies are purchased on open
markets, utility companies own almost all mine-mouth mines.
TCT sheds light on these mine-mouth governance decisions by suggesting that
hierarchies can economize on transaction costs by lowering costs and avoiding opportunism in
two ways. First, close proximity reduces transportation and inventory holding costs. Second,
after utility firms build facilities adjacent to mines, there is a fundamental transformation
wherein a coal supplier increases its bargaining power (Williamson, 1985). If the coal supplier is
an external supplier, it could make false promises and state that it would offer lower prices to the
utility once it locates there, however, after the utility relocates, a supplier could act
opportunistically and raise prices to market levels. Thus, coal suppliers’ bargaining power would
be ‘fundamentally transformed’ once significant asset specific investments were made. To avoid
opportunism and maintain lower transaction costs, utilities more frequently used hierarchical
governance (Joskow, 1985), which is consistent with TCT predictions.
Because of cases such as the automobile and coal industry examples cited above, as well
as a significant amount of empirical research (e.g., Coles & Hesterly, 1998; Dutta & John, 1995;
Jones, 1987; Joskow, 1985; Masten, 1984; Masters & Miles, 2002; Monteverde & Teece, 1982;
Pisano, 1990), asset specificity is considered a key determinant of governance choice. Moreover,
several TCT literature reviews support this notion and conclude that asset specificity drives
26
transaction governance (e.g., Boerner & Macher, 2002; Leiblein, 2003; Rindfleisch & Heide,
1997). David and Han (2004), for example, found that when asset specific investments are
present, firms chose hierarchical governance over markets in 60% of the significance tests
studied. Therefore, asset specific investments appear to be a good predictor of hierarchical
governance.
The foregoing discussion indicates that because of bounded rationality and opportunism,
asset specific investments can create exchange hazards. Because of these hazards, TCT predicts
that firms choose hierarchical governance over market contracting or hybrids to avoid transaction
costs and improve adaptation ability (Williamson, 1981, 1985). In sum, because of exchange
hazards, transaction costs, and adaptability problems caused by asset specific investments, TCT
predicts that asset specific investments increase the likelihood that firms will choose hierarchical
governance over markets or hybrids (Williamson, 1991) and a substantial amount of empirical
evidence appears to support this prediction (e.g., David & Han, 2004; Rindfleisch & Heide,
1997).
Uncertainty
Uncertainty is another key determinant of governance choice (Williamson, 1975). If asset
specificity is considered the “big locomotive,” uncertainty might be best conceptualized as the
big locomotive’s fuel. Knight (1965) made several early contributions to the concept of
uncertainty, suggesting that in its absence markets could manage most transactions without the
existence of firms. Uncertainty ranges from low (e.g., in stable markets) to high (e.g., in fastchanging markets). Broadly speaking, the higher the uncertainty the greater the problems
stemming from bounded rationality and the threat of opportunism. Indeed, uncertainty in market
contracting makes it difficult to specify ex ante critical contingencies that affect adaptation
(Teece, 1986; Williamson, 1975). Because of this, uncertainty activates exchange hazards,
particularly in long-term contracts, and creates adaptation problems (Pisano, 1990).
Early TCT literature failed to differentiate between different types of uncertainty (e.g.,
Williamson, 1975). However, uncertainty is now recognized as a multidimensional construct
(Langlois, 1984; Sutcliffe & Zaheer, 1998). Although labeled throughout the TCT literature in
several ways, the present study considers two main types of uncertainty – environmental and
27
behavioral. These two types of uncertainty appear throughout the strategic management,
organizational theory, economics, and marketing literatures and evidence indicates that each type
impacts hierarchical governance decisions (Boerner & Macher, 2002; David & Han, 2004; Klein
& Shelanski, 1994; Rindfleisch & Heide, 1997; Sutcliffe & Zaheer, 1998). This evidence,
however, suggests that each type of uncertainty shapes governance choice.
Environmental uncertainty refers to unpredictability, such as forecast and demand
uncertainty, how quickly the environment is changing (e.g., technological developments or
obsolescence), consumer preference changes (e.g., fads), and natural events (Balakrishnan &
Wernerfelt, 1986; Bensaou & Venkatraman, 1995; Harrigan, 1985; Heide & John, 1990;
Koopmans, 1957; Schilling & Steensma, 2002; Walker & Weber, 1984). In the presence of
environmental uncertainty, contract writing becomes more complex and boundedly rational
managers necessarily write incomplete market contracts. Not surprisingly, adaptation problems
can surface, particularly with long-term contracts because personnel responsible for writing
contracts cannot conceive of and include all contingencies and appropriate adaptations in
contracts ex ante. Thus, TCT asserts that firms can better adapt to problems stemming from
exogenous sources of uncertainty and lower transaction costs through hierarchical governance
(Williamson, 1985).
Recently the environmental uncertainty construct has been scrutinized and its effect on
hierarchical governance has been called into question (e.g., David & Han, 2004; Klein, Frazier,
& Roth, 1990). Indeed, theoretical developments and empirical evidence from within and outside
the TCT literature (i.e., real options) (Miller & Folta, 2002) suggest that environmental
uncertainty might, in fact, influence hierarchical governance decisions (e.g., Balakrishnan &
Wernerfelt, 1984; Gatignon & Anderson, 1988; Schilling & Steensma, 2002), but not in the way
envisioned by Williamson (1975, 1985).
In contrast to TCT’s predicted relationship between environmental uncertainty and
hierarchical governance, real options theory suggests that in the presence of uncertainty,
hierarchy can limit a firm’s flexibility and adaptability (Folta, 1998). When faced with
unpredictability and rapid environmental change, managers may instead prefer to use market
contracting to avoid, rather than absorb, this uncertainty. Schilling and Steensma (2002), for
28
example, found that firms facing technological uncertainty avoided hierarchical governance (i.e.,
acquisitions) and instead used licensing agreements. Real options logic provides one plausible
explanation for Schilling and Steensma’s findings by indicating that if firms acquired target
firms possessing technology that soon became obsolete, this could limit the acquiring firm’s
future flexibility and thus cause adaptation problems. In this instance, adaptation problems
surface because the acquiring firms would have been locked in to the technology for the long
term. Yet TCT does not predict this relationship. Key differences between TCT and real options
theory are highlighted in Table 3 below.
TABLE 3 – Key Differences Between TCT and Real Options Theory
Key Dimension
Transaction Cost Theory
Assumptions
Bounded Rationality
Opportunism
Focus
Firm Governance Decisions
Role of
Uncertainty
Creates opportunities for
opportunism and thus needs
to be protected against
Protect the firm against
uncertainty through the use
of hierarchy
Implications
Real Options Theory
Economic actors are rational
Contracts are complete, thus
opportunism is not a major
concern
Investment Opportunity
Evaluation
Creates opportunities for
options, which provide
managerial flexibility
Maintain firms flexibility and
adaptability by deferring
uncertain investments through
the use of markets or hybrids
Although based on vote count procedures, David and Han’s (2004) review concludes that
there is little support for TCT’s predicted effect of environmental uncertainty on hierarchical
governance. Moreover, they conclude that hierarchical governance, in the presence of
environmental uncertainty, can increase rather than reduce adaptation problems. Thus, not only
is there a competing explanation for the relationship between environmental uncertainty and
hierarchical governance, but there is also a lack of empirical evidence and conclusions
supporting the contrary. Therefore, the relationship between environmental uncertainty and
governance choice is not well established.
29
Behavioral uncertainty differs from environmental uncertainty and refers to when
performance is difficult to measure (Williamson, 1985). Performance measurement problems
surface when tasks are accomplished jointly by more than one person or when it is difficult to
assess an output’s quality (Alchian & Demsetz, 1972; Anderson, 1985; Ouchi, 1980). When
tasks are accomplished jointly and considered non-separable, shirking problems can arise.
Shirking problems refer to evading one’s assigned duties or reducing effort, which can occur in
non-separable tasks when one person shirks at the expense of others (Alchian & Demsetz, 1972;
Jensen & Meckling, 1976). Alchian and Demsetz (1972) use a manual freight-loading example
to illustrate this. Specifically, they note: “Two men jointly lift cargo into trucks. Solely by
observing the total weight loaded per day, it is impossible to determine each persons marginal
productivity” (Alchian & Demsetz, 1972: 779). In this instance, it is difficult to measure each
loader’s contribution because the output is yielded by a team and therefore is non-separable. If
one loader chooses to reduce output, it would be difficult to measure how much reduced output
is attributed to that person, thereby making the appropriate corrections difficult to implement.
Alternatively, when an output’s quality is difficult to assess, problems can also surface
(Poppo & Zenger, 1998; Williamson, 1985). These problems surface as it is difficult to measure
and accurately reward performance in a timely manner. In such cases, suppliers may sell lower
quality goods or services, but because it is hard to detect quality problems in a timely manner,
the supplier can continue to produce such goods without getting caught (Hennart, 1993). Taken
together, non-separable tasks and quality measurement problems would not be problematic if
economic actors were considered trustworthy. Because of TCT’s opportunism assumption,
however, performance measurement difficulties create shirking and cheating problems that firms
might seek to avoid.
With respect to transactions and performance measurement, market governance is most
effective when performance can be accurately measured and incentives can be linked to
productivity (Alchian & Demsetz, 1972). When performance cannot be accurately measured,
market contracts present opportunism problems because economic actors can fail to disclose
relevant information (e.g., quality measures) in order to profit from a relationship. Although
some transactors will act in a trustworthy fashion, TCT predicts that others will act
30
opportunistically by intentionally misleading exchange partners (Williamson, 1985). Thus, in
market contracts, behavioral uncertainty exposes firms to higher exchange hazards, which can
cause subsequent adaptation problems. Hierarchical governance, on the other hand, provides
stronger controls and dispute-resolution mechanisms that can be used to deter opportunistic
actions, particularly when performance is difficult to measure (Williamson, 1991). Some
evidence indicates that hierarchies supplant market and hybrid governance modes when
performance is difficult to measure (e.g., Anderson & Schmittlein, 1984; Jenson & Rothwell,
1998; Rindfleisch & Heide, 1997).
In summary, behavioral uncertainty stems from an economic actor’s inability to measure
performance, thereby limiting managers’ abilities to specify appropriate adaptations in
contractual agreements and protect the focal firm’s interests. Because hierarchies possess formal
controls and dispute resolution systems designed to protect the firm’s interests from
opportunism, TCT predicts that hierarchical governance reduces problems caused by the inability
to accurately measure performance and opportunism. Thus, TCT predicts that hierarchy is the
preferred governance mode when behavioral uncertainty is present (Williamson, 1985).
Frequency
Transactions can also be described in terms of the frequency in which transactions occur
(Williamson, 1981). Williamson (1985) suggests that transaction frequency can be categorized as
one-time, occasional, or recurring, and argues that frequency levels shape governance choice.
TCT devotes less attention to one-time and occasional transactions because they have a lesser
impact on transaction cost economizing than recurring transactions. Specifically, markets will
handle most one-time and occasional transactions as they do not increase transaction costs or
pose significant adaptation problems (Williamson, 1975). In contrast, recurring transactions are a
key determinant of governance choice because as frequency increases, firms can lower
production and transaction costs. As frequency increases, fixed costs can be amortized over more
transactions, lowering the fixed cost per transaction. Consequently, lower fixed cost per
transaction makes hierarchy more desirable than cases where fixed costs need to be amortized
over a few transactions. Moreover, as transactions become more frequent, firms are also more
likely to invest in specialized equipment (i.e., asset specific investments) to lower the sum of
31
production and transaction costs (Williamson, 1985). Stated differently, as frequency increases,
firms might take actions, such as investing in specialized equipment, directed towards reducing
variable cost per transaction. By investing in specialized equipment, firms lower their production
costs. However, as noted earlier, hierarchical governance is predicted to reduce transaction costs
by lowering exchange hazards posed when asset specific investments (e.g. specialized
equipment) are present (Williamson, 1985). Ceteris paribus, the higher the frequency, the greater
the opportunities for firms to lower fixed and variable costs per transaction, and thus, economize
on transaction costs through hierarchy.
The relationship between transaction frequency and hierarchical governance has received
significantly less empirical attention in the TCT literature than the relationships discussed
previously (David & Han, 2004; Silverman, 2002). Several studies have shown no support (e.g.,
Anderson & Schmittlein, 1984; Fernandez, Arrunada, & Gonzales-Diaz, 2000; Hanna & Maltz,
1998; John & Weitz, 1989; Leiblein & Miller, 2003), whereas other TCT studies indicate a
positive relationship between transaction frequency and hierarchical governance (e.g., Bienstock
and Mentzer, 1999; Brechhemier & Saussier, 2001; Dahlstrom & Nygaard, 1993; Fishback,
1992; Joskow, 1985; Klein, 1989; Muris, Scheffman, & Spiller, 1992; Stuckey, 1983). Taken
together, the contradictory empirical results create ambiguity surrounding the notion that
transaction frequency leads firms to choose hierarchical governance over markets.
MODERATING EFFECTS AMONG TRANSACTION ATTRIBUTES
The foregoing discussion neglects three core issues. First, it presents a positive linear
relationship between uncertainty and hierarchical governance. Second, the discussion also
presents a positive linear relationship between frequency and hierarchical governance. However,
Williamson (1985) notes that asset specificity interacts with both uncertainty and frequency to
influence governance choice. Third, resource-based theory (RBT) relies on similar logic to
explain governance choice (Leiblein, 2003; Madhok, 2002; Williamson, 1999). Specifically,
there is some overlap between TCT’s definition of asset specific investments and RBT’s
definition of strategically valuable resources. Given this, RBT logic is used to suggest that
certain types of asset specific investments might have a stronger positive relationship with
hierarchical governance. Each of these issues is outlined below.
32
Uncertainty and Asset Specificity
While early TCT research asserts that uncertainty is independently and positively related
to hierarchical governance (Williamson, 1975), more recent theoretical developments suggest
that the relationship between uncertainty and hierarchical governance is stronger when asset
specific investments are present (Williamson, 1985, 1991). Indeed, Williamson (1985: 59)
contends that “uncertainty matters little without the need for asset specific investments.” When
buyers procure standardized inputs that do not require asset specific investments, uncertainty is
less problematic because non-specific investments are involved, which can be redeployed
without significant costs. Given this, transactions surrounded by uncertainty and supported by
non-specific investments can be governed by market contracts because there is no loss if
disturbances occur. However, when asset specificity and environmental or behavioral uncertainty
are joined, exchange hazards can surface due to incomplete contracts and because assets
involved in the transaction are at greater risk. Specifically, exchange hazards might increase as
one transactor is positioned to take advantage of the other. Hierarchical governance can use
formal controls and dispute resolution mechanisms to attenuate these hazards more efficiently
than markets or hybrids (Klein et al., 1978).
Although the relationship between uncertainty and hierarchical governance is prominent
in the literature (cf. Boerner & Macher, 2002; David & Han, 2004), Williamson (1985) contends
that there is an interactive relationship between asset specificity, uncertainty, and hierarchical
governance. More specifically, he asserts that uncertainty enables greater opportunism in
markets and hybrids only when asset specific investments are present (Williamson, 1985). Thus,
as asset specificity increases, exchange hazards caused by uncertainty increase. Research
indicates that Williamson’s (1985) arguments regarding the interactive relationship between
uncertainty, asset specificity, and hierarchical governance have some support (e.g., Anderson,
1985; Coles & Hesterley, 1998; Walker & Weber, 1987), however, other studies found no
support or negative relationships for the predicted relationships (e.g., Anderson & Schmittlein,
1984; Buvik & John, 1999; Fan, 1999; Murray & Kotabe, 1992; Widener & Selto, 1999).
Because of this, there is ambiguity surrounding the predicted interactive relationship between
environmental uncertainty and asset specificity.
33
Frequency and Asset Specificity
Recall that one-time and occasional transactions present relatively fewer exchange
hazards, and thus, have little impact on transaction costs and a firm’s adaptation ability.
Recurrent transactions, on the other hand, can reduce overall costs by reducing fixed costs per
transaction. Specifically, higher transaction frequency reduces costs through improved
economies of scale and by reducing ongoing costs of drafting and negotiating contracts (Stigler,
1968; Williamson, 1975). Williamson (1985) asserts that the higher the transaction frequency,
the more likely it is that firms will invest in more efficient production mechanisms, which can be
broadly conceived of as asset specific investments.
As noted earlier, asset specific investments create fewer exchange hazards, transaction
costs, and adaptation problems when hierarchical governance is used (Williamson, 1985, 1991).
When recurrent transactions are supported by highly specific assets, hierarchies should best
economize on transaction costs, and therefore replace markets and hybrid governance modes
(Williamson, 1991). Thus, the relationship between frequency and hierarchical governance might
be contingent on the level of asset specific investments used to support the transaction.
Type of Asset Specificity
Although empirical evidence seems to support the predicted relationship between asset
specificity and hierarchical governance (Rindfleisch & Heidi, 1997; David & Han, 2004), an
issue that has received scant attention is whether some asset specific investments influence
governance choice more than others (Leiblein, 2003; Silverman, 2002). Yet some types of asset
specific investments might be more important than others. RBT describes resources as tangible
and intangible “assets, capabilities, organizational processes, information, and knowledge”
(Barney, 1991: 101). When these resources are valuable, rare, nonsubstitutable, and inimitable,
they are considered strategically valuable and thus capable of creating sustained organizational
advantages (Barney, 1991).
There is, however, considerable overlap between TCT’s definition of asset specific
investments and RBT’s definition of strategically valuable resources. For example, both
theoretical perspectives recognize the importance of human and brand assets. Some strategically
valuable resources are logically a subset of Williamson’s (1985, 1995) broader categories of
34
asset specific investments. According to TCT, human and brand assets increase exchange
hazards, transaction costs, and cause subsequent adaptability problems. RBT describes these
assets as valuable, rare, nonsubstitutable, and inimitable, and suggests that firms will leverage
them to create advantages (Barney, 1991; Wernerfelt, 1984). Thus, such assets might not only
increase exchange hazards, transaction costs, and subsequent adaptation problems, but such
assets might also be at the core of a firm’s advantage.
In sum, TCT does not explain why some asset specific investments are more likely to be
governed by hierarchy than other types. RBT sheds light into this by suggesting that assets that
are valuable, rare, nonsubstitutable, and inimitable can create sustained organizational
advantages. Therefore, asset specific investments that fall into this category not only reduce
transaction costs, but they can also be leveraged to improve firm performance when governed by
hierarchy. Given this, perhaps some asset specific investments are more strongly related to the
degree of integration than others. The present study seeks to examine this by drawing on RBT to
investigate whether human and brand assets are more strongly related to the degree of integration
than other types of asset specific investments. Specifically, it will examine whether human and
brand assets are more strongly related to the degree of integration than physical, site, dedicated,
and/or temporal assets.
EXOGENOUS MODERATING EFFECTS
Despite the empirical evidence supporting some key TCT predictions, researchers have
devoted little attention to potential exogenous moderating effects impacting the relationships
between transaction attributes and the degree of integration. Although Williamson (1985, 1991)
describes potential moderating variables, TCT research has been criticized for focusing mainly
on transaction attributes and failing to account for exogenous variables, such as institutional
factors, that might also shape governance choice (Ghoshal & Moran, 1996; Perrow, 1986).
Beyond TCT’s primary governance choice explanations (i.e., transaction cost economizing),
institutional factors, such as a firm’s industry or social factors, such as cultural norms, might also
influence governance choice (Williamson, 1985). In other words, firms might copy norms (e.g.,
degree of integration decisions) set by other firms in their industry (Mahoney, 1992) or
governance choice may be shaped by prevailing societal norms or county-specific cultural
35
factors (Dimaggio & Powell, 1983; Dyer, 1997). But these factors are noticeably absent in the
literature.
In short, extant TCT research has not fully developed the theoretical logic for several
potential exogenous moderators. The present study takes a step towards investigating these
influences and specifically delineates how two observable factors – industry and country
influences – moderate the relationship between transaction attributes and a firm’s decision to use
market, hierarchical, or hybrid governance.
SUMMARY
This chapter provided a broad overview of TCT. The chapter began by explaining that
TCT is centrally concerned with adaptation. Figure 1 shows that adaptation problems result from
transaction attributes, exchange hazards arising from bounded rationality and opportunism, and
the choice of governance. The second section illustrates that three main transaction attributes –
asset specificity, uncertainty, and frequency – influence the preferred degree of integration.
Despite the empirical support for TCT, this review indicates that there are ambiguous, and in
some cases, contradictory findings, surrounding the relationships described by Williamson
(1975, 1985). The study also suggests that relationships among transaction attributes as well as
industry and social factors might moderate the relationships between transaction attributes and
governance choice. In chapter three, I build on this review to propose main and moderating
effects and develop testable hypotheses.
36
CHAPTER 3: HYPOTHESES
The last chapter provided a broad overview of transaction cost theory (TCT), summarized
its main predictions and empirical results, and outlined some unanswered questions. This chapter
draws heavily on chapter two to develop testable hypotheses. Before developing these
hypotheses, however, it is necessary to reiterate TCT’s logic delineating why firms govern
transactions with market, hybrid, or hierarchical modes. As depicted in Figure 1, TCT postulates
that transaction attributes (e.g., asset specificity), exchange hazards (e.g., threat of opportunism),
transaction costs (e.g., crafting agreements), and the adaptation capabilities shape which mode is
the preferred degree of integration.
Transaction attributes are the starting point for degree of integration decisions. Because
of a transaction’s attributes, exchange hazards arising from bounded rationality and the threat of
opportunism create transaction costs, or frictions in market exchanges. Exchange hazards were
previously described as situations in which one transactor has the ability to exploit unwritten or
unenforceable parts of contracts (Klein, 1980). Because contracts are incomplete as a result of
bounded rationality, exchange hazards are created when economic actors can cheat, renege, or
fail to modify agreements to reflect changing economic realities. Hazards arise because some
economic actors are opportunistic (Williamson, 1975). Exchange hazards lead to both ex ante
and ex post transaction costs. Ex ante transaction costs result from searching for exchange
partners, and from the expense of crafting, negotiating, and safeguarding agreements
(Williamson, 1985). Firms incur these expenses to protect themselves from environmental
disturbances that could impair adaptation. Ex post transaction costs include the setup and
operating expenses of dispute resolution mechanisms, haggling expenses resulting from joint
efforts to realign contracts when conditions change, and ongoing expenses that secure
commitments between the parties (Williamson, 1985). Ex post transaction costs can be
uneconomical and the underlying disputes can be time consuming. These costs, including the
time involved with settling disputes, affect a firm’s adaptation capabilities. Transaction costs and
perceived adaptability impact the degree of integration, or the mode of governance chosen.
Governance modes have differing degrees of integration. Markets and hierarchies are
polar modes. Markets represent the lowest and hierarchies represent the highest degree of
37
integration; hybrids are situated between these modes. Key differences among governance modes
in lowering exchange hazards and transaction costs include formal controls, dispute resolution
mechanisms, and incentive intensity (Williamson, 1991). First, hierarchies have more formal
controls, including monitoring capabilities (e.g., internal audits) and internal reward structures
(e.g., compensation and stock options) than do markets or hybrids. Second, hierarchies can
resolve disputes via fiat rather than through the court system or mediators. Third, markets and
hybrids preserve stronger incentives than hierarchies because owner compensation is contingent
on firm performance under these governance modes. In contrast, employees typically share less
in firm profits when hierarchy governs a transaction, which reduces incentives below optimal
levels. Because of these differences, each governance mode varies in its ability to manage
transactions and adapt to environmental disturbances. Accordingly, once a firm chooses a
governance mode, this decision inevitably shapes its future adaptive capabilities.
In short, when the appropriate mode is not used to govern transactions, higher transaction
costs and adaptation problems result. Drawing on this theoretical logic, this chapter’s central
objective is to outline TCT’s key predictions and develop new hypotheses. To accomplish this
objective, the chapter proposes main effect hypotheses, describes interactive relationships among
transaction attributes, and offers theory that explains how factors that have not traditionally been
shown to impact governance choice might influence such decisions. The chapter concludes with
a brief summary.
MAIN EFFECT HYPOTHESES
As noted previously, this study’s purpose is to examine TCT’s key predictions. The main
predictions shown in the literature are the relationships between the transaction attributes 1) asset
specificity, 2) environmental uncertainty, 3) behavioral uncertainty, 4) frequency, and the degree
of integration. These relationships are depicted below in Figure 3. In short, TCT predicts
transaction attributes are positively related to the degree of integration. In other words, as the
level of a transaction attribute increases, firms will move from lower degrees of integration (i.e.,
markets) toward more hierarchical modes (i.e., hybrids or hierarchies). These predictions are
outlined below.
38
Asset Specificity
Environmental
Uncertainty
Behavioral
Uncertainty
Degree of
Integration
Frequency
FIGURE 3 – Hypothesized Main Effects
Asset Specificity
Asset specificity refers to the level of unique investment required to support a transaction.
Investments can range from non-specific to highly specific, and the degree of asset specificity
influences governance choice (Williamson, 1981, 1985). Transactions supported by non-specific
assets do not pose significant exchange hazards because the assets can be more easily redeployed
without significant loss of value. Such assets can be more easily redeployed than highly specific
assets because alternate buyers or sellers of the asset can be identified (Mahoney, 1992).
Managers can simply redeploy non-specific assets to new uses if a transactor were to attempt to
engage in opportunism. Thus, non-specific assets reduce the threat of opportunism. According to
TCT, when the threat of opportunism is low, there is little need for formal controls or dispute
resolution mechanisms (Williamson, 1985). Moreover, markets and hybrids preserve stronger
incentives than hierarchies. Thus, when non-specific assets support transactions, market and
hybrid modes are more efficient than hierarchical modes and therefore should be used to govern
such transactions.
When highly specific assets support transactions, there are fewer alternative buyers and
sellers. Because there are fewer potential buyers and sellers, transactors incur increased search
costs and expenses when trying to redeploy highly specific assets. Joskow’s (1985) analysis
illustrates this point. He explains that utility firms that built power plants (e.g., asset specific
39
investments) adjacent to coal mines would experience difficultly redeploying assets outside the
focal transaction without significant loss of productive value. Indeed, it is costly to redeploy
power plants without significant cost because such assets are used specifically to generate power,
and reconfiguring these assets to accomplish another task without a significant loss of value
would be costly. Moreover, redeploying the assets would reduce the advantage of close
proximity to the coal mines. In this instance, coal suppliers could charge higher prices than those
originally agreed upon because the utility firms would have difficulty accessing comparably
priced supplies without the advantage of proximity. Following this logic, when asset specificity
increases, the difference between the asset’s first- and second-best use increases, and because of
this, market and hybrid arrangements expose firms to greater potential threats of opportunism
(Mahoney, 1992).
When the threat of opportunism is high, firms have two options to reduce such threats:
they can devise more complete contracts or they can use hierarchical governance. Barthelemy
and Quelin (2002) show that because of increased threats of opportunism created by higher
levels of asset specific investments, transactors seek to make contracts more complete.
Specifically, to protect against threats, boundedly rational transactors attempt to specify
numerous contractual contingencies. Managers include these contingencies in an attempt to be
complete and to protect the firm against threats. However, crafting such agreements is costly.
Further, even in detailed contracts, certain contingencies might not be specified, requiring
subsequent contract renegotiations. In the example used earlier, GM did not predict significant
volume increases and, consequently, failed to include this contingency in their Fisher Auto Body
contract (Klein et al., 1978). If this contingency had been specified, GM would not have faced
the high transaction costs that arose from extensive bargaining or the resulting adaptation
problems. Thus, when asset specific investments are present, increased transaction costs and
subsequent adaptation problems can result in markets and hybrids.
In sum, when asset specificity increases, not only must market and hybrid contracts be
more complete, but adaptations might also be needed that require renegotiation. Yet bounded
rationality confounds transactors’ abilities to write complete contracts and specify appropriate
adaptations. Because of these limitations, market contracts often do not specify efficient dispute
40
resolution mechanisms. When disputes arise, costly and time-consuming haggling or court
assistance might be used to resolve differences. Unlike markets, hybrids typically specify dispute
resolution mechanisms that are less costly and less time-consuming than the court system
(Williamson, 1985). These mechanisms typically require transactors to seek resolution of
differences privately. If a resolution cannot be reached, a neutral party (e.g., mediator) is
typically used, which resolves disputes more efficiently than agreements that must be resolved
through the court system. By replacing costly and time-consuming haggling or meditation with
resolution by fiat, hierarchies can resolve disputes more efficiently than markets or hybrids.
Moreover, hierarchies do not require complete contracts that specify dispute resolution
mechanisms when asset specific investments are present. Thus, transaction costs and the time
required to resolve disputes are reduced. Because of this, as asset specificity increases,
governance modes that have higher degrees of integration are preferred. In short, when asset
specificity increases, markets, and to a lesser extent hybrids, increase transaction costs and cause
greater adaptability problems than hierarchies. Thus, it is predicted that:
Hypothesis 1: Asset specificity is positively related to the degree of integration.
Uncertainty
There are two main types of uncertainty: environmental and behavioral. Broadly
speaking, environmental uncertainty refers to unpredictability outside the firm’s boundaries
(Williamson, 1975, 1985). When environmental uncertainty is low, there are few unanticipated
disturbances. Transaction costs are low because firms can anticipate and specify ex ante
appropriate adaptations to disturbances in market and hybrid contracts (Williamson, 1985).
When environmental uncertainty is high, however, boundedly rational economic actors cannot
anticipate environmental disturbances and specify all appropriate contractual adaptations. Thus,
the higher the environmental uncertainty, the greater the likelihood that contracts will be
incomplete.
When contracts are incomplete, the threat of opportunism and transaction costs increase
because adaptations are needed in response to environmental disturbances (Williamson, 1985).
To reduce the threat of opportunism and the resulting transaction costs (e.g., renegotiations) that
potentially arise in markets and hybrids, contracts must be more complete (Barthelemy &
41
Quelin, 2002). While complete contracts can reduce exchange hazards and improve adaptability
under predictable situations, greater environmental uncertainty increases the number of
unforeseen contingencies that arise. Because these contingencies are unforeseen, transactors
cannot specify them in contracts, thereby increasing the threat of opportunism when adaptations
are needed. In short, increased environmental uncertainty confounds managers’ abilities to
predict contingencies, which makes contracts more incomplete. When contracts are more
incomplete, however, higher transaction costs result because bargaining and renegotiations are
needed to resolve disputes arising from unforeseen contingencies.
Compared to hierarchies, markets and hybrids have less powerful ways to resolve
disputes. As noted earlier, hierarchies can resolve disputes more efficiently via fiat, which
reduces transaction costs and improves adaptability (Williamson, 1985). Markets, on the other
hand, often do not specify dispute resolution mechanisms and although hybrid contracts may
include such mechanisms, they are less efficient than fiat. Thus, as environmental uncertainty
increases, hierarchy lowers transaction costs and better enables firms to navigate their
environments. In short, as environmental uncertainty increases, firms prefer higher degrees of
integration. That is, under such conditions, hybrids are preferred over markets, but hierarchies
are favored over hybrids.
Behavioral uncertainty refers to performance measurement difficulties (Jones, 1987;
Williamson, 1985, 1991). Performance measurement difficulties are low when outputs are easy
to monitor and evaluate. When outputs are easily monitored and evaluated, the value of a
transaction can be assessed and rewards can be linked to productivity (Alchian & Demsetz,
1972). In contrast, performance measurement difficulties are high when outputs cannot be easily
monitored and evaluated. The threat of opportunism is increased because transactors can
deliberately lower output or reduce quality in ways that might not be detected by others. Alchian
and Demsetz’s (1972) freight loading example shows that when two men jointly load cargo, it is
difficult to determine each person’s productivity solely by observing the total weight loaded per
day. Measuring each loader’s productivity is difficult because a team yields the output. In such
instances, one loader could restrict output at the expense of the other. Because of situations such
as this, the threat of opportunism increases when output is difficult to measure.
42
To reduce the potential threat of opportunism, firms govern transactions in three main
ways (Barthelemy & Quelin, 2002; Milgrom & Roberts, 1990). First, firms can invest in
mechanisms to improve performance measurement, such as quality control persons. Second,
costly contracts specifying how other transacting partners should act can be written. Third,
hierarchical governance can be used. Because hierarchies can monitor performance directly, the
threat of opportunism is reduced compared to markets or hybrids (Williamson, 1985). When
people’s behaviors are monitored, they tend to act differently than they would otherwise
(Eisenhardt, 1989; Rothlisberger & Dickson, 1939; Wright, Kroll, & Elenkov, 2002). Hierarchies
can also reward employees through compensation or promotions, which can reduce opportunistic
behaviors by rewarding individuals based on perceived behaviors (Williamson, 1985; Zenger &
Hesterly, 1997). In addition, if disputes arise, hierarchies can resolve them via fiat, thereby
reducing transaction costs and improving firms’ adaptation capabilities. Thus, hierarchies are
best equipped to handle transactions when performance is difficult to measure (Williamson,
1985).
In short, when environmental or behavioral uncertainty increases, markets and to a lesser
extent, hybrids, increase transaction costs and thus lower firms’ adaptation capabilities.
Hierarchies, by contrast, can use formal controls and dispute resolution mechanisms to reduce
transaction costs and improve adaptation capabilities. Based on this, as environmental or
behavioral uncertainty increase, firms prefer more highly integrated governance modes (e.g.,
hierarchy) over less integrated modes (e.g., markets). Stated formally:
Hypothesis 2a: Environmental uncertainty is positively related to the degree of
integration.
Hypothesis 2b: Behavioral uncertainty is positively related to the degree of integration.
Transaction Frequency
Transaction frequency refers to the number of times a transaction occurs. According to
Williamson (1985), transaction frequency can be categorized as one-time, occasional, or
recurring. When frequency is one-time or occasional, transaction costs and adaptation problems
are often low because these transactions pose fewer threats of opportunism. In short, contracts
can be more easily written when transactions are one-time or occasional. If a transaction is to
transpire for one week, for instance, transactors could more easily anticipate and specify
43
contractual contingencies. Accordingly, firms’ adaptation capabilities are not heavily influenced.
Thus, non-recurring transactions have fewer threats of opportunism, and have a lesser impact on
transaction costs or adaptability. Because markets preserve more powerful incentives than
hierarchy and such transactions have little impact on firms, they are consequently handled by
markets (Globerman & Schwindt, 1986; Joskow, 1985; Klein, 1989).
When transactions recur, however, hierarchy can lower transaction costs (Williamson,
1985). Masters and Miles (2002), for example, found that because of costs resulting from
negotiating and renegotiating contracts for recurring needs, market contracting increases
transaction costs. Firms preferred hierarchy instead because contracts do not need to be
negotiated or renegotiated on an ongoing basis, thereby reducing transaction costs. Thus,
hierarchies lower transaction costs by ameliorating ongoing negotiating and renegotiating costs
with other transactors. Therefore, it is predicted that:
Hypothesis 3: Transaction frequency is positively related to the degree of
integration.
INTERACTIONS AMONG TCT VARIABLES
The type of asset specificity or uncertainty might impact the relationships between these
transaction attributes and the degree of integration. This section draws on RBT to suggest that
human and brand asset specificity are more strongly related to the degree of integration than
physical, site, dedicated, or temporal asset specificity. Further, real options theory is used to
argue that behavioral uncertainty is more strongly related to the degree of integration than
environmental uncertainty. These relationships are depicted in Figures 4 and 5 respectively, and
described in greater detail below.
Type of Asset Specificity
Because of bounded rationality, the threat of opportunism, and the potential loss of an
asset’s value outside the focal transaction, TCT posits that markets fail when asset specific
investments are present (Williamson, 1981). TCT, therefore, takes a market failures approach,
wherein the focus is matching transactions to governance structures that reduce opportunism and
minimize transaction costs (Madhok, 2002). Other theorists take what might be called an
44
High
(Hier.)
Degree of
Integration
Human and Brand Asset Specificity
(Hyb.)
Physical, Site, Dedicated, Temporal
Asset Specificity
Low
(Mkt.)
Low
High
Level of Asset Specificity
FIGURE 4 – Relationships Between Different Asset Specific Investments and the Degree of
Integration
High
(Hier.)
Behavioral Uncertainty
Degree of
Integration
(Hyb.)
Environmental Uncertainty
Low
(Mkt.)
Low
High
Level of Uncertainty
FIGURE 5 - Relationships Between Behavioral Uncertainty, Environmental Uncertainty,
and the Degree of Integration
organizational advantages approach. They suggest that when asset specific investments are
present, governance choice is contingent on advantages firms can achieve when hierarchical
governance is used even if the transaction could conceivably be performed by markets or hybrids
(Leiblein & Miller, 2003; Mahoney, 1992; Silverman, 2002). Thus, there is perhaps a more
compelling reason for governing asset specific investments beyond TCT’s market failures
explanation.
45
Resource-based theory (RBT) has been perhaps the most prominent theory used to
understand organizational advantages in the last decade (Barney et al., 2001). RBT makes two
core assumptions. First, it assumes that assets are heterogeneously distributed across firms
(Barney, 1991; Peteraf, 1993). Second, assets are assumed to be imperfectly mobile (Barney,
1991; Dierickx & Cool, 1989; Lippman & Rumelt, 1982). When assets are heterogeneously
distributed and not perfectly mobile, any advantages arising from asset ownership can endure for
sustained periods. More specifically, sustained advantages can arise from owning assets that are
valuable, rare, nonsubstitutable, and inimitable (Barney, 1991). Asset value is derived from the
ability to reduce costs or increase prices that customers are willing to pay. Asset rareness simply
means that the asset is not widely available. Nonsubstitutable assets are those that do not have
comparable alternatives. Inimitable assets are not easily copied. According to RBT, valuable and
rare assets can create advantages; however, these assets must also be nonsubstitutable and
inimitable for firms to achieve sustained advantages. Assets used to gain sustained advantages
are hereafter referred to as strategically valuable assets (Chi, 1994). Therefore, beyond TCT’s
logic suggesting that opportunism influences the preferred degree of integration for all asset
specific investments, this study contends that consequences of opportunism can be even graver if
they affect strategically valuable assets, which are at the core of sustained advantage.
According to Williamson (1985, 1996), there are six main categories of asset specific
investments, including site, physical, human, dedicated, brand name capital, and temporal.
Strategically valuable assets are a subset of asset specific investments, and RBT’s logic suggests
that such assets might influence governance choice more strongly than other types of asset
specific investments. Despite the large body of empirical findings linking asset specificity to
hierarchical governance (e.g., David & Han, 2004), there has not been a systematic examination
into whether strategically valuable assets are more strongly related to hierarchical governance
than other types of specific investments.
Although physical, site, dedicated, and temporal assets are degree of integration
determinants in TCT, such assets are valuable, but not necessarily rare, nonsubstitutable, or
inimitable. First, physical assets include specialized tooling, plant, and equipment. Such assets
shape governance choice (e.g., Klein et al., 1978; Joskow, 1985) and can be valuable and
46
somewhat rare, but they are not typically difficult to imitate (Mata, Fuerst, & Barney, 1995;
Powell & Dent-Micaleff, 1997). One reason is that physical assets are tangible, and therefore,
visible to many firms, making them relatively easy to imitate. Second, site asset specificity refers
to assets that permit products or services to progress through successive stages in close
geographic proximity, which allows for lower transportation and inventory costs (e.g., Cook,
1997; Joskow, 1985). While valuable, these assets are not necessarily rare because other
organizations can simply copy these assets by purchasing land and equipment in close proximity.
Third, dedicated assets are investments in generalized production capacity or assets that are
dedicated to serving a specific customer, such as manufacturing facilities or other assets that
make transactions more efficient (e.g., Globerman & Schwindt, 1986; Maltz, 1993). Although
these assets make transacting more efficient, they are relatively easy to imitate and are not rare
because the physical assets used to accomplish such transactions are more widely available.
Fourth, temporal asset specificity is a subset of site specificity, and relates to assets that facilitate
interdependent transactions requiring timely responses (e.g., Butler & Carney, 1983; Murris,
Scheffman, & Spiller, 1992). Extant supply chain management research indicates that such assets
might not be rare, nonsubstitutable, or inimitable because transactors can substitute such assets
with collaborative relationships (Hult, Ketchen, & Nichols, 2002; Sharland, Eltantawy, &
Giunipero, 2003). Thus, transactions involving temporal asset specificity do not seem capable of
creating sustained advantages because there are other viable substitutes.
According to RBT, asset value and rarity are necessary, but not sufficient, to create
sustained advantages from asset specific investments; substitutability and imitability are key
determinants for creating sustained advantages (Barney, 1991). Because physical, site, dedicated,
and temporal assets are not necessarily rare, nonsubstitutable, or inimitable, they cannot be used
to gain sustained advantages. A key implication is that such assets cannot be the source of
sustained competitive advantage. Human and brand assets, on the other hand, are considered
more rare, nonsubstitutable, and inimitable, and might thus create opportunities for sustained
advantages (Hitt, Bierman, Shimizu, & Kochhar, 2001; Nayyar, 1990). Thus, these assets are
more likely to be at the core of a firm’s sustained advantage. Given that human and brand assets
47
create opportunities for sustained advantages, there might be a more compelling reason to use
hierarchy over markets or hybrids.
Human asset specificity. Human assets include knowledge and expertise possessed by a
firm’s employees (Barney, 1991). Similarly, human asset specificity refers to the skills of people
who are specific to a transaction (Williamson, 1985). These human assets have the potential to
become more valuable and rare as their transaction specific knowledge and experience increase
(Argyres, 1996; Leiblein & Miller, 2003). Moreover, there are social interactions among
participants who perform the transaction, and cooperative relationships may form among some
of the participants (Barney & Hansen, 1994). Thus, the means by which these human assets
accomplish a task are ambiguous and complex, making the interactions and cooperative
relationships among participants difficult, if not impossible, to imitate perfectly, especially for
other firms seeking to replicate these assets from a distance (Reed & DeFillippi, 1990). In
addition to the difficulty associated with imitating another firm’s human assets, the costs of
imitating those assets might simply be too high because knowledge and experience are not easily
acquired, thereby negating any potential financial benefits that might be derived (Peteraf, 1993).
Therefore, human assets, their social interactions, cooperative relationships, and their patterns of
organization can be key determinants of sustained advantages (Hitt et al., 2001).
According to TCT, however, human asset specificity increases exchange hazards,
transaction costs, and subsequent adaptability problems (Williamson, 1985). Because specific
human skills that are learned on the job take substantial time to acquire, they are hard to replace.
Therefore, when specific skills support a transaction, the use of market contracts or hybrids can
create exchange hazards and subsequent adaptation problems, which can be mitigated by
hierarchical governance (e.g., Anderson, 1988; Coles & Hesterly, 1998). Thus, TCT suggests
that human assets should be governed by hierarchy to reduce transaction costs and improve
firms’ adaptation capabilities. Alternatively, RBT suggests that inimitable human assets can
create sustained advantages (Barney, 1991). When assets can create sustained advantages,
hierarchical governance permits firms to accrue advantages from ownership. Thus, governing
human assets by hierarchy protects firms from the threat of opportunism that could potentially
impact the core of their advantage. Moreover, hierarchical governance provides greater
48
protection from imitability, which allows firms to leverage assets for sustained advantages.
Therefore, governing human assets should not only economize on transaction costs, but should
also create sustained organizational advantages that other assets, such as physical, site, dedicated,
and temporal assets, are incapable of producing.
In sum, beyond the problems identified in the TCT literature regarding the use of less
integrated governance modes when human asset specificity increases (Williamson, 1985), RBT
indicates such assets provide opportunities for sustained advantages (Barney, 1991). Taken
together, these theoretical perspectives indicate that not only is the threat of opportunism greater
when human assets are at the core of sustained advantage, but also that governing such assets
allows firms to create sustained advantages. Therefore, governing human assets though more
integrated modes is essential. Given this, it is predicted that:
Hypothesis 4a: Human asset specificity is more strongly related to the degree of
integration than physical asset specificity.
Hypothesis 4b: Human asset specificity is more strongly related to the degree of
integration than site asset specificity.
Hypothesis 4c: Human asset specificity is more strongly related to the degree of
integration than dedicated asset specificity.
Hypothesis 4d: Human asset specificity is more strongly related to the degree of
integration than temporal asset specificity.
Brand asset specificity. Beyond human assets, brand assets can also create
organizational advantages (Barney, 1991; Combs & Ketchen, 1999). Brands are intangible assets
that firms accumulate over time and are considered assets because customers gain experiential
benefits such as perceived product quality and ease-of-use (Nayyar, 1990; Williamson, 1996).
Because customer time is limited, brands that have been positively experienced, such as those
perceived to be high quality or easy to use, outperform brands that are untested (Srivastava,
Fahey, & Christensen, 2001). According to Srivastava and colleagues (2001: 785): “Over time,
based in part upon their assessment of attributes and benefits, customers develop attitudes toward
or holistic perceptions of a particular firm or brand and its offerings.” A brand therefore activates
customer attitudes toward products and the selling firm. If attitudes arising from customer
experiences are positive, the product and selling firm will be viewed in a positive light (Keller,
1993). Over time, firms develop reputations for their products, which can exist on a continuum
49
from extremely negative to extremely positive. When reputations are positive, they become
valuable and rare intangible assets, and customers prefer to buy products associated with sound
brand names because they are confident their experience will be favorable (Roberts & Dowling,
2002; Srivastava et al., 2001).
When customers have favorable product experiences, brand assets become more valuable
(Roberts & Dowling, 2002). As favorable customer experiences accumulate, a customer
develops a history with the selling firm. Once this history is established, customers become less
willing to switch products, reducing both the willingness and ability of other firms to try to
emulate the brand image, thereby leading to advantages for incumbent brands (Fournier, 1998;
Porter, 1980; Srivastava & Shocker, 1991). Thus, as the number of positive customer
experiences grows, customers develop a positive brand image and might become more loyal
(Keller, 2003). Therefore, as firms develop reputations for quality, they increase their brand
image and loyalty. Accordingly, brand assets become more valuable, rare, nonsubstitutable, and
inimitable. Because of this, such assets can be used to create sustained advantages.
According to TCT, however, brand assets create an increased threat of opportunism, as
other transactors could act opportunistically and thus jeopardize the firm’s reputation. Combs
and Ketchen (1999), for example, showed that firms tend to use more hierarchy than franchising
(a form of hybrid) once brands are established. Once brands are established, firms need to
preserve brand integrity to maintain a consistent image and ensure customer loyalty. Nayyar
(1990: 516) argued that quality lapses could “destroy the reputation” built up through prior
customer experiences. The possibility that a firm’s reputation will be damaged is higher when
firms use markets or hybrids to govern transactions that potentially affect reputation. For
example, when franchisees are located in places such as along major highways where most
consumers are not repeat buyers, franchisees might lower quality to improve profitability at the
expense of the franchisors’ brand image (Caves & Murphy, 1976). Over time, however, the
cumulative effect of poor customer experiences could damage the brand’s reputation. Company
managers, on the other hand, have different incentives, and are paid salaries to enforce company
policies and procedures (Bradach, 1997). Thus, they have no incentive to “cut corners.”
Likewise, through close monitoring, hierarchical governance can ensure that product quality and
50
consistency remains high; therefore, brand integrity is preserved more easily (Cook, 1997).
Consequently, when brands are established, hierarchy should preserve the brand image, and
customers are perhaps more likely to remain loyal.
The foregoing discussion indicates that, according to TCT, brand assets should be
governed by hierarchy to ensure that opportunistic transactors do not diminish brand image and
the resulting customer loyalty (Williamson, 1985). RBT, on the other hand, suggests that brand
assets are hard to imitate, and can thus create sustained advantages (Barney, 1991). By
suggesting that such assets be protected from opportunism to preserve the core of sustained
advantages, these theoretical perspectives provide a more compelling rationale for governing
brand assets by hierarchy than physical, site, dedicated, or temporal assets. Thus, because brand
assets are more difficult to imitate than other types of asset specific investments, can create
greater advantages, and expose firms to increased opportunism in market or hybrid exchange, it
is predicted that:
Hypothesis 5a: Brand asset specificity is more strongly related to the degree of
integration than physical asset specificity.
Hypothesis 5b: Brand asset specificity is more strongly related to the degree of
integration than site asset specificity.
Hypothesis 5c: Brand asset specificity is more strongly related to the degree of
integration than dedicated asset specificity.
Hypothesis 5d: Brand asset specificity is more strongly related to the degree of
integration than temporal asset specificity.
Type of uncertainty. Although both environmental and behavioral uncertainty might lead
to hierarchical governance, they impact degree of integration decisions differently (Sutcliffe &
Zaheer, 1998). As noted above, environmental uncertainty refers to unpredictability, and
behavioral uncertainty refers to performance measurement difficulties (Williamson, 1985).
Empirical evidence of the effect of both types of uncertainty on governance choice is mixed, but
the relationship between behavioral uncertainty and hierarchical governance is more strongly
supported (David & Han, 2004). Anderson and Schmittlein (1984), for example, found that
behavioral uncertainty, but not environmental uncertainty, impacts governance choice.
Specifically, they studied electronic component manufacturers and found that firms use a direct
51
sales force (i.e., hierarchy) instead of distributors or other resellers when it is difficult to measure
salespersons’ results precisely.
Although TCT asserts that the most efficient way to manage environmental uncertainty is
through hierarchy (Williamson, 1975), real options theory suggests that the use of hierarchy can
reduce a firm’s flexibility and adaptability (Folta, 1998). When hierarchy is used, firms make
asset specific investments (e.g., specialized human assets) that could be avoided in market and
hybrid exchange. The logic of real options asserts that because these investments are costly and
irreversible they can also reduce a firm’s adaptability and threaten its survival (Folta, 1998).
When firms commit large sums to asset specific investments, their ability to change and adapt to
environmental disturbances diminishes. Thus, real options theory contends that the more
efficient way to manage environmental uncertainty is to use less integration. Specifically, under
environmental uncertainty, boundedly rational managers cannot accurately predict when
hierarchy best serves the firm. Therefore, managers might prefer the right to use specific assets
in markets and hybrids, without the difficult-to-reverse investment that would occur with
hierarchical governance (Lieblein, 2003). Thus, it might be best to rely on markets or hybrids to
absorb this environmental uncertainty. Because of this, hierarchical governance might not serve
the firm’s best interest when environmental uncertainty is present. Perhaps this is why
Williamson (1985) suggests that behavioral uncertainty is more strongly associated with
hierarchical governance than environmental uncertainty.
Behavioral uncertainty relates to performance measurement problems when tasks are
accomplished jointly or when quality is difficult to assess (Alchian & Demsetz, 1972;
Williamson, 1985). Markets are most effective when tasks can be separated and contributions
measured, allowing for appropriate incentives to be linked to productivity or quality outcomes
(Alchian & Demsetz, 1972; Poppo & Zenger, 1998). Yet because some tasks are accomplished
jointly or quality is difficult to measure, transactors can act opportunistically by lowering output
or quality to raise profits (Williamson, 1985). Without precise measurement capabilities, such
problems are difficult to identity and correct in markets and hybrids. Hierarchies, by contrast,
have enhanced monitoring capabilities. Because of this, managers can observe behaviors more
closely and thus worry less about measuring outcomes (Eisenhardt, 1989).
52
In sum, the reasoning and empirical evidence supporting TCT’s predicted relationship
between environmental uncertainty and hierarchical governance is equivocal. Real options
theory provides a contradictory theoretical explanation to TCT, suggesting that firms should
avoid uncertainty by using market and hybrid governance modes. This might explain why
empirical evidence supporting the relationship between environmental uncertainty and
hierarchical governance is mixed. Alternatively, because behavioral uncertainty exposes firms to
hazards posed by opportunism and the best mode for lowering these hazards is hierarchy,
behavioral uncertainty should be more strongly related to hierarchical governance than
environmental uncertainty. Following this logic, it is predicted that:
Hypothesis 6: Behavioral uncertainty is more strongly related to the degree of
integration than environmental uncertainty.
This section outlines two key TCT predictions. As noted earlier, asset specificity is
TCT’s ‘big locomotive.’ Because of this, when asset specific investments are joined with other
transaction attributes, such as uncertainty or frequency, the relationships between those
transaction attributes and the degree of integration should become more positive. Specifically,
Williamson (1985) asserts that there is an interactive relationship between environmental
uncertainty, asset specificity, and the degree of integration. He also asserts that there is an
interactive relationship between frequency, asset specificity, and the degree of integration. These
relationships are depicted in Figure 6 below and the hypotheses are then developed.
Asset Specificity
Environmental
Uncertainty
Degree of
Integration
Asset Specificity
Frequency
Degree of
Integration
FIGURE 6 – Interactions Among Different Transaction Attributes
53
Environmental Uncertainty and Asset Specificity
Although transactions involving environmental uncertainty increase transaction costs,
and therefore give rise to hierarchical governance, these types of transactions become even more
problematic when joined with asset specificity (Williamson, 1985). When transactions are
supported by non-specific assets, these assets can be redeployed without a significant loss of
value (Mahoney, 1992; Williamson, 1985). Thus, if uncertainty creates the need to redeploy a
non-specific asset, it can be done at a low cost. However, when transactions are supported by
more specific investments, exchange hazards surface in market and hybrid governance modes
(Williamson, 1985). When asset specific investments are joined with increased environmental
uncertainty, it becomes more difficult and thus more costly to write contingent contracts
specifying appropriate adaptations to environmental disturbances (Williamson, 1991). According
to Williamson (1985: 60), “whenever assets are specific in nontrivial degree, increasing the
degree of uncertainty makes it more imperative that the parties work things out.” In essence,
environmental uncertainty increases the threat of opportunism associated with asset specific
investments and thus, the probability that disputes will occur. The disputes that result are costly
to resolve in markets or hybrid governance modes because extensive bargaining is needed. In
contrast, hierarchies can resolve such disputes by fiat and thus reduce transaction costs
(Williamson, 1991).
In sum, the combined presence of non-trivial asset specific investments and
environmental uncertainty increases exchange hazards more than either alone (Leiblein & Miller,
2003; Sutcliffe & Zaheer, 1998). Exchange hazards increase transaction costs and create
potential environmental adaptation problems in market and hybrid governance modes. In
contrast, hierarchies can reduce transaction costs and subsequent adaptation problems when
transactions involve asset specific investments and environmental uncertainty. Thus, as
environmental uncertainty and asset specificity are joined, firms favor more integrated modes of
governance. Therefore, it is predicted that:
Hypothesis 7: As asset specificity increases, the positive relationship between
environmental uncertainty and the degree of integration becomes stronger.
54
Frequency and Asset Specificity
Because one-time and occasional transactions have a smaller impact on firms, TCT is
mainly concerned with recurring transactions. As transactions become more frequent, firms can
reduce costs in two ways. First, transaction costs can be reduced by ameliorating ongoing
negotiating and renegotiating expenses for recurring needs (Masters & Miles, 2002). Second,
production costs can be reduced by investing in asset specific investments (Williamson, 1985).
As noted previously, however, when such investments are made, transaction costs rise because
firms seek to reduce potential exchange hazards and subsequent adaptation problems. When
recurring transactions are supported by asset specific investments, exchange hazards and the
resulting adaptation problems increase (Williamson, 1985). Hierarchies reduce exchange
hazards, transaction costs, and the resulting adaptation problems through the use of formal
controls and executive fiat. Drawing on this logic, Widener and Selto (1999) showed that firms
tend to use internal staff when frequent transactions are joined with asset specific investments.
Specifically, they found that the combined presence of frequency and asset specific investments
explained 53% of the variance in degree of integration decisions. In addition, Masters and Miles
(2002) found that when recurring tasks require some level of expertise (i.e., human asset
specificity), most firms preferred hierarchies to other modes of governance.
A key implication is that transaction frequency increases the opportunity for firms to
make production more efficient (Williamson, 1985). As frequency increases, firms are better
equipped to recover costs from asset specific investments, such as specialized dies and tooling,
and to achieve economies of scale. Yet such investments create exchange hazards, and,
consequently, transaction costs and adaptation problems when market or hybrid governance
modes are used (Williamson, 1991). As noted previously, when asset specificity increases, the
difference between an asset’s first- and second-best use increases (Mahoney, 1992). Unlike
transactions supported by non-specific assets, transactions supported by increased asset
specificity can cause financial harm to one party when the other acts opportunistically.
Hierarchies have more powerful formal controls than markets or hybrids to lower the threat of
opportunism, which lowers transaction costs and improves firms’ adaptability.
55
In sum, when transactions become more frequent, there is incentive for firms to invest in
specialized investments to reduce costs. However, these investments increase exchange hazards
and transaction costs, and cause adaptation problems in markets and hybrids. Hierarchies, in
contrast, reduce exchange hazards and the resulting transaction costs and adaptability problems
when asset specific investments are present. Thus, when frequency and asset specificity are
joined, firms prefer increased integration to reduce transaction costs and the subsequent
adaptability problems. Therefore, it is predicted that:
Hypothesis 8: As asset specificity increases, the positive relationship between frequency
and the degree of integration becomes stronger.
EXOGENOUS MODERATING EFFECTS
Institutional theory offers an alternative explanation for governance choice decisions. The
explanation institutional theory provides is that firms are affected by external entities that define
accepted standards (Dimaggio & Powell, 1983; Scott, 1995). This perspective explains that firms
are embedded in social and economic relationships that influence their actions (Granovetter,
1985; Oliver, 1997). A growing body of empirical evidence supports this notion (e.g., Barr &
Glynn, 2004; Brouthers & Brouthers, 2000; Delios & Beamish, 1999; Geletkanycz, 1997).
Williamson (1985: 22) recognizes this and points out that his work “concentrates on transaction
cost economizing, but the costs need to be located in the larger context of which they are a part.”
Institutional theory suggests that larger context includes the industry in which the firm competes
(Teece, Rumelt, Dosi, & Winter, 1994; Oliver, 1997), the cultural ethos of the country in which
the firm is situated (Dacin, 1997; Kogut, Walker & Anand, 2002), and the regulatory practices
that affect industry (Delios & Beamish, 1999; Konrad & Linehan, 1995). Thus, the relationships
between transaction attributes and the degree of integration might be shaped by such factors. In
an effort to take a broad, systematic perspective, this study examines the moderating influence of
industry norms, cultural ethos, and the effect of regulatory differences within countries. These
potential moderating influences are depicted below in Figure 7, followed by the hypotheses.
56
Exogenous Factors
Industry
Cultural Ethos
Regulatory Differences
Asset Specificity
Environmental
Uncertainty
Behavioral
Uncertainty
Degree of
Integration
Frequency
FIGURE 7 – Exogenous Moderating Factors
Industry
Institutional theory suggests that the industry in which firms compete influences key
managerial decisions (Dimaggio & Powell, 1983). One way that a firm’s industry shapes its
managerial decisions is that some firms imitate strategies used by industry leaders in order to
lower uncertainty and increase legitimacy (Dimaggio & Powell, 1983; Teece, 1982). When an
industry leader’s strategy is presumed effective, it is sometimes copied by others (Oliver, 1991).
Copying these strategies increases a firm’s legitimacy from the standpoint of other key
stakeholders, such as a firm’s suppliers or creditors. It increases legitimacy because key
stakeholders often trust an industry leader’s strategy and believe that when it is followed the
copying firm will produce similar results. Thus, copying an industry leader’s strategy is
considered socially advantageous. In contrast, legitimacy can be challenged by key stakeholders
when a firm’s strategy does not resemble that of at least some firms in their industry (Dimaggio
& Powell, 1983; Oliver, 1997). Deephouse (1999), for example, shows that when firms conform
to certain industry standards, they can increase performance. Because of this, he suggested that
57
when banks’ asset management strategies do not differ dramatically from those that are
normative for the industry, key stakeholders consider such strategies to be legitimate. When
strategies are considered legitimate, the firm’s overall legitimacy improves, which increases firm
performance. Thus, conforming to norms established by industry leaders improves a firm’s
survival chances and overall prosperity (e.g., Azoulay & Shane, 2001; Deephouse, 1999).
When several firms in an industry adopt similar strategies, the phenomenon is called the
proliferation of “industry recipes.” Extant research suggests that firms adopt strategies that are
similar to those of competitors (e.g., Kogut et al., 2002; Teece et al., 1994). Once these “industry
recipes” are adopted, they sometimes endure even if they lose technical efficiency (Tolbert &
Zucker, 1983). Williamson (1985) also contends that beyond a transaction’s attributes, industry
factors also shape governance choice. This contention arises from the notion that industry
participants confront similar environmental disturbances and the appropriate action might be
similar for each firm. Thus, industry participants confronting similar issues might respond with
similar strategies, including how certain transactions are governed (Williamson, 1985). Given
that firms adopt strategies of industry leaders to gain legitimacy and acceptance, it follows that
industry factors impact the relationship between transaction attributes and the degree of
integration. Based on this, it is predicted that:
Hypothesis 9: The relationship between transaction attributes and the degree of
integration is more similar within than between industries.
Cultural Ethos
Beyond industry factors, institutional theory’s logic suggests that cultural factors might
also impact the relationship between transaction attributes and governance choice. Williamson
(1985: 22) acknowledges this and asserts that “the social context in which transactions are
embedded – the customs, mores, habits, and so on – have a bearing, and therefore need to be
taken into account, when moving from one culture to another.” Institutional theory posits that
managerial decisions and the actions of employees are, to some extent, enabled or constrained by
cultural elements, such as the prevailing cultural norms or the structure of social relations outside
firms’ boundaries (Dimaggio & Powell, 1983; Granovetter, 1985). Perhaps these contextual
factors explain why Dyer’s (1996, 1997) findings contrast with TCT’s predictions. He found that
58
asset specific investments lower rather than increase transaction costs. Dyer’s study was
conducted in Japan; the fact that his findings are different from those predicted by TCT,
illustrates the role cultural differences might play in modifying the premises of TCT.
Drawing on institutional theory’s logic, Kogut et al. (2002) argue that cultural differences
affect managerial decisions. They found different diversification patterns among firms
headquartered in different countries, and assert that these patterns are shaped in part by the
beliefs embedded in a country’s culture. Drawing on Hofstede’s (1980) work on how cultural
differences impact managerial decision-making, Geletkanycz (1997) found that cultural
differences between countries shape managerial decisions. Hofstede’s (1980) work provides
perhaps the best-known framework for examining the ways in which cultural differences among
countries shape decisions. In this work, he explains that the degree of individualism or
collectivism is a key source of cultural difference. Triandis (2004) contributes to this discussion
by examining managerial behaviors in individualistic and collectivistic cultures, and concludes
that this dimension of culture has the most significant influence on different behaviors.
Individualistic cultures are those in which individuals are primarily concerned with their own
well being (Hofstede, 1980). The threat of opportunism might grow when individuals place their
concerns above those of others. That threat increases because individuals who place their
concerns first are more willing to take advantage of others to accomplish their goals. Thus, in
individualistic cultures there might be a higher threat of opportunism, which increases
transaction costs and affects governance choice.
Collectivistic cultures, on the other hand, are those in which individuals perceive
themselves as being interconnected within a larger social group and who place greater emphasis
on the interests of the collective group (Hofstede, 1980). In collectivist cultures, therefore,
individuals might be more concerned about acceptance within or alienation from social groups or
broader society. Indeed, when individuals are deeply embedded in social relationships, they
develop trust and shared understandings, which leads to reduced opportunism (Granovetter,
1985). Thus, exchange hazards might be fewer in cultures with a collectivist ethos because
individuals are perhaps less prone to take opportunistic actions that could damage their
59
reputation. In such cultures, a damaged reputation could lower an individual’s social status or
make the person an outcast.
Given the potential consequences of a damaged reputation in collectivistic cultures,
individuals might be less prone to take opportunistic actions. In market and hybrid exchange,
opportunism is a key determinant of transaction costs and the resulting governance decisions
(Williamson, 1975, 1991). In collectivist cultures, however, where individuals place the group’s
interests above their own and are concerned with social status, the overall threat of opportunism
is likely reduced. Markets and hybrids preserve higher-powered incentives than hierarchies
(Williamson, 1985; Zenger & Hesterly, 1997). Consequently, the transaction costs associated
with using market and hybrid governance modes should be lower than in individualistic cultures,
thereby shaping degree of integration decisions. Therefore, it is predicted that:
Hypothesis 10: The extent of cultural collectivism is negatively related to the degree of
integration.
Regulatory Differences
Beyond the cultural factors that impact governance choice, institutional theory purports
that regulatory authorities also shape firm decisions (Dimaggio & Powell, 1983; North, 1990). A
key concern is whether regulatory authorities protect the right to appropriate returns from asset
ownership (Furubotn & Pejovich, 1974; Teece, 1986). In short, if regulatory authorities do not
protect property rights, there is little incentive to use markets or hybrids because there might be
unwanted leakage of proprietary information (Williamson, 1996). In such cases, firms might
prefer hierarchy to reduce opportunities for leakage. Supporting this notion, Delios and Beamish
(1999) found that when specific assets (e.g., intellectual property) are transferred to affiliates in
other countries, the level of property protection influences foreign investors’ equity positions.
Specifically, in cases where regulatory authorities provide lower property rights protection, firms
preferred higher equity positions. This suggests that if governments do not protect property
rights, and allow the theft or copying of a firm’s assets (e.g., intellectual property), firms prefer
hierarchical governance. Hierarchy might be preferred because it has formal controls, such as
auditing and monitoring capabilities, to limit such threats. Therefore, more returns could be
appropriated from asset ownership. Recognizing this, Williamson (1991: 290) asserts that the
60
“increased risk of leakage increases the cost of hybrid contracting as compared with hierarchy.”
Accordingly, when the potential for leakage is high, such as it is in countries where property
rights are not protected, market and hybrid governance modes pose greater exchange hazards.
The preceding discussion indicates that beyond transaction cost economizing, governance
choice is likely contingent on property rights protection. Specifically, in countries where
regulatory authorities provide low protection of property rights, the threat of opportunism
increases because firms could have their assets stolen or copied without recourse. Given this
increased potential for opportunism, hierarchical governance should be favored in countries with
lower property rights protection. Thus, as regulatory authorities increasingly enforce property
rights, there is less of a need for higher integration. Therefore, it is predicted that:
Hypothesis 11: Property rights protection is negatively related to the degree of
integration.
PERFORMANCE IMPLICATIONS
Perhaps an implicit implication from the preceding sections is that matching transactions
to the appropriate governance mode leads to improved firm performance through lower
transaction costs and enhanced adaptability (Williamson, 1981, 1985). As noted previously,
when non-specific assets support transactions or when there is little uncertainty, firms are less
concerned about the threat of opportunism or the difficulty of adaptation in markets or hybrids.
Firms are less concerned because they can easily redeploy assets or identify alternative
transactors without high search costs (Williamson, 1985). Moreover, markets and hybrid modes
of governance preserve stronger incentives. Thus, when transactions are supported by nonspecific assets or lack uncertainty, TCT postulates that market and hybrid modes economize on
transaction costs, and thus, improve performance (Williamson, 1985). Following Ouchi (1980),
Williamson (1981) refers to this as his ‘discriminating alignment’ hypothesis, and posits that
matching transactions to the appropriate degree of integration creates efficient boundaries, and
hence, improved firm performance. This relationship is depicted in Figure 8.
61
Asset Specificity
Environmental
Uncertainty
Behavioral
Uncertainty
Matched
Degree of
Integration
Performance
Frequency
FIGURE 8 – Discriminating Alignment Performance Implications
As transactions become more specific and uncertain, transactors become more concerned
about the threat of opportunism. First, as asset specificity increases, the threat of opportunism
increases as a result of the difference between an asset’s first- and second-best use (Mahoney,
1992). Using markets or hybrids therefore exposes firms to greater exchange hazards. Second,
when environmental uncertainty increases, contracts are more incomplete. This can cause
adaptation problems because as environmental uncertainty increases, the number of unforeseen
contingencies that can arise also increases. However, because these contingencies are
unforeseen, transactors cannot specify them in contracts, which increases the threat of
opportunism when adaptations are needed (Williamson, 1985). Third, when performance is
difficult to measure, transactors can either lower output or quality that other transactors cannot
easily detect. To protect the firm against such threats, firms can either craft and negotiate
extensive market or hybrid contracts, which is costly, or use hierarchical governance
(Barthelemy & Quelin, 2002).
The foregoing discussion indicates that transactions should be matched to governance
structures that can reduce the threat of opportunism and minimize transaction costs (Madhok,
2002). When the threat of opportunism and transaction costs are reduced, firms’ adaptation
capabilities and performance should be improved (Leiblein & Miller, 2003; Poppo & Zenger,
1997; Williamson, 1975, 1985). Thus, it is predicted that:
Hypothesis 12: Matching transactions to the appropriate degree of integration improves
firm performance.
62
SUMMARY
In summary, this chapter investigates factors influencing degree of integration decisions
and the performance implications of such decisions. In the first section, main effect hypotheses
relating asset specificity, uncertainty, and frequency to the preferred degree of integration were
developed. Then, interactive relationships among transaction attributes were described and
theory was offered that explains how factors that have not traditionally been shown to impact
governance choice might influence such decisions. In the final section, the performance
implications of matching transaction attributes to the appropriate governance mode were
proposed. The next chapter outlines the study’s method of investigating these relationships.
63
CHAPTER 4: METHOD
This chapter describes the method used to examine the relationships outlined in chapter
two. First, I recapitulate why meta-analysis is the appropriate technique to analyze these
relationships. Second, I describe the sample and coding procedures. Third, I include brief
descriptions of the measures. Fourth, I give details of the meta-analytic procedures used to test
main effects. Fifth, I explain the procedures used to examine moderating influences.
As noted earlier, several qualitative reviews have provided contradictory results and
concluded that more empirical TCT research is needed (e.g., David & Han, 2004). Unlike these
reviews, meta-analytic techniques quantitatively aggregate prior empirical findings (Hunter &
Schmidt, 1990). Thus, meta-analysis can resolve the contradictory results by aggregating
findings to estimate the effect size between transaction attributes and degree of integration.
Further, meta-analysis is more accurate than qualitative reviews (e.g., vote count procedures)
because it accounts for methodological problems, such as sampling and measurement error in
primary studies (Hunter & Schmidt, 1990). Finally, meta-analysis enables researchers to detect
potential moderating influences that impact relationships of interest (Aguinis & Pierce, 1998).
Although meta-analysis appears to offer a more systematic approach to assess the TCT literature,
the method is not without its limitations. Two key limitations, which are outlined in chapter six,
include the use of bivariate correlations and the ‘file drawer’ problem (Dalton & Dalton, 2005).
Despite these limitations, however, meta-analysis has capabilities beyond those offered in
qualitative reviews, and thus seems to be the best approach to empirically assess the TCT
literature.
Sample
In an effort to find and to include all studies that investigate key TCT predictions. I
conducted a keyword search, examined reference sections of major reviews, and engaged in
correspondence with authors in the field. Dissertation Abstracts, ABI Inform, EconLit, and
JSTOR were the databases used for the keyword search. The keywords that were included were
transaction cost(s), asset specificity, uncertainty, and frequency. I used 1975 as the starting date
for the search because TCT gained popularity after Williamson published his influential book in
1975. The search resulted in 892 articles. In order to find articles that were not identified in the
64
keyword search, I examined reference sections of major qualitative reviews (e.g., Boerner &
Macher, 2002, David & Han, 2004; Mahoney, 1992; Rindfleisch & Heide, 1997). This search
helped identify several usable working papers. In a further attempt to include all relevant
studies, I sent over 50 emails to authors who have published extant empirical TCT work but who
did not include correlations in their studies. These emails requested correlations, any articles in
press, unpublished manuscripts, and assistance in locating unpublished work of other colleagues.
My email requests resulted in two additional usable correlation matrices, along with one usable
working paper. Because this meta-analysis examined only correlations among key TCT
predictions, studies for inclusion had to list correlations between at least one measure of an
independent variable (e.g., asset specificity) and at least one measure of the degree of integration.
Further, if it was clear that separate primary studies used the same sample, effect sizes were
averaged and counted as one study (Hunter & Schmidt, 1990). This resulted in 98 usable studies.
Each study is listed in Appendix A.
Coding
There are two key issues involved with coding: standardization and consistency. To
ensure standardization, I created a coding sheet with specific instructions that describe how each
data item is to be coded (Appendix B). To ensure consistency (i.e., inter-rater reliability), the
dissertation’s author trained a strategic management doctoral student to code studies based on the
coding instructions and coding sheet (Appendix C & D). Appendix C contains a coding sheet
with space used for data input, including key TCT measures and other primary study attributes,
such as industry and country factors. Appendix D is the coding sheet used for correlations among
key TCT variables. For example, the correlation between site asset specificity (13-1) and the
degree of integration between markets versus hierarchy (16-1) was coded in the first column in
the 14th row. There were many opportunities for disagreement. After a random set of 10 studies
was coded, we met to discuss agreement (i.e., consistency) among coders. During the meeting,
discussions about disagreements were held to resolve inconsistencies, propose resolutions, and
update instructions for the remainder of the coding. After 15 more studies were coded, we met
again to discuss agreement. There was significantly more consistency, and we agreed to code the
remaining studies according to the updated instructions. When we finished, we had overall initial
65
inter-rater reliability of 88 percent (i.e., 2042/2327). We discussed the remaining 285
discrepancies, and agreed on a single coding approach to resolve each.
Measures
Correlations among measures and the associated reliabilities for each measure that have
been used to depict TCT’s constructs were collected from primary studies, including: 1)
transaction attributes – asset specificity, uncertainty, and frequency, 2) degree of integration –
market, hierarchy, or hybrid, and 3) performance. In addition, studies were coded for moderator
analyses based on the primary studies’ sample of the nature of the transaction, the industry, and
the country.
A key concern in the organizational sciences is whether measures are valid (Kerlinger,
1986; Schwab, 1999). The measures used in existing TCT studies vary dramatically, thus I
strived to include only valid measures in the meta-analyses. This was accomplished by putting
each measure through a retranslation exercise (Smith & Kendall, 1963). The first step was to
provide construct definitions, outlined below in Table 4, to a panel of three Assistant Professors
of Management at research-oriented universities. The second step was to provide a description of
each measure (e.g., specialized training for human asset specificity) used to operationalize the
construct. The third step was to have the experts categorize whether measures possessed low (1),
medium (2), or high (3) validity for operationalizations of each construct. The meta-analyses
were run with measures that received a combined total content validity rating of six or better. In
other words, the experts’ combined ratings needed to suggest that a measure had medium
validity overall in order for a measure to be included.3 Table 5 contains the studies that tested at
least one of the dissertation’s hypothesized relationships along with sample measure
descriptions. It also contains the expert raters’ overall assessments of the measures’ content
validity and their identification of the classification of the TCT construct (e.g., physical versus
brand asset specificity or environmental versus behavioral uncertainty). Table 6 shows the
different dependent variables found in the primary studies.
3
Because the total content validity score was equal to or greater than six, measures were included that received one
low content validity ranking (1) from one expert as long as one other expert gave the measure a high ranking (3).
66
TABLE 4 – TCT’s Core Construct Definitions
Construct
Definition
Site Asset
Specificity
Physical Asset
Specificity
Human Asset
Specificity
Dedicated Asset
Specificity
Brand Asset
Specificity
Temporal Asset
Specificity
Environmental
Uncertainty
Behavioral
Uncertainty
Frequency
Degree of
Integration
Assets that permit products or services to progress through successive
stages in close geographic proximity.
Specialized assets, including tooling and dies required to support a
transaction.
Skills of people who are specific to a transaction.
Performance
Assets that are dedicated to serving a specific customer or working with
a specific supplier.
Intangible asset that firms accumulate over time because customers gain
experiential benefits.
Assets that facilitate interdependent transactions requiring timely
responses.
Unpredictability stemming from a firm’s environment.
Performance measurement difficulties.
How often a transaction occurs.
Markets and hierarchies are polar degrees of integration. Markets
represent the lowest degree, hierarchies represent the highest degree, and
hybrids are situated in between.
Improvements in operational or financial performance.
Industry. In addition to correlations that were collected from each study to examine the
hypothesized main effects, the nature of each primary study’s sample was coded to examine the
predicted moderating effect. Specifically, studies were grouped according to whether the sample
was drawn from one industry or more than one industry.
Country. The nature of the primary studies’ sample country was coded. For studies that
drew samples from one country or a specific geographic region (e.g., North America), the
country or region was noted. Following Barr and Glynn (2004), Brouthers and Brouthers (2000),
and Geletkanycz (1997), the individualism versus collectivism cultural scores developed by
Hofstede (1980, 1991) were used for each country to code studies into subgroups. Thus, analyses
were conducted using primary samples from individualistic cultures, and from collectivistic
cultures. In extant cross-cultural research, researchers assert that these cultural scores
67
TABLE 5 – Primary Study Measures and Content Validity Ratings
Type1 Author
Sample Measure Description
Content
Validity
Rating2
Site Asset Specificity
Shane, 1998
Geographic concentration
Masten et al., 1989
Proximity to other production stages
Subramani & Venkatraman, 2003 Location proximity to buyers
6
7
8
Physical Asset Specificity
Zahra & Nielsen, 2002
Anderson et al., 2000
Bigelow, 2004
Leiblein et al., 2002
Mayer & Nickerson, 2003
Dragonetti et al., 2003
Schilling & Steensma, 2002
Anderson & Coughlan, 1987
Bienstock & Mentzer, 1999
Mesquita, 2002
Joshi & Campbell, 2003
Leiblein & Miller, 2003
Coles & Hesterly, 1998
Pouder, 1994
Combs & Ketchen, 1999
Lassar & Kerr, 1996
Mesquita, 2002
Mesquita, 2002
Joshi & Stump, 1999
Walker & Weber, 1984
Are resources and facilities strong
Number of parts in subassembly
Car horsepower, cubic displacement, and cylinder diameter
Does exchange require analog tech or memory
Does work involve mainframe
Fixed assets to sales ratio
How common is the technology throughout the industry
Is product components and materials or equipment
Costs incurred from shipments in refrigerated trailers
Investments in generic assets
Supplier has necessary tools and equipment
If exchange involves analog, memory, or customized product
Can equipment be reused at other hospitals
Is specialized equipment required
Is there difficultly redeploying equipment
Level of technological specificity, design, and performance
Physical assets to help OEM customers
Specialized Equipment and Tooling
Specialized manufacturing assets
Specialized tooling and equipment, manufacturing technology
4
6
6
6
6
6
6
6
7
7
7
8
9
9
9
9
9
9
9
9
If managers are compensated after taxes
Number of web employees
Number of hours (informal and formal) training by managers’ peers
Does firm have development and marketing experience
Prevalence of team selling requiring non-sales personnel
Experience in therapeutic area
Firm specific training time
How long does it take to train employees and managers
If persons outside product team are involved in execution
If persons outside product team are involved in execution
Managers' marketing experience/Number of managers
Managers' production experience/ Number of managers
Managers' technological experience/Number of managers
Difficulty of hiring and training salespeople
Franchisee franchise experience
4
5
6
6
6
7
7
7
7
7
7
7
7
8
8
Human Asset Specificity
Dunbar & Phillips, 2001
Rasheed & Geiger, 2001
Masters & Miles, 2002
Affuah, 2001
John & Weitz, 1989
Hughes, 1999
Coles & Hesterly, 1998
Combs & Ketchen, 1999
Mayer & Nickerson, 2003
Mayer, 1999
McGee et al., 1995
McGee et al., 1995
McGee et al., 1995
Weiss & Anderson, 1992
Shane, 1998
68
TABLE 5 – continued
Nicholls-Nixon & Woo, 2003
Level of biotech expertise
8
Takeishi, 2001
Zahra & Nielsen, 2002
Weiss & Anderson, 1992
Anderson & Coughlan, 1987
Gebelt, 1992
Poppo & Zenger, 2002
Gainey & Klass, 2003
Takeishi, 2001
Zaheer & Venkatraman, 1994
Level of coordination required with engineering and purchasing
Level of management and employee manufacturing skills
Sales rep has a great deal of product knowledge
Training requirements to sell product
Company, industry, hardware, and software knowledge
Individuals have company specific skills and information
Is training tailored to firm's needs
Level of specific engineering and component-specific knowledge
Specialized employee skills, training, and workflows
8
8
8
8
9
9
9
9
9
Are our products more technologically advanced, higher quality, and unique
than competitors
Number of patents
Is the company well respected
Pricing and Advertising relative to competitors
Composite of advertising intensity and if premium pricing is used
Degree of differentiation product offers
Proprietary technology in product
4
6
6
6
7
7
8
Brand Asset Specificity
Aulakh & Kotabe, 1997
Mutinelli & Piscitello, 1998
Combs & Ketchen, 1999
Lassar & Kerr, 1996
Girlea, 2001
Schilling & Steensma, 2002
Mayer, 1999
Dedicated Asset Specificity
Dragonetti et al., 2003
Heide & John, 1992
Orr, 1998
Reuer et al., 2002
Buvik & John, 2000
Mesquita, 2002
Subramani, 1997
Zaheer & Venkatraman, 1995
Joshi & Campbell, 2003
Andersen & Buvik, 2001
Artz & Brush, 2000
Buvik & Andersen, 2002
Assets specific to main suppliers that create switching costs
Composite of partner specific physical assets, procedures, and training
Composite of site, physical, human, and dedicated assets
Experience on projects with same partner
Investments in equipment and routines for this supplier
Process investments for OEM customers
Specificity of planning for products, programs, and product pricing
Specificity of workflows, routines, and people
Supplier has necessary knowledge and technology
Supplier product design enhancements and production equipment
Physical assets, personnel, and training that are dedicated to a supplier
Composite of physical assets, production facilities, tooling, and knowledge
which are adapted to a supplier's product
Mayer, 1999
Mean of suppliers investment in specific assets and R&D
Poppo & Zenger, 2002
Process is custom tailored to the customer
Stump, 1995
Production system is tailored to supplier
Joshi & Stump, 1999
Relationship specific assets
Aulakh & Kotabe, 1997
Significant investment geared toward customizing product to foreign market
and it is costly to change existing way of doing business
Reuer & Arino, 2002
Specialized investments, people and facilities dedicated to relationship
Masten et al., 1989
Standard or non-standard productive assets
Buvik & Gronhaug, 1999
Supplier has made product adjustments, invested in equipment, and invested
other resources
Heide & John, 1990
Supplier investments in physical assets, procedures, and people
Sawhney et al., 1999
The supplier has adapted production equipment, dedicated resources, and we
are only buyer
Subramani & Venkatraman, 2003 To what extent can the assets be used with other customers
69
8
8
8
8
8
8
8
8
8
8
9
9
9
9
9
9
9
9
9
9
9
9
9
TABLE 5 – continued
Salmond, 1987
Was the investment substantial and how did it compare to investments with
other partners
9
Temporal Asset Specificity
Buvik & Gronhaug, 1999
Our firms production tech is sequential, preprogrammed, and control and
scheduling is needed
Subramani & Venkatraman, 2003 Composite of software, operating procedures, admin procedures
Widener & Selto, 1999
Time spent working with proprietary information; firm specific language and
routines
7
8
9
Asset Specificity-Not Classified3
Penner-Hahn, 1998
Annual R & D expenses
Gebelt, 1992
Number of user departments involved in project
Fan, 1996
Percentage of product plants that have at least 1 input plant close by
Advertising to sales ratio
D'aveni & Ravenscraft, 19944;
Delios & Beamish, 1999;
Dhanaraj & Beamish, 2004;
Kobrin, 1991; Lu, 2002; Reuer,
2001; Shrader, 2001; Tan &
Vertinsky, 1996; Widener &
Selto, 1999
Mesquita, 2002
Generic process improvements
Stump, 1995
Product conforms to industry standards or our own needs
Andersen & Buvik, 2001
Product demand and market conditions
Subramani & Venkatraman, 2003 Product planning, conception, and design
Belderbos, 2002; Brouthers, 2002; R & D to sales ratio
D'aveni & Ravenscraft, 1994;
Delios & Beamish, 1999;
Dhanaraj & Beamish, 2004;
Dunbar & Phillips, 2001; Hughes,
1999; Kobrin, 1991; Lu, 2002;
Mutinelli & Piscitello, 1998;
Osborn & Baughn, 1990; Oxley,
1997; Reuer, 2001; Shrader,
2001; Tan & Vertinsky, 1996;
Widener & Selto, 1999
D'aveni & Ravenscraft, 1994
Selling expenses to sales ratio
Pouder, 1994
Are specialized skills required
Zaheer & Venkatraman, 1994
Customized technology support
Zahra & Nielsen, 2002
Level of coordination between manufacturing and other functions
Masten et al., 1989
Value of engineering effort
Heide & John, 1990
Composite of specialized physical assets, procedures, and people
Rasheed & Geiger, 2001
Hardware, software, communications support, and personnel investments
Subramani, 1997
Level of software applications, and operating procedures
Coles & Hesterly, 1998
Required interaction with other hospital employees
John & Weitz, 1989
Required time to become familiar with company customers, products, and
procedures
Fink, 1995
Level of relationship specific equipment, training, and production systems
Bigelow, 2004
Physical and technical uniqueness is interconnected to other processes
Specialized training, equipment, and proprietary goods and services
Brouthers et al., 2003
4
6
6
6
6
6
6
6
6
6
7
7
7
7
8
8
8
8
8
9
9
9
Environmental Uncertainty
Lee, 1998
Mitchell & Singh, 1996
Total number of previous legal lawsuits due to patent infringement
Is firm a startup
70
5
5
TABLE 5 – continued
Andersen & Buvik, 2001
Andersen & Buvik, 2001
Dragonetti et al., 2003
Dwyer & Welsh, 1985
Fan, 1996
Harrigan, 1985
Heide & John, 1990
Leiblein & Miller, 2003
Mcnally & Griffin, 2004
Mitchell & Singh, 1996
Mutinelli & Piscitello, 1998
Schilling & Steensma
Domestic versus international buyer-seller relationships
Is product sourced domestically or internationally
Product renewal rates
Product standards, protection laws, and rapid product improvements
Standard deviation of residuals for price variations
Variations in sales/volumes
Do technological requirements change
Sum of square errors of 5 year demand/units delivered
Is supply stable?
Average 1 year market growth
Financial investor risk ratings by country
Average length of product life and value
Widener & Selto, 1999
Artz & Brush, 2000
Bergh & Lawless, 1998
Brouthers, 2002
Variation and turnover in business structure, demand, and activities
Price and volume uncertainty
Industry net sales variability
Converting and repatriating profits in nationalized risks, cultural similarity, and
market conditions
Coles & Hesterly, 1998
Amount of technological change
Cusumano & Takeishi, 1991
Rate of input price change
Delios & Beamish, 1999
Euromoney's Risk index
Hughes, 1999
Number of industry competitors
John & Weitz, 1989
Stability in forecasts and sales
Joshi & Campbell, 2003
Supply and price volatility
Leffler & Rucker, 1991
Number of tree species on land
Mcinnes, 1999
Economic uncertainty (no more detail offered in survey)
Mcinnes, 1999
Manpower fluctuation, difficult to recruit qualified candidates,
difficult to manage
Mcinnes, 1999
Volume uncertainty (no more detail offered in survey)
Oxley, 1999
Euromoney's country risk measures
Oxley, 1999
Euromoney's overall country risk rating
Stump, 1995
Technological changes in end product
Subramani & Venkatraman, 2003 Likelihood of major changes
Tan & Vertinsky, 1996
Average percentage change in host market shipments over three years
Zaheer & Venkatraman, 1995
Pricing and new product introduction uncertainty
Buvik & Gronhaug, 1999
Demand varies, market conditions are unstable
Buvik & John, 2000
Demand varies widely, competitors changing, and high innovation rates
Chelariu, 2002
Does environment change fast or slow?
Dickson, 1997
Composite of whether competitors change marketing practices and rate of
product obsolescence
Dwyer & Welsh, 1985
Business cycle variability and competitive intensity
Fink, 1995
The number of past product improvements and ability to predict product
improvements
Heide & John, 1990
Level of forecast variability
Johnson, 1988
Technology stability in last 5 years
Joshi & Campbell, 2003
Technology standards are constantly evolving
71
6
6
6
6
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
8
TABLE 5 – continued
Mcinnes, 1999
Mcinnes, 1999
Mcnally & Griffin, 2004
Russo, 1992
Salmond, 1987
Sawhney et al., 1999
Funding uncertainty – is program government funded
Political uncertainty (no more detail offered in survey)
Anticipated future technological adjustments
Gas price changes
Changes in product demand and market entry by foreign competitors
Complex versus simple products, high vs low tech innovation, tech change
Political, social, economic stability
Rate of product obsolescence, tech change, and forecasting variability
Pace of technological change and economic stability
8
8
8
8
8
8
9
9
9
4
5
5
5
5
5
5
5
5
John & Weitz, 1989
Lassar & Kerr, 1996
Mcinnes, 1999
Poppo & Zenger, 2002
Pouder, 1994
Steensma & Corley, 2001
Walker & Weber, 1984
Walker & Weber, 1984
Widener & Selto, 1999
Cusumano & Takeishi, 1991
Gebelt, 1992
Girlea, 2001
Do you need to follow up on production and quality control
Rigidity of thicker parts
Average age of plant and equipment in use
Are there problems monitoring quality
It is hard to evaluate, on site inspection is required
How responsibilities are defined
It is hard to evaluate, on site inspection is required
Level of satisfaction with quality
Sales are not the only measure of performance
Maximum amount of time (e.g., years) that strategic
planning makes sense
Outside suppliers could take advantage of us without us knowing
Customer similarity or differences
How difficult is it to specify the task
Would the technology meet technical standards
Number of different support services provided
Frequency of meetings with other competitors
Variation in transportation time/duration
Project size in terms of menu screens, number of reports produced, and number
of files accessed
Ability to assess performance accurately
Level of monitoring needed to ensure conformance
Service complexity (no more detail offered in survey)
How hard is it to measure performance
How difficult is it to define the quality of service
Will technology work the way it was designed, will it meet specifications
Daily or monthly volume requirement fluctuations
Frequency of product specification changes
How difficult it is to determine performance
Number of categories of information possessed about a supplier
Is project a new development or modification of existing system
Difficulty measuring performance
Joshi & Campbell, 2003
Joshi & Stump, 1999
Masters & Miles, 2002
Customer preferences are continuously evolving and demand varies
Supplier price, delivery, and adaptability performance predictability
Extent to which performance could be assessed
8
8
8
Brouthers, et al., 2003
Steensma, 2000
Sutcliffe & Zaheer, 1998
Behavioral Uncertainty
Heide & John, 1990
Anderson et al., 2000
Balakrishnan & Wernerfelt, 1986
Brouthers et al., 2003
Heide & Miner, 1992
Mcnally & Griffin, 2004
Orr, 1998
Sutcliffe & Zaheer, 1998
Weiss & Anderson, 1992
Dragonetti et al., 2003
Gainey & Klass, 2003
Johnson, 1988
Pouder, 1994
Schilling & Steensma, 2002
Shane, 1998
Sutcliffe & Zaheer, 1998
Bienstock & Mentzer, 1999
Gebelt, 1992
72
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
7
7
8
8
8
TABLE 5 – continued
Mayer & Nickerson, 2003
Mayer, 1999
Parkhe, 1993
Johnson, 1988
Poppo & Zenger, 2002
If technology made quality difficult to measure
Difficulty verifying quality after initial inspection
Speed and reliability regarding accuracy of information from partner
Did you have enough information, can you predict outcomes
How fast skills sets and optimal org configurations are changing
8
8
8
9
9
External source is not relevant to decision making
Are firms involved in alliance headquartered in different countries
5
7
Number of annual reports needed
Overall prior years purchase requirements
Are purchases small, medium, or large
Natural log of purchasing volume
Volume purchased
Would the task be performed repetitively
Annual volume purchased
Annual volume purchased
Annual volume purchased
Compared to other large customers, this customers order frequency is lower to
higher
Volume of timber on tract
Number of annual interactions between firms
Number of monthly online transactions
Frequency of customer orders
How often deliveries are received
Shipments per year
6
6
7
7
7
7
8
8
8
Average size of largest plants producing half of industry output
Firm concentration ratios
Is decision based on cost advantage or advantage of external partner
Market share
Total assets
Advantage in scale in operations
Advantages that arise from manufacturing in-house
Firm concentration ratios
Natural log of firms cumulative production capability
Natural log of total assets
Units produced per year
Number of hospital beds
Number of people in sales force
Average plant capacity/firms total required input capacity
Overhead cost to sales ratio
Production cost to sales ratio
Comparative production costs
Is it more expensive to produce internally or externally
6
6
6
6
6
7
7
7
7
7
7
8
8
8
8
8
9
9
Uncertainty-Not Classified5
Salmond, 1987
Gulati, 1995
Frequency
Widener & Selto, 1999
Stump, 1995
Leffler & Rucker, 1991
Buvik & Gronhaug, 1999
Buvik & John, 2000
Masters & Miles, 2002
Andersen & Buvik, 2001
Buvik & Haugland, 2005
McInnes, 1999
Orr, 1998
Leffler & Rucker, 1991
Parkhe, 1993
Rasheed & Geiger, 2001
Weiss & Anderson, 1992
Heide & Miner, 1992
Bienstock & Mentzer, 1999
8
8
9
9
9
9
9
Production Costs
Kobrin, 1991
Harrigan, 1985
Gebelt, 1992
Harrigan, 1985
Belderbos, 2002
Walker & Weber, 1984
Walker & Weber, 1984
Balakrishnan & Wernerfelt, 1986
Leiblein & Miller, 2003
Dunbar & Phillips, 2001
Bigelow, 2004
Coles & Hesterly, 1998
John & Weitz, 1989
Fan, 1996
D'aveni & Ravenscraft, 1994
D'aveni & Ravenscraft, 1994
Walker & Weber, 1987
Pouder, 1994
73
TABLE 5 – continued
Table Endnotes:
1
Experts received descriptions of sample measures used in each primary study. If two of the
three experts classified the measure as one type of asset specificity (e.g., site versus physical), it
was categorized accordingly.
2
Content Validity Rating is the sum of the three experts’ ratings regarding a primary study’s
sample measure. Experts could rate a measure’s content validity as low=1, medium=2, or
high=3. Only those measures that received combined scores of six or higher were used in the
meta-analyses. Notably, the post hoc tests examining effect size differences between measures
with low (i.e., 4 and 5) and high (i.e., 8 or 9) validity yielded similar results. Validity rankings,
therefore, had little to no impact on the overall effect size estimates.
3
This category contains asset specificity measures that were not classified by the experts. In such
instances, the experts agreed that measure was a valid proxy for asset specificity in general, but
they could not categorize the type of asset specificity. If these measures received a content
validity rating of six or higher, they were included in the meta-analyses (i.e., H1, H7, H8, H9,
H10, H11, and H12).
4
Although the experts did not classify the advertising to sales ratio as a measure of brand assets,
these correlations were included in the effect size estimate for Hypotheses 5a-d because the
measure is widely used as a proxy for brand asset specificity in the literature (e.g., Gatignon &
Anderson, 1988; Lu, 2002; Tan & Vertinsky, 1996).
5
Because I do not predict that uncertainty in general is positively related to the degree of
integration in this dissertation, the correlations associated with these measures are not included in
the results.
meet reasonable standards (Geletkanycz, 1997; Kogut & Singh, 1988). Countries receiving
scores of 70 or lower were considered collectivistic, and those receiving scores of 80 or higher
were considered individualistic.
Primary studies were also coded into subgroups based upon the level of property rights
protection (i.e., weak or strong) provided in countries from which the sample was drawn (Delios
& Beamish, 1999). Following Oxley (1999), intellectual property protection scores were drawn
from Ginarte & Park’s (1997) index of patent rights. Thus, once a study’s data source country
was coded, subgroups were developed according to the level of property rights protection
provided by regulatory authorities within that country.
74
TABLE 6 – Degree of Integration Measures
Construct
Primary Studies
Measure
Market /
Hierarchy
Affuah, 2001; Anderson & Coughlan, 1987; Anderson et
al., 2000; Aulakh & Kotabe, 1997; Balakrishnan &
Wernerfelt, 1986; Bergh & Lawless, 1998; Bienstock &
Mentzer, 1999; Bigelow, 2004; Coles & Hesterly, 1998;
D'Aveni & Ravenscraft, 1994; Dhanaraj & Beamish,
2004; Dragonetti et al., 2003; Dunbar & Phillips, 2001;
Fan, 1996; Gebelt, 1992; Harrigan, 1985; Heide & John,
1992; John & Weitz, 1989; Kobrin, 1991; Lee, 1998;
Leiblein & Miller, 2003; Leiblein et al., 2002; Lu, 2002;
Masten et al., 1989; Masters & Miles, 2002; Mayer &
Nickerson, 2003; Mayer, 1999; McGee et al., 1995;
McInnes, 1999; Nicholls-Nixon & Woo, 2003; Oxley,
1999; Penner-Hahn, 1998; Pouder, 1994; Rasheed &
Geiger, 2001; Russo, 1992; Schilling & Steensma, 2002;
Shrader, 2001; Steensma & Corley, 2001; Sutcliffe &
Zaheer, 1998; Tan & Vertinsky, 1996; Walker & Weber,
1984; Walker & Weber, 1987; Weiss & Anderson, 1992;
Widener & Selto, 1999; Zahra & Nielsen, 2002
Andersen & Buvik, 2001; Artz & Brush, 2000; Blodgett,
1992; Buvik & Andersen, 2002; Buvik & Gronhaug,
1999; Buvik & Haugland, 2004; Buvik & John, 2000;
Chelariu, 2002; Cusumano & Takeishi, 2001; Dickson,
1997; Dwyer & Welsh, 1985; Fink, 1995; Gainey &
Klass, 2003; Heide & John, 1990; Heide & Miner, 1992;
Johnson, 1988; Joshi & Campbell, 2003; Joshi & Stump,
1999; Kotabe et al., 2003; Lassar & Kerr, 1996; Leffler
& Rucker, 1991; Mayer, 1999; McNally & Griffin, 2004;
Mesquita, 2002; Mitchell & Singh, 1996; Orr, 1998;
Osborn & Baughn, 1990; Parkhe, 1993; Poppo & Zenger,
2002; Reuer & Arino, 2002; Reuer et al., 2002; Salmond,
1987; Sawhney et al., 1999; Shane, 1998; Sriram et al.,
1992; Steensma., 2000; Stump, 1995; Subramani &
Venkatraman, 2003; Subramani, 1997; Zaheer &
Venkatraman, 1994; Zaheer & Venkatraman, 1995
Vertically integrated (1) or not (0); Integrated distributors versus independent arrangement; Insource (1) or outsource
(0); Survey measure from distributors to WOS; Value-added to sales ratio; Whether more businesses were acquired (>1)
then divested (<1); Common carrier vs. private/leased delivery; Makes or purchases component; Make or buy; Total
internal $ transfers; Degree of stock ownership; Ratio of outsourced activities to total sales; Ratio of outsourced
activities to total spending on activity; Owns input plant or otherwise; Inhouse development vs. purchase from vendor
with no customization; Percentage of internal transfers by competitors; Percentage of buyers requirements produced
internally; Percentage of salary in total compensation; Percentage of sales that are intrafirm; Market governance or
M&A; To make or not to make; Contractual relationship vs. full ownership; Wholly owned entry (95% equity) or less;
Percentage of company's component needs procured by firm; Was position filled with internal candidate or temp; Use of
employees versus independent contractors; Subcontractor versus company personnel; Use of cooperative agreements
with other firms; Contract out or not; Percentage of internal vs. external R & D; Contracts vs. equity arrangements;
Sponsored collaborative relationships vs. controlled internal research; Percentage of a service that is privatized;
Outsourced vs. internalized; Equity ownership of subsidiaries; Licensing vs. acquisition; From exporting to wholly
owned subsidiary; License (0) vs. acquisition (1); Expand inhouse or outsource; Manufacturing and operating costs
divided by sales less operating profit; Make (0) or buy (1); Make or buy; Will firm convert distribution agreements to
direct salesforce; Proportion of outsourced to total spending on function; Firm's use of outsourcing and licensing.
Market /
Hybrid
Hybrid /
Hierarchy
1
Belderbos, 2003; Brouthers, 2002; Brouthers et al., 2003;
Combs & Ketchen, 1999; Delios & Beamish, 1999;
Girlea, 2001; Gulati, 1995; Hughes, 1999; Mutinelli &
Piscitello, 1998; Nickerson et al., 2001; Reuer, 2001;
Takeishi, 2001
Extent of interfirm coordination; Extent of information sharing and assistance with supplier; Is equity share equal or
unequal; Extent of interfirm activity, resource, and information flows; Level of interfirm coordination (info exchange,
cooperation); Extent of formalized interfirm scheduling; Information exchange frequency and joint efforts; Are conflict
settlements, monitoring, and interfirm flows formalized; Contract length; Problems are treated jointly by both parties;
Agreement based versus equity alliances; Mean of level of buyer-supplier formalization, participation, centralization,
and specialization; Composite of exchange mutuality and solidarity; Level of collaboration; Extent to which parties take
actions together or unilaterally; Anticipated future interactions (long vs. short); Composite of amount of supplier control,
frequency of communication, and the need for supplier approval; Mutual understanding is basis for decisions with
supplier; Extent of involvement in each others' operations; Number of years in relationship; Extent of coordination and
support with distributors; Is payment to supplier lump sum or per unit; Duration of buyer-supplier contract; Extent to
which parties take actions together or unilaterally; Equity position; Number of collaborative agreements established;
Mean of quasi integration and electronic integration; Agreement vs. joint venture; Intended alliance duration; Do both
parties share a short- and long-term plan; Level of equity investment; If firm had a minority equity position in partner;
Non-collaborative to collaborative; How many years has company dealt with supplier; Length of franchise agreement;
Market versus close collaborative ties with supplier; Are alliances used no (0) yes (1); Number of months in
relationship; Percentage of sales that customer accounted for and joint decision making; Joint decision making,
accommodation, and responses; Are referrals through a proprietary system; Extent that parties take actions together or
unilaterally
Majority stake acquisition or not; Wholly owned vs. joint ventures; Use of joint ventures vs. wholly owned subsidiaries;
Franchise vs. company owned outlets; Equity investment versus wholly owned subsidiary; Activity performed through
cybermediary or own web site; Did alliance use equity or not; Alliance versus equity investment; Percentage equity
ownership; Contracting versus equity arrangement; Percent of JV equity investment; Degree of stock ownership.
The measures are listed in order of the primary studies.
75
Meta-Analytic Procedures
To conduct the meta-analyses, software provided by Schwarzer (1992) that follows
guidelines and formulae offered by Hunter and Schmidt (1990) was used. These formulae correct
for sampling and measurement error and permit researchers to aggregate primary studies’ effect
size estimates to derive an overall sample size weighted effect size estimate (i.e., correlation). By
aggregating effect size estimates and thereby removing sampling error, seemingly contradictory
empirical results can be resolved. In the present study, meta-analysis made it possible to estimate
the size of the relationship between TCT’s core transaction attributes and the degree of
integration. These techniques are described next, followed by a description of how the theorydriven moderator hypotheses were tested.
Effect size estimates for main effects. There were four main steps used to test main
effects, including: 1) calculating a sample size weighted effect size estimate for each main effect,
2) examining effect size variance among studies, 3) creating confidence intervals, and 4)
correcting effect sizes for measurement error.
The first step was to calculate an effect size estimate for each main effect. To calculate
effect sizes, each correlation was weighted by the primary study’s sample size (Hunter &
Schmidt, 1990). This allowed for the estimation of the sample size weighted mean correlation
( r ) across all of the studies included in the meta-analysis (Hunter & Schmidt, 1990). This
estimate is more accurate than those obtained from any one study because positive and negative
sampling errors cancel out (Hunter & Schmidt, 1990). In short, by aggregating effect size
estimates from primary research, meta-analysis increases the sample size, which reduces
sampling error. This notion is based on the central limit theorem, which asserts that the size of
errors due to sampling error shrinks as the sample size grows. Sampling errors disappear entirely
when the population is investigated (Hunter & Schmidt, 1990).
To compute the sample size weighted mean correlation ( r ) for the relationship between
asset specificity and the degree of integration, for example, each correlation was weighted by the
primary study’s sample size. This correlation was averaged to derive an effect size estimate for
the relationship according to the formula: r = ∑ [n r ] / ∑ n , where ni was the sample size and
i i
i
76
ri was the correlation reported for the ith study. This procedure was repeated for other
hypothesized main effects.
The second step was to examine variance in effect size estimates among studies. Variance
in effect size estimates can arise from artifactual variance, such as sampling error, or from
systematic effects, such as variables that moderate the relationship of interest (Hunter &
Schmidt, 1990). Meta-analytic techniques allow researchers to determine if significant variance
remains after accounting for artifactual variance. To test whether the effect size variance was
more than expected by chance, the following formula was used: χ K2 −1 =
T
s r2 , where K
(1 − r 2 ) 2
was the number of effects, T was the total sample size from primary studies, and s r2 was the
observed variance of r . If chi-square ( χ 2 ) was statistically significant, it suggested that there
was significant variance in the population relationship, which is due to either unaccounted for
artifactual variance or true variance from a moderating effect.
After examining effect size (i.e., correlation) variance among studies, a review of the
sample studies was conducted to ensure that outliers were not driving large positive or negative
effect size estimates. Hunter and Schmidt (1990) assert that estimates can be deleted if they are
extreme values and greatly influence the overall effect size estimate and the amount of variance.
Given this, after the initial effect size and variance estimates were complete, study-level effect
sizes were sorted using disjoint cluster analysis to determine whether an effect size was extreme
(Schwarzer, 1992). The outliers were removed and the data was reanalyzed to determine if the
relationships of interest still held (Dalton & Dalton, 2005). Accordingly, results are presented
with and without outliers, and any material differences in the results are discussed in chapters
five and six.
The third step was to calculate confidence intervals to determine if effect size estimates
differ from zero (Whitener, 1990). If the χ 2 statistic is not significant, effect size variance among
studies is small (i.e., homogeneous case), and all variance is assumed to result from sampling
error, not moderating effects. Sampling error variance is calculated: σ e2 =
77
(1 − r 2 ) 2
, where N =
( N − 1)
T / K. The standard error of sampling error variance: σ e2 / K was used to create confidence
intervals for the homogeneous case. If significant effect size variance remains unexplained (i.e.,
heterogeneity), a wider confidence interval was calculated based on the standard error of the total
effect size variance, i.e., σ r2 / K (Whitener, 1990).
The fourth and final step involved with examining main effects is correcting for
measurement error (Hunter & Schmidt, 1990). The extent of measurement error contained in a
measure is reflected in its reliability coefficient, and unreliable measures systematically reduce
the size of correlations in primary studies. Hunter and Schmidt (1990), for example, assert that if
a measure is only 80% reliable (i.e., α = .8), then 80% of the variance is due to the true score,
and 20% is due to measurement error. But this 20% error causes a systematic reduction (i.e.,
attenuation) of relationships found in primary studies. Because of this, when internal consistency
(i.e., reliabilities) are available, they will be collected. If reliabilities are not reported, the mean
of the available reliabilities will be used to correct r according to: rc =
r
rxx ryy
, where r c is
the corrected effect, r xx and r xy represent the average reliability estimates for the independent
and dependent variables respectively (Hunter & Schmidt, 1990). Thus, r c shows the estimated
effect size if measurement error from unreliable measures did not exist in primary studies,
thereby providing a more accurate effect size estimate.
Hypothesis tests procedures. To test main effect hypotheses, the method described
above was used. Specifically, for each predicted main effect (H1, H2a, H2b, H3), such as the
relationship between asset specificity and the degree of integration (H1), r was computed using
the average correlation in each study between each independent variable measure (e.g., asset
specificity) and measures of the degree of integration (e.g., market versus hierarchy). Further, a
homogeneity of variance test χ 2 was conducted and confidence intervals were drawn. A
confidence interval that contains zero indicates no population effect. The final step was to correct
for measurement error to improve the effect size estimate.
There are two ways that moderator hypotheses were tested – assessing correlations
between different transaction attribute measures (e.g., environmental versus behavioral
78
uncertainty) and subgroupings of primary studies based on the nature of the sample (e.g., single
industry versus multiple industry sample). The first method involved examining correlations that
show differences in r for the transaction attributes of interest. For example, to test Hypothesis 6,
two separate meta-analyses were conducted on the correlations between the degree of integration
and 1) environmental uncertainty and 2) behavioral uncertainty. The first analytical step was to
produce r for the relationship between environmental uncertainty and the degree of integration.
The second step was to produce an r for the relationship between behavioral uncertainty and the
degree of integration. The third step was to create confidence intervals around each r . If the
confidence intervals overlapped, there was no support for the moderating effect.
Hunter and Schmidt (1990) also content that moderating effects can be examined by
creating subgroupings of primary studies based on the nature of the sample (e.g., single industry
versus multiple industries). Once subgroupings were created, the first analytical step was to
produce r for the relationship of interest for each subgroup. The second step was to determine if
r is different across subgroups. For example, effect size estimates among transaction attributes
and the degree of integration are predicted to vary based on whether a study used a single
industry sample or a multiple industry sample (i.e., H9). Thus, primary studies were grouped by
the nature of the sample, and effect size estimates were examined for each subgroup.
If effect size estimates differed 1) between r for different transaction attributes (e.g.,
environmental or behavioral uncertainty) and/or 2) across primary study subgroups (e.g.,
industry or country), there is some evidence for a moderating effect. Moreover, if the percentage
of variance explained by artifacts within studies is reduced, this also suggests the presence of
moderating effects. Thus, if there are significant effect size differences between the r within
studies or across subgroups, and effect size homogeneity increases when subgroups are created,
moderating effects are present (Hunter & Schmidt, 1990). Further, confidence intervals were
created around the effect size estimates to ensure that they did not overlap. Taken together,
differences within or across studies and subgroups, increased homogeneity of variance within
subgroups, and the presence of non-overlapping confidence intervals provide strong evidence of
moderating effects.
79
Human (H4a-d) and brand (H5a-d) assets are predicted to be more strongly related to the
degree of integration than other types of asset specificity. To conduct these analyses, the r
between human assets and the degree of integration across studies was calculated. Then, metaanalyses were conducted on the average of correlations between all other asset specificity
measures (e.g., physical, dedicated, site, and temporal assets) and the degree of integration. The
latter r was then compared to r for human asset specificity. If the effect size was larger for the
human asset specificity r , if there was greater homogeneity of variance within subgroups, and if
the confidence intervals did not overlap, Hypotheses 4a-d is supported. This procedure was
repeated for the brand asset specificity hypotheses (H5a-d).
As noted above, one approach for testing moderator hypotheses is to create subgroups of
studies based on the moderating variable of interest. In the present study, the nature of firms in
the sample was used to create subgroups. For the interactive relationship proposed between
environmental uncertainty and asset specificity (H7), for example, studies that examine the
relationships between asset specificity and the degree of integration were grouped by the nature
of industries reflected in sample. Castrogiovanni (2002) used instability of sales as an indicator
of environmental uncertainty. Following his logic, single industry studies that test the
relationship between asset specificity and the degree of integration were grouped based on
environmental uncertainty stemming from an industry’s instability of sales. Once the primary
studies’ sample industries were coded, environmental uncertainty was calculated using
COMPUSTAT data. Specifically, I regressed the sales in year x (e.g., $ 2 billion) on year x (e.g.,
1984), and divided the standard error of the regression slope by the mean industry sales for the
years under investigation (Dess & Beard, 1984). Primary studies were then grouped into low or
high uncertainty categories. The difference in r in the relationship between asset specificity and
the degree of integration was then used to test the predicted moderating effect. If r is greater for
the high uncertainty subgroup and if there is greater effect size homogeneity within the low and
high uncertainty subgroups, Hypothesis 7 would be supported. Table 7 lists the studies used to
examine Hypothesis 7 along with their respective scores and rankings for transaction uncertainty.
80
TABLE 7 – Primary Study Uncertainty Rankings Used to Test Hypothesis 7
Study
Industry Context
Fink, 1995
Masten et al., 1989
Bigelow, 2004
Walker & Weber, 1984
Walker & Weber, 1987
Takeishi, 2001
Leiblein & Miller, 2003
Leiblein et al., 2002
Bienstock & Mentzer, 1999
McGee et al., 1995
Shrader, 2001
Coles & Hesterly, 1998
Fan, 1996
Stump, 1995
Paper and Pulp
Auto Manufacturing
Auto Manufacturing
Auto Manufacturing
Auto Manufacturing
Auto Manufacturing
Circuit Manufacturing
Circuit Manufacturing
Motor Carriers
High Tech Manufacturing
High Tech Manufacturing
Hospitals
Chemicals
Chemicals
Data Years Uncertainty
Score
1983-1992
0.84
1983-1988
0.94
1983-1999
1.25
1983-1999
1.25
1983-2000
2.25
1989-1998
1.30
1987-1996
1.31
1987-1996
1.31
1986-1995
1.32
1983-1990
1.38
1983-1990
1.38
1983-1991
1.45
1983-1994
1.60
1983-1994
1.60
Rank
30
29
28
27
26
25
24
23
22
19-21
18
17
16
15
Zaheer & Venkatraman, 1994 Insurance
1983-1992
1.87
14
Girlea, 2001
Anderson & Coughlan, 1987
Afuah, 2001
Mutinelli & Piscatello, 1998
Nicholls-Nixon & Woo, 2003
Mayer & Nickerson, 2003
Weiss & Anderson, 1992
Hughes, 1999
Lassar & Kerr, 1996
Combs & Ketchen, 1999
Mayer, 1999
Reuer et al., 2002
1992-2001
1983-2001
1985-1994
1985-1994
1983-1991
1990-1999
1983-1990
1987-1997
1984-1993
1987-1996
1986-1998
1985-1994
1.91
1.96
2.14
2.23
2.27
2.47
2.48
2.57
2.73
2.87
3.41
3.56
13
12
11
10
9
8
7
6
5
4
2-3
1
Semiconductor
Semiconductor
Computer Manufacturing
Mining
Pharmaceuticals
Information Technology
Electronic Components
Pharmaceuticals
Stereo Speaker
Restaurants
Biotechnology
Biotechnology
Low or High
Uncertainty
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Low
Midpoint
Removed
High
High
High
High
High
High
High
High
High
High
High
High
In a similar vein, the same procedure was used to test the interactive relationship between
frequency and asset specificity (H8). The only exception was the method by which subgroupings
were created. To test this hypothesis, the expert panel was asked to create subgroups according
to those studies that sample low versus high frequency transactions. To do this, experts were
provided with the primary study’s data source, industry context, and the nature of the transaction.
For Masten et al. (1989), for example, the following information was provided: the data source
was a managerial survey, the context was the automobile industry, and the nature of the
transaction was component procurement. Because automobile component inputs, such as steel,
81
must be procured on an ongoing basis, the experts classified this transaction as “frequent” as
opposed to “infrequent.” Table 8 contains the primary studies and the experts’ transaction
frequency classifications. These studies’ effect size estimates were used to test Hypothesis 8. It is
supported if r is greater for the high frequency subgroup and if there is greater effect size
homogeneity within each subgroup.
TABLE 8 – Primary Study Transaction Frequency Classifications Used to Test Hypothesis 8
Studies
Barthelemy & Quelin, 2002; Brouthers, 2002; Combs & Ketchen, 1999; Fan, 1996; Heide & John, 1992; Lu,
2002; Orr, 1998; Pouder, 1994; Reuer, 2001; Shane, 1998; Tan & Vertinsky, 1996
Low or
High
Frequency
Low
Affuah, 2001; Affuso, 2002; Andersen & Buvik, 2001; Anderson & Coughlan, 1987; Anderson et al., 2000;
Argyres & Silverman, 2004; Artz & Brush, 2000; Aulakh & Kotabe, 1997; Belderbos, 2002; Bienstock &
Mentzer, 1999; Bigelow, 2004; Brouthers et al., 2003; Buvik & Andersen, 2002; Buvik & Gronhaug, 1999;
Buvik & John, 2000; Buvik & Reve, 2001; Coles & Hesterly, 1998; D'Aveni & Ravenscraft, 1994; Delios &
Beamish, 1999; Dhanaraj & Beamish, 2004; Dragonetti et al., 2003; Dunbar & Phillips, 2001; Fink, 1995;
Gainey & Klass, 2003; Gebelt, 1992; Girlea, 2001; Heide & John, 1990; Hughes, 1999; John & Weitz, 1989;
Joshi & Campbell, 2003; Joshi & Stump, 1999; Kobrin, 1991; Lassar & Kerr, 1996; Leiblein & Miller, 2003;
Leiblein et al., 2002; Masten et al., 1989; Masters & Miles, 2002; Mayer & Nickerson, 2003; Mayer, 1999;
McGee et al., 1995; Mesquita, 2002; Mutinelli & Piscitello, 1998; Nicholls-Nixon & Woo, 2003; Osborn &
Baughn, 1990; Penner-Hahn, 1998; Poppo & Zenger, 2002; Rasheed & Geiger, 2001; Reuer & Arino, 2002;
Reuer et al., 2002; Robertson & Gatignon, 1998; Salmond, 1987; Sawhney et al., 1999; Schilling & Steensma,
2002; Shrader, 2001; Stump, 1995; Subramani & Venkatraman, 2003; Subramani, 1997; Takeishi, 2001;
Walker & Weber, 1984; Weiss & Anderson, 1992; Widener & Selto, 1999; Zaheer & Venkatraman, 1994;
Zaheer & Venkatraman, 1995; Zahra & Nielsen, 2002
High
Slightly different procedures were used to assess the impact of industry and country
influences on TCT’s predicted relationships. For industry influences, subgroups were based on
whether the primary study sampled one or more industries. Meta-analyses were run to estimate
effect size between transaction attributes and the degree of integration for each subgroup (i.e.,
one industry versus two or more industries). If there are differences among the effect size
estimates produced and there is greater effect size homogeneity within the subgroups,
Hypothesis 9 would be supported.
The procedure that was used to test Hypothesis 10, which predicts that cultural
collectivism moderates the relationships between transaction attributes and the degree of
integration, is similar to the subgrouping procedure used to examine hypotheses 7 and 8. To test
82
Hypothesis 10, however, studies were grouped based on whether the country from which the
sample was drawn was viewed as either individualistic (low) or collectivistic (high) using
Hofstede’s (2001) scores. If the subgroup that is more collectivistic has a lower r for the
relationship between measures of transaction attributes and the degree of integration, and if there
is greater effect size homogeneity within subgroups, Hypothesis 10 would be supported.
Similarly, this procedure was repeated to test the strong versus weak property rights protection
(H11), but the subgroupings differed based on property rights protection as suggested by Ginarte
and Park (1997). Table 9 lists the countries identified in primary studies. This table contains each
country’s individualism/collectivism and intellectual property protection scores and
classifications.
TABLE 9 – Collectivism and Property Protection Rankings Used for Hypotheses 10 and 11
Number of
Primary
Studies
7
1
4
1
1
7
1
60
Country
Japan
Spain
Norway
France
Italy
Canada
UK
US
Collectivism
Score1
46
51
69
71
76
80
89
91
Low/High Intellectual Property Low/High
Collectivism
Protection Score2
Protection
High
3.94
Low
High
3.62
Low
High
3.29
Low
3.9
Low
Omitted3
Omitted
4.05
High
Low
2.76
Low
Low
2.99
Low
Low
4.52
High
Table Notes:
1
Collectivism scores were drawn from Hofstede (2001).
2
Following Oxley (1999), intellectual property protection scores were drawn from Ginarte &
Park’s (1997) index of patent rights. The highest recorded index was 4.52, and thus considered
high.
3
In an effort to dichotomize low and high collectivism scores, primary studies with collectivism
scores in the 70s were not included in the meta-analyses for H10.
To examine whether matching transaction attributes (e.g., asset specificity) to the
appropriate degree of integration impact performance (H12), two analytic steps were taken. The
first step was to create subgroups for studies producing low versus high effect size estimates
between asset specificity and the degree of integration. Because the mean r was .08 for the
83
relationship between asset specificity and the degree of integration, unmatched and matched
groups were those reporting correlations below and above .08, respectively. The second step was
to examine how these differences shaped performance. Thus, meta-analyses were conducted to
test whether the r between the degree of integration and performance differed based on the
unmatched versus matched subgroup. If the effect size is greater for the “matched” modes,
Hypothesis 12 would be supported.
SUMMARY
This chapter outlined the method used to examine the relationships outlined in chapter
three. Specifically, the chapter explains the sample and coding procedures, as well as the metaanalytic procedures used to examine main and moderating effects.
84
CHAPTER 5: RESULTS
Table 10 contains results for each hypothesis. Because different studies examined
different TCT relationships (e.g., asset specificity and the degree of integration or environmental
uncertainty and the degree of integration), there is a different number of studies and
corresponding N size for each hypothesis test. The table lays out the: hypothesis number, number
of total transactions sampled (N), number of studies (K) that examined the hypothesized
relationship, observed effect size estimate or weighted average correlation (r ) , corrected effect
size estimate (rc ) , observed variance of the effect size (σ r2 ), effect size variance attributable to
sampling error ( σ e2 ), residual variance after subtracting variance due to measurement and
2
), percentage of variance attributable to measurement or sampling error,
sampling error ( σ Residual
chi-square ( χ 2 ) statistic, and 95 and 90 percent confidence intervals.
Hypothesis 1, which predicted that asset specificity is positively related to the degree of
integration, was supported with r = .080 (p < .01). Sampling and measurement error explain
only 13 percent of the variance and χ K2 −1 = 629.07 (p < .001). Hypothesis 2a predicted that
environmental uncertainty is positively related to the degree of integration. This hypothesis was
not supported; the effect size estimate was not different from zero with r = -.009 (ns).
Hypothesis 2b predicted that behavioral uncertainty is positively related to the degree of
integration. This effect size estimate was also not significant, thus the hypothesis was not
supported ( r = -.021, ns). Hypothesis 3 predicted that transaction frequency is positively related
to the degree of integration. This hypothesis was not supported ( r = .065, ns).
Hypotheses 4a, 4b, 4c, and 4d predicted that human asset specificity is more strongly
related to the degree of integration than site, physical, dedicated, and temporal assets. These
hypotheses were not supported. The effect size between human asset specificity and the degree
of integration was not stronger than the estimates for the relationship between physical asset
specificity ( r = .087 versus r = .072, ns) or site asset specificity ( r = .087 versus r = .103, ns)
and the degree of integration. Further, the effect size estimates for dedicated ( r = .087 versus r
= .215, p < .01) and temporal ( r = .087 versus r = .267, p < .01) asset specificity were larger
than the human asset specificity estimate, which is opposite of the predictions.
85
TABLE 10 – Hypothesis Test Results
Findings
H1-Asset Specificity (AS)
H2a-Envir. Uncertainty (Un)
H2b-Behavioral Un
H3-Frequency (Fr)
H4/5-Site AS
H4/5-Physical AS
H4/5-Human AS
H4/5-Brand AS
H4/5-Dedicated AS
H4/5-Temporal AS
H7-AS & Low Un
H7-AS & High Un
H8-AS & Low Fr
H8-AS & High Fr
H9-Single Industry
H9-Multiple Industry
H10-Individualistic
H10-Collectivistic
H11-High IPP
H11-Low IPP
H12-Unmatched
H12-Matched
Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Not Supported
Supported
Supported
N
35981
23003
4005
2565
486
7261
6066
20277
4455
451
6726
3048
1557
30534
12317
28415
26650
17173
25423
18532
5247
2080
K
77
44
26
18
3
18
21
16
25
3
15
13
8
57
41
31
63
12
56
20
9
11
Obs. r Corrected Observed
r
Variance
0.080
-0.009
-0.021
0.065
0.103
0.072
0.105
0.067
0.215
0.267
0.050
0.072
0.145
0.067
0.061
0.051
-0.008
0.101
-0.013
0.101
-0.039
0.141
0.103
-0.011
-0.026
0.083
0.133
0.092
0.135
0.086
0.276
0.344
0.064
0.093
0.187
0.086
0.078
0.065
-0.010
0.130
-0.017
0.130
-0.050
0.181
Sample Residual Percent
Chi Signif of
Error
Variance Artifact Square Chi Sq.
Variance
Variance
0.017
0.015
0.042
0.033
0.005
0.023
0.020
0.010
0.027
0.045
0.012
0.028
0.022
0.016
0.020
0.009
0.014
0.003
0.014
0.004
0.024
0.013
0.00211
0.00191
0.00649
0.00696
0.00604
0.00245
0.00339
0.00078
0.00511
0.00574
0.00222
0.00422
0.00492
0.00185
0.00330
0.00109
0.00236
0.00068
0.00220
0.00106
0.00171
0.00508
86
0.015
0.013
0.036
0.026
-0.002
0.021
0.017
0.009
0.022
0.039
0.010
0.024
0.017
0.014
0.016
0.008
0.012
0.003
0.011
0.003
0.022
0.008
12.92%
13.43%
15.62%
21.35%
135.68%
11.12%
17.40%
8.93%
19.09%
13.06%
19.31%
15.25%
23.30%
12.15%
17.31%
13.96%
17.65%
23.32%
17.02%
30.41%
7.62%
41.20%
629.07
347.58
169.65
85.77
2.25
169.59
125.08
206.18
134.18
23.46
81.90
87.64
35.14
499.72
245.19
247.66
374.54
60.21
346.51
73.29
126.36
27.32
0.00%
0.00%
0.00%
0.00%
32.40%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.20%
95% CI
95% CI
90% CI
90% CI
0.051
-0.045
-0.100
-0.019
0.015
0.001
0.044
0.018
0.150
0.027
-0.006
-0.019
0.043
0.034
0.018
0.018
-0.037
0.068
-0.044
0.073
-0.140
0.075
0.109
0.028
0.059
0.149
0.191
0.142
0.165
0.116
0.280
0.507
0.105
0.164
0.247
0.100
0.104
0.084
0.022
0.134
0.017
0.128
0.063
0.207
0.056
-0.039
-0.087
-0.006
0.030
0.013
0.054
0.026
0.161
0.067
0.003
-0.004
0.060
0.039
0.025
0.023
-0.032
0.073
-0.039
0.078
-0.123
0.085
0.105
0.022
0.046
0.135
0.177
0.130
0.155
0.108
0.269
0.468
0.096
0.149
0.231
0.094
0.097
0.078
0.017
0.129
0.012
0.124
0.046
0.197
Hypotheses 5a, 5b, 5c, and 5d predicted that brand asset specificity is more strongly
related to the degree of integration than site, physical, dedicated, and temporal assets. The effect
size estimate for the relationship between brand asset specificity and the degree of integration
was not stronger than the estimates for the relationship between physical asset specificity ( r =
.067 versus r = .072, ns) or site asset specificity ( r = .067 versus r = .103, ns) and the degree
of integration. Moreover, the effect size estimates for dedicated ( r = .067 versus r = .215, p <
.01) and temporal ( r = .067 versus r = .267, p < .01) asset specificity were larger than the
brand asset specificity estimate, also the opposite of what was predicted. Therefore, these
hypotheses were not supported.
Hypothesis 6 predicted that behavioral uncertainty is more strongly related to the degree
of integration than environmental uncertainty; it did not receive support with r = -.027 versus r
= -.018 (ns). Hypothesis 7 predicted that the relationship between asset specificity and the degree
of integration would be moderated by environmental uncertainty. This hypothesis was not
supported. The effect size estimate for the relationship between asset specificity and degree of
integration for transactions occurring in low uncertainty environments was r = .052, whereas
the estimate for the relationship between asset specificity and the degree of integration for
transactions in high uncertainty environments was r = .072 (ns). Hypothesis 8 predicted that the
relationship between asset specificity and the degree of integration would be moderated by
transaction frequency. This hypothesis was also not supported. The effect size estimate for
transactions categorized by the expert panel as infrequent was r = .145 whereas the effect size
estimate for transactions categorized as frequent was r = .067 (ns).
Hypothesis 9 predicted that the relationship among transaction attributes (i.e., asset
specificity, uncertainty, and frequency) and the degree of integration is more similar within
industries than between industries. Because effect size estimates did not differ between single
industry and multiple industry ( r = .061 versus r = .051, ns) studies, this hypothesis was not
supported. Hypothesis 10 was also not supported. This hypothesis predicted that the relationship
among transaction attributes and the degree of integration is stronger in countries possessing
more individualistic than collectivistic cultures. The data indicate that the opposite is true; the
effect size estimate for studies with samples from countries classified as individualistic ( r = .008) was smaller than the estimate for studies with samples from countries classified as
collectivist (versus r = .101, p < .01).
87
Hypothesis 11 predicted that the relationship among transaction attributes and the degree
of integration is stronger in countries offering low, rather than high, intellectual property
protection. This hypothesis was supported. The effect size estimate for studies with samples from
countries classified as those offering low intellectual property protection was r = .101 whereas
the estimate for high intellectual property protection was r = -0.013 (p < .01).
Hypothesis 12 predicted that matching transactions to the appropriate degree of
integration improves firm performance. ‘Unmatched’ and ‘matched’ transactions, respectively,
were classified according to whether a primary study’s correlation between asset specificity and
degree of integration was below or above the overall effect size estimate for the relationship
between asset specificity and the degree of integration ( r = .081). The effect size estimates for
‘unmatched’ transactions involving asset specific investments was larger than the effect size
estimate for ‘matched’ transactions ( r = -.039 versus r = .141, p < .01), offering support for
Hypothesis 12.
Post Hoc Test Results
In order to examine the robustness of the results, I conducted several post hoc tests. Each
post hoc test result is outlined in Table 11.
All but one chi square statistic ( χ 2 ) suggested that effect sizes (r) across studies were
heterogeneous. One potential source of heterogeneity is an outlier study, thus the first type of
robustness test involved removing outliers for each hypothesized main effect (i.e., H1, H2a, H2b,
and H3) and reexamining the overall effect size estimates ( r ) (Dalton & Dalton, 2005).
Accordingly, study level effect sizes (r) were rank ordered and sorted using disjoint cluster
analysis (Mullen & Rosenthal, 1985; Schwarzer, 1992). If a study’s effect size (r) differed at a
critical value at .05 significance from the effects of other studies, it was considered an outlier.
Outlier studies were then removed and each hypothesis retested. The post hoc test results
indicated that, although the variability for effect size estimates across studies fell, the effect size
estimates for non-outlier studies did not change significantly, and thus, outliers did not affect the
results for H1, H2a, H2b, and H3.
The second type of robustness test involved reexamining effect sizes based on the
experts’ content validity ratings. The motivation for this test is that meta-analysis corrects for
reliability but not for validity. Accordingly, I separated measures into groups low (i.e., 4 and 5)
and high (i.e., 8 or 9) overall content validity ratings, and I ran post hoc tests to compare effect
88
size estimates. There were no extreme frequency groupings because no measures received low
validity ratings. Further, because experts assigned low validity ratings for only two
environmental uncertainty measures, findings related to these two measures could be entirely
artifactual. Thus, I only tested the robustness of findings related to asset specificity (i.e., H1) and
behavioral uncertainty (i.e., H2b). The results of the post hoc tests showed that the overall effect
size estimates were not affected by the experts’ ratings of a measure’s content validity. One
possible interpretation of this finding is that the experts might not have differentiated between
valid and invalid measures because TCT’s construct definitions are very broad. Stated
differently, numerous measures (e.g., fixed asset to sales ratio or R&D intensity) that have
different relationships with the degree of integration fall within the construct’s domain, and it
might be difficult to distinguish good measures from poor measures. If this is the case, efforts
directed toward reducing the conceptual domain of TCT’s constructs might have merit.
Nevertheless, content validity rankings had little to no impact on the overall effect size estimates
r.
The third type of robustness test involved examining whether main effects (i.e., H1, H2a,
H2b, H3) differed based on the type of degree of integration examined. Specifically, studies were
placed into one of three groups, those that involved the choice between: 1) market versus
hierarchy, 2) market versus hybrid, and 3) hybrid versus hierarchy. Meta-analyses were then
conducted to compare effect size estimates based on the degree of integration measure. The post
hoc tests yielded slightly different results for H1 (i.e., asset specificity) and H3 (i.e., frequency).
For asset specificity, it was found that the relationship between asset specificity and the degree of
integration is heavily influenced by studies investigating the choice between markets versus
hybrids. Specifically, the effect size estimate for the relationship between asset specificity and
the use of hybrids over markets was larger at r = .187 (p < .1) than the relationship for the
choice between markets versus hierarchies ( r = .061) or for the choice between hybrids and
hierarchy ( r = .074). Further, when studies using markets versus hierarchies and hybrids versus
hierarchies were combined, they yielded r = .063 (p < .01).
There were no studies that tested the relationship between frequency and the choice
between hybrids versus hierarchies. Yet it was found that the relationship between transaction
frequency and the degree of integration changed when studies were grouped according to: 1)
markets versus hierarchies and 2) markets versus hybrids. Specifically, transaction frequency is
89
more related to the use of hybrids over markets with r = .137 (p < .01), but not to the use of
hierarchies over markets with r = -.02 (ns). These effect sizes are not different though, which is
likely due to the number of studies involved. Nevertheless, one estimate is no longer significant
and the magnitude of the effects are quite different.
The post hoc tests thus showed that asset specificity and transaction frequency impact
degree of integration decisions, but the strength of the empirical relationship is contingent on the
type of integration (i.e., dependent measure) examined. Further, the finding regarding transaction
frequency offers mixed evidence for H3; the effect size estimate for the positive relationship
between transaction frequency and overall degree of integration was not significant.
Finally, to test the robustness of the Hypothesis 9 results (i.e., relationship between
transaction attributes and the degree of integration is more similar within than between
industries), I tested whether effect size estimates differed between manufacturing ( r = .042) and
service firms ( r = .004, ns). The results were not significant. Although this test was perhaps
more of a test between economic sectors, it was an effort to add robustness to the results.
SUMMARY
In summary, the hypothesis test results and post hoc test results offered support for only
three hypotheses (i.e., H1, H11, and H12) and mixed evidence for a fourth (i.e., H3). First, there
was evidence suggesting that asset specific investments are positively related to the degree of
integration, but the post hoc tests showed that the relationship is moderated by the type of
integration examined. Second, the results provided evidence that the level of intellectual property
protection is negatively related to the degree of integration; effect size estimates were stronger in
countries lacking such protection. Third, governing asset specific transactions by more integrated
governance modes has a stronger effect on performance. Thus, ‘matching’ transactions to the
specified degree of integration impacts performance. Fourth, the post hoc test results showed that
when the relationship between frequency and market versus hybrid is considered, frequency is
positively related to the use of hybrids over markets, but not hierarchies over markets.
90
TABLE 11 – Post Hoc Test Results
N
H1-Asset Spec. No Outliers 5%
H1-AS Mkt/Hierarchy
H1-AS Mkt/Hyb
H1-AS Hyb/Hierarchy
H1-AS Mkt/Hier and Hyb/Hier
H1-Content Validity 4 & 5
H1-Content Validity 9
H2a-Envir. Un. No Outliers 5%
H2a-Envir. Un Mkt/Hierarchy
H2a-Envir. Un Mkt/Hyb
H2a-Envir. Hyb/Hierarchy
H2b-Behav. Un. No Outliers 5%
H2b-Behav. Un Mkt/Hierarchy
H2b-Behav. Un Mkt/Hyb
H2b-Behav. Un Hyb/Hierarchy
H2b-Content Validity 4 & 5
H2b-Content Validity 8 & 9
H3-Frequency No Outliers 5%
H3-Frequency Mkt/Hierarchy
H3-Frequency Mkt/Hyb
H9-Manufacturing
H9-Service
Production Cost
AS (Economics Literature)
AS (All Other Literatures)
Dedicated AS Mkt/Hierarchy
Dedicated AS Mkt/Hybrid
K
35135
26997
5175
4146
31143
1859
8056
21654
9160
12314
2163
3541
3042
2091
330
1865
1422
2358
1181
1384
20666
11660
9605
3934
32047
601
4469
Obs. r Corrected Observed Sample
Residual Percent
Chi
Signif 95% CI 95% CI 90% CI 90% CI
R
Variance Error
Variance Artifact Square of Chi
Variance
Variance
Sq.
72
37
30
13
50
9
29
41
18
22
6
23
19
13
3
10
10
16
9
9
58
13
16
4
73
4
23
0.078
0.061
0.187
0.074
0.063
0.097
0.128
-0.025
-0.015
-0.011
0.046
-0.003
-0.017
0.045
-0.080
0.058
0.042
0.077
-0.020
0.137
0.042
0.004
0.099
0.082
0.080
-.011
.214
0.100
0.079
0.240
0.095
0.081
0.124
0.165
-0.032
-0.019
-0.014
0.059
-0.004
-0.022
0.058
-0.103
0.075
0.054
0.100
-0.026
0.176
0.053
0.005
0.128
0.105
0.103
-.014
.275
0.014
0.013
0.030
0.011
0.013
0.012
0.028
0.011
0.013
0.010
0.042
0.022
0.035
0.047
0.033
0.034
0.022
0.019
0.037
0.018
0.024
0.007
0.028
0.000
0.019
.020
.027
0.00202
0.00136
0.00540
0.00310
0.00159
0.00475
0.00348
0.00189
0.00196
0.00179
0.00276
0.00650
0.00624
0.00619
0.00897
0.00533
0.00701
0.00670
0.00761
0.00626
0.00280
0.00111
0.00163
0.00100
0.00225
.00665
.00469
0.012 15.36% 495.24 ns
0.012 11.40% 353.11 ns
0.025 18.13% 169.17 0.00%
0.007 30.67% 44.03 0.00%
0.011 13.50% 397.74 0.00%
0.007 40.41% 22.82 0.30%
0.025 12.68% 236.42 0.00%
0.009 18.84% 231.26 0.00%
0.011 15.53% 122.72 0.00%
0.009 18.37% 128.10 0.00%
0.039
6.82% 91.76 0.00%
0.015 30.30% 77.38 0.00%
0.028 18.38% 105.35 0.00%
0.041 13.51% 98.07 0.00%
0.024 27.75% 10.95 0.00%
0.029 16.07% 63.69 0.00%
0.015 32.28% 31.52 0.00%
0.012 36.63% 44.43 0.00%
0.030 20.77% 43.98 0.00%
0.012 35.15% 26.08 0.10%
0.021 12.33% 491.35 0.00%
0.005 18.87% 76.05 0.00%
0.026
6.23% 275.08 0.00%
-.001 294.74%
1.50 68.0%
0.017 12.26% 627.50 0.00%
.013 34.59% 11.76 .080%
.023 17.66% 131.61 0.00%
91
0.050
0.025
0.124
0.018
0.032
0.025
0.067
-0.057
-0.068
-0.054
-0.118
-0.064
-0.101
-0.072
-0.285
-0.056
-0.050
0.011
-0.146
0.049
0.002
-0.040
0.017
0.063
0.048
-0.148
0.146
0.105
0.098
0.249
0.130
0.094
0.168
0.189
0.006
0.039
0.031
0.211
0.057
0.066
0.163
0.125
0.172
0.134
0.144
0.106
0.225
0.081
0.048
0.181
0.101
0.112
0.126
0.281
0.055
0.031
0.135
0.028
0.037
0.037
0.077
-0.052
-0.059
-0.047
-0.091
-0.054
-0.087
-0.053
-0.251
-0.037
-0.035
0.021
-0.126
0.063
0.008
-0.033
0.031
0.066
0.053
-0.126
0.157
0.100
0.092
0.239
0.121
0.089
0.157
0.179
0.001
0.030
0.024
0.184
0.047
0.053
0.144
0.091
0.154
0.119
0.133
0.085
0.210
0.075
0.040
0.168
0.098
0.107
0.104
0.270
CHAPTER 6: DISCUSSION AND CONCLUSION
The present study began by acknowledging the considerable ambiguity surrounding the
empirical evidence supporting some of TCT’s key theoretical predictions. This ambiguity,
together with criticisms of TCT (e.g., Ghoshal & Moran, 1996; Perrow, 1986) and more recent
theoretical developments (e.g., real options theory), suggested that TCT might not be an
empirical success story and thus, does not deserve its prominent status in governance choice
research. These results shed light on the aggregate evidence offered by extant research.
Therefore, this study takes a step toward resolving the ambiguity in the literature and thus begins
to answer the question of whether TCT deserves the prominent role it has attained. Although the
results provide clarity in some areas, they indicate that several important theoretical questions
remain unresolved. Before discussing the implications of the study's findings and describing the
avenues they open for future research, however, I examine some of the dissertation’s limitations.
Limitations
The meta-analytic technique used in this dissertation relies on correlations to compute the
weighted average effect size estimate ( r ) (Hunter & Schmidt, 1990). The study might have
benefited from several extant empirical TCT studies that were not included because they did not
report correlations (e.g., Anderson, 1985; Masten, 1984; Monteverde & Teece, 1982). Most of
the studies that did not report correlations were published in economics journals. Nevertheless, if
a TCT study that reported results was identified but it did not contain correlations, an email was
sent to the first author requesting correlations. Although 37 authors responded, most said that
they no longer had correlations available. There were two exceptions (Bienstock & Mentzer,
1999; Sriram, Krapfel, & Spekman, 1992), and their studies were included in the meta-analyses.
Yet if more studies had reported correlations or if the authors had furnished correlation matrices,
additional effect sizes would have been included in the analyses, and it is possible that the results
would be different. Because most of the missing correlations were from economics articles, I
examined the subset of economics articles that did report correlations and compared their effect
size estimate to the effect size estimate in other studies (e.g., strategic management). The effect
sizes were virtually identical ( r = 0.082 for economics and r = 0.080 for all others). Thus,
given that 98 studies were included and that there was no material difference between the
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r computed from economics journals versus all other studies, it is not likely that including more
studies would lead to material differences in the results. The only way material differences
would surface is if missing studies reported very strong effects regarding a large number of
transactions, which would further increase variance among studies.
A second limitation is that this study does not differentiate according to the quality of the
dependent variable measures (i.e., degree of integration). Instead, all measures that capture the
dependent variable were included. Each dependent measure can be found in Table 9. But,
perhaps not surprisingly, some dependent measures lack validity (Rindfleisch and Heide, 1997).
Specifically, Rindfleisch and Heide (1997) argued that the value-added-to-sales ratio found in
Balakrishnan and Wernerfelt (1986) is a poor measure for degree of integration decisions for
multi-industry studies because it does not account for differences among industries. Specifically,
it does not account for industry differences, when such differences have been shown to impact
strategy and profitability (McGahan & Porter, 1997; Scherer & Lee, 2002). Despite potential
problems, Dalton and Dalton (2005) argue that discarding studies, even if measures are suspect,
adds an “unnecessary subjective element.” In an effort to avoid this unnecessary subjectivity, all
studies containing a degree of integration measure were included.
A third potential limitation is that extant research contains many disparate measures of
each construct. There were more than 50 variations of asset specificity measures, and more than
30 variations of degree of integration measures. In meta-analysis, including disparate measures
inflates variance, causing increased uncertainty about effect size estimates and thus wider
confidence intervals (Hunter & Schmidt, 1990). Wider confidence intervals potentially lead to
two problems. First, wider confidence intervals increase the probability of making a Type II
error. In other words, wider intervals increase the probability of including zero and concluding
there is no relationship among variables of interest even when an empirical relationship is
present. Second, inflated variance triggers the search for theoretically important moderators
(Hunter & Schmidt, 1990). Yet it might be the use of disparate measures, not a theoretically
important moderator, that is inflating variance. This suggests that there is a need for TCT
researchers to converge on broadly accepted measures, so that the use of disparate measures does
not inflate variance, and cause some theoretically important relationships to go unnoticed.
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A fourth limitation results from some deficiencies in how meta-analyses are conducted.
Specifically, the use of bivariate effect sizes (r) and the ‘file drawer’ problem present potential
problems. Bivariate estimates ignore the role of intervening variables, such as a firm’s industry
environment, which can be statistically controlled in primary research. I tried to account for
some intervening variables via moderator analysis. Specifically, I examined whether a firm’s
industry environment (e.g., manufacturing versus service) influenced the relationships among
transaction attributes and the degree of integration. But several potential intervening variables
might have gone unnoticed, such as industry life cycle (Stigler, 1968). Therefore, to advance our
understanding of TCT, future research should attempt to estimate all relevant bivariate
correlations via meta-analysis and then use the resulting corrected effect size estimates (rc ) in
structural equation models (Hunter & Schmidt, 1990). The second potential problem is the ‘file
drawer’ problem. In this dissertation, the file drawer problem results if researchers could not
publish TCT studies because of reviewers’ and editors’ proclivities to publish studies offering
empirical support for received theory (Dalton & Dalton, 2005). Thus, numerous studies remain
in ‘file drawers.’ This results in an overestimation of the population effect size by meta-analysis.
This problem is reduced in the dissertation because numerous studies are included where TCT is
not the singular focus of the study (e.g., Combs & Ketchen, 1999; Leiblein & Miller, 2003).
A fifth limitation is that I focused mainly on transaction costs, and thus did not consider
how the total costs of ownership or quality influence degree of integration decisions (Ellram &
Carr, 1994). Although I include the impact of production costs later in this chapter, data
availability limited my ability to examine how the sum of production and transaction costs
together or how quality shapes such decisions. Examining total costs and/or quality might have
provided more insights than were offered in the TCT literature, and hence, this dissertation.
Taken together, the lack of inclusion of all TCT studies, the failure to account for
potentially poor dependent measures, the possibility for inflated variance, the use of bivariate
effect sizes, and the lack of inclusion of the total cost of ownership or quality all present possible
limitations of this dissertation. Despite these limitations, however, this dissertation makes several
contributions.
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Hypotheses With Significant Results
Asset specificity. The empirical evidence suggests that asset specificity influences firms’
decisions to integrate transactions, but only to a modest extent. One way to interpret the finding
is that a one standard deviation increase in asset specificity only leads firms to a .103 standard
deviation increase in the level of integration. Moreover, as shown by the post hoc tests, the
relationship between asset specificity and the degree of integration is moderated by the type of
integration (e.g., markets versus hybrids or markets versus hierarchies). Specifically, studies
examining the relationship between asset specificity and the choice between markets versus
hybrids reported stronger effect sizes ( rc = .24) than studies examining the choice among
markets versus hierarchies or hybrids versus hierarchies ( rc = .08, p < .01). Thus, although asset
specificity shapes degree of integration decisions, it is mostly due to firms’ movement from
market to hybrid, not toward hierarchy as TCT describes.
Another problem is that the effect size estimate might actually be overstated because it is
possible that strategically valuable resources, a logical subset of asset specific investments, led
firms to more integration because ownership of resources, according to RBT, leads to higher
performance (Chi, 1994). It is likely that at least part of the rc = .08 effect of firms moving to
hierarchy is a function of firms’ taking ownership of specific assets that are, or the firm hopes
will become, strategically valuable resources. Thus, even the small effect that was found might
be explained without the aid of TCT. In their analysis of restaurant chains, for example, Combs
and Ketchen (1999) found that when making governance decisions, firm resource concerns (e.g.,
reputation for quality and service) outweigh concerns arising from exchange conditions (e.g.,
asset specificity). Yet this study does not distinguish between generic asset specific investments
and those that are strategically valuable. Because of this, although the findings suggest that asset
specificity is related to the degree of integration, an alternative interpretation is that the findings
offer support for RBT, not TCT. At its worst, therefore, asset specificity might mean nothing
more than a proxy for strategically valuable resources. Future research might therefore benefit
from attempts to determine whether the specific assets that affect integration are also
strategically valuable, or those that management hopes will become strategically valuable.
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A second, related implication is that in order to derive a more complete understanding of
degree of integration decisions, researchers must at a minimum, integrate TCT with other
theories. Specifically, degree of integration decisions need to be studied in light of more recent
theoretical developments. Beyond RBT, the logic of real options theory suggests that governing
asset specific investments, which can be costly and irreversible, can reduce a firm’s adaptability
and threaten its survival (Folta, 1998). One plausible explanation for the strong effect of asset
specificity on hybrids, then, is that firms want to preserve flexibility (Folta, 1998) particularly
when the assets alone cannot improve firm performance. Another explanation is that hybrids
preserve stronger incentives (Zenger & Hesterly, 1997). Perhaps not surprisingly, Williamson’s
(1999) more recent work takes stock of more recent theoretical developments, such as RBT. He
asserts that governance choice research must account for firms’ pre-existing investments (i.e.,
strengths and weaknesses or capabilities). Further, accounting for asset specific investments that
take on such characteristics “should help to reduce the unexplained variance in simple tests of
the generic alignment hypothesis” (Williamson, 1999: 1103).
Jacobides and Winter (2005), for example, took a step forward by showing how RBT,
evolutionary economics, and TCT can be integrated to explain mortgage banking integration
decisions over time. In short, they showed how pre-existing specific investments and
heterogeneous capabilities (i.e., strengths and weaknesses) across firms led firms to choose
different paths of integration. Specifically, they chronicled how some retail banks, that invested
in and developed customer risk assessment and selection capabilities (i.e., retail production),
gained valuable experience. This experience allowed these firms to profitably move into
mortgage loan warehousing, a related banking area requiring similar capabilities. Their analysis
suggested that it was retail banks’ investments and accumulated experience that enabled
customer risk assessment and selection capabilities to develop. Consequently, these capabilities
enabled retail banks to perform the mortgage loan warehousing function profitably. Thus, initial
asset specific investments might allow for capability development. Based on these capabilities
and profits that flowed from them, firms were then able to choose which transactions to govern
by hierarchy.
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In sum, the effect size estimate between asset specificity and the degree of integration is
non-zero, but small. These findings are somewhat surprising given that asset specificity is TCT’s
“big locomotive” (Williamson, 1985: 56). Indeed, the strong relationship between asset
specificity and choice of hybrids over markets, and the lack of distinction among asset specific
investments considered strategically valuable (i.e., valuable and rare), and those that are simply
specific to a transaction but not strategically valuable, cast doubt on the notion that TCT “is an
empirical success story” (Williamson, 1996: 55). Future research therefore should add clarity by
examining the strategic importance of asset specific investments and estimating how such
investments impact firms’ flexibility.
Regulatory differences. The results indicate that when confronted with low intellectual
property rights protection, firms chose more integrated governance modes. From fear of
appropriation (i.e., the threat of opportunism) wherein licensees might copy ideas and use the
intellectual property for their benefit without reproach, firms opt instead for more integrated
governance to maintain greater control and to avoid this threat. This finding is consistent with
Delios and Beamish (1999) and Oxley (1999). Delios and Beamish (1999) found that Japanese
firms used more integrated governance modes when confronted with less effective patent and
trademark protection. Similarly, Oxley (1999) found that firms used equity arrangements instead
of contractual alliances in countries with lower property rights protection.
A key finding is that firms are skeptical about using arm’s length exchange in countries
where property rights are not protected. This suggests that the threat of opportunism matters, and
that the country in which transactions take place is a contingency factor affects the level of
potential opportunism risks. By suggesting that country environments shape managerial
decisions, institutional theory sheds light on such factors (Dimaggio and Powell, 1983). A key
implication is that future TCT research should consider the institutional context in which the firm
is embedded, and integrate TCT with insights provided by institutional theory.
Cultural collectivism. In contrast to the prediction, the results indicate that there is a
stronger linkage between transaction attributes and the degree of integration in countries
characterized by higher cultural collectivism. Despite perhaps lower threats of opportunism,
firms in more collectivistic cultures, such as Japan, appeared to choose more integrated
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governance modes for transactions characterized by asset specificity, uncertainty, and frequency.
This finding is consistent with Dyer’s (1997) analysis of large automobile manufacturers. He
showed that Japanese manufacturers favored collaborative (i.e., hybrids) over arm’s length
exchange (i.e., markets) with suppliers while U.S. manufacturers favored arm’s length exchange
instead. But the motivations for Japanese firms might have been different then their U.S.
counterparts. Dyer found that Japanese firms using more collaborative exchange also performed
better than their U.S. competitors. One explanation offered was that Japanese firms wanted to
access to suppliers’ capabilities and vice versa. By providing greater access to each other’s
capabilities, both the manufacturer and the supplier could improve performance. He concluded
that improving overall performance was a strong motive for establishing collaborative
relationships, and thus, firms sought to maximize transaction value instead of reducing
transaction costs.
Accordingly, one explanation is that higher cultural collectivism creates better conditions
to maximize transaction value through hybrid governance. Perhaps collectivism creates more
trust among transacting firms. This trust, in turn, likely creates conditions for closer
collaboration, allowing firms to focus on transaction value maximization, and the resulting
benefits in improved performance might outweigh those involving transaction cost minimization
(Dyer, 1997). More broadly, my country culture results run counter to the threat of opportunism
assumption underlying TCT in collectivist countries. Thus, another explanation is that the
damage from a poor reputation associated with violating trust outweighs any benefits from acting
opportunistically. If this is the case, it seems plausible that firms would invest in co-specialized
assets (e.g., dedicated) and would use close collaboration in lieu of arm’s length exchange to
increase firm performance (Chi, 1994; Dyer, 1996, 1997), not to reduce opportunism but to
increase value.4 Therefore, in countries characterized by cultural collectivism, firms seem to
prefer hybrids (i.e., collaborative relationships) rather than hierarchy, but this is not what TCT
predicts.
Of note, there were only six countries involved in testing for differences for both
collectivism and high intellectual property protection. Of these six countries, only two countries
4
Data were not available to examine this notion.
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contained scores that classified them in the same direction for both categories. Specifically, only
Canada and the United Kingdom received low rankings for both collectivism and intellectual
property protection. The other four countries - Japan, Spain, Norway, and the United States –
were classified in opposite directions for each category. For example, Japan was classified as a
high collectivism country with low intellectual property rights protection. I could not determine
whether it was the extent of collectivism or intellectual property rights protection. More research
is needed to determine country effects on the relationship between transaction attributes and the
degree of integration is due to collectivism or intellectual property rights protection.
Performance. Consistent with the theory, there was evidence that matching transactions
to TCT’s prescribed degree of integration shapes performance, at least for transactions involving
asset specific investments. Specifically, the results showed that the relationship between degree
of integration and performance was stronger for asset specific investments that were governed
through more integrated governance modes. This finding is consistent with the findings of
Brouthers, Brouthers, and Werner (2003), which showed that firms that invested in specialized
training, proprietary products and services, and that used more integrated governance modes
experienced better performance than others.
Given that the strategic management field’s central dependent variable is performance
(Meyer, 1991), this finding is perhaps the most important to strategic management research.
Indeed, a key implication is that if managers choose more integrated governance modes for
transactions characterized by asset specificity, they might also improve firm performance. In
light of this finding, however, these results must be interpreted with caution. Specifically, it is
possible that the measures of asset specific investments captured in the meta-analyses also
captured the degree to which assets are strategically valuable, and thus capable of generating
advantages (Barney, 1991; Chi, 1994). The empirical evidence, therefore, might also be
interpreted as support for RBT rather than TCT. Despite this potential problem, the evidence
suggests that in extreme cases where transactions are characterized by low and high asset
specificity, respectively, governing asset specific investments through more integrated
governance modes shapes performance.
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Hypotheses Lacking Significant Results
Environmental uncertainty. The results also suggest that firms do not use more
integrated governance mechanisms when environmental uncertainty is present. This finding is
consistent with David and Han’s (2004) results. In their review of extant TCT studies, they found
little support for the notion that environmental uncertainty, whether arising from market
conditions or technological change, impacted governance choice. In fact, they found that
technological uncertainty led more firms toward less integrated governance modes. The results
offered in this dissertation seem to corroborate their findings.
Further, the lack of support indicates that environmental uncertainty is not a key factor in
firm governance decisions. As noted by Williamson (1985), because of TCT’s threat of
opportunism assumption, uncertainty creates the need for more complex contracts to protect the
firm against opportunism. But writing these contracts leads to higher transaction costs; thus firms
choose more integrated governance modes to avoid these costs (Williamson, 1985). While this
might be true in some cases, the aggregate evidence does not support this notion.
One possible explanation for the lack of corroborating results is that many managers seek
to govern uncertain transactions with less integrated governance modes (Folta, 1998) despite
higher transaction costs. As noted earlier, real options theory suggests that in the presence of
uncertainty, hierarchy can limit a firm’s flexibility and adaptability. In a world characterized by
fast-paced change (Levy, 1994; Teece, Pisano, & Shuen, 1997) integration reduces, rather than
maintains flexibility. This is consistent with Schilling and Steensma’s (2002) findings. They
found that firms facing technological uncertainty avoided hierarchical governance (i.e.,
acquisitions) and instead used licensing agreements. Because hierarchy locks firms into a
specific course of action, firms might avoid using more integrated governance modes for
uncertain transactions and, in doing so, preserve their adaptive abilities. Therefore, firms might
value flexibility when uncertainty is present.
Behavioral uncertainty. The finding that behavioral uncertainty does not influence the
degree of integration decisions also conflicted with TCT’s prediction. Yet this finding is also
consistent with those outlined in David and Han’s (2004) vote count review of TCT.
Accordingly, one way to interpret this finding is that safeguarding against opportunism, even
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when performance is difficult to measure, might not be a large concern for firms, at least not to
the extent that a firm would adopt a more integrated governance mode. One plausible reason for
this is that advancements in information technology and accounting procedures are increasing
managers’ abilities to cost effectively measure performance (Zenger & Hesterly, 1997).
The notion that performance measurement uncertainty does not influence degree of
integration decisions has broader implications for TCT. While broad implications are discussed
later, it important to point out here that the non-finding regarding performance measurement
uncertainty suggests that economic actors are not as consistently opportunistic or as guileful as
Williamson (1975) described. If they were, integration would likely be the optimal course of
action. But instead, the lack of results suggest that economic actors do not attempt to “mislead,
distort, disguise, obfuscate, or otherwise confuse” (Williamson, 1985: 47), even when others
would be unaware of this deviant behavior. This is consistent with Ghoshal’s (2005) assertions
that people do not seek advantages (i.e., act opportunistically) at others’ expense. Specifically, he
argued that people are most often “other regarding” (Ghoshal, 2005: 83), which is supported by
evidence provided by Camerer and Thaler (1995). They showed that even when one party was in
a position to demand more (i.e., act opportunistically) from another party, the party in the more
favorable position suggested a 50/50 split of the asset (i.e., gift). Thus, people generally seem to
be fair, not opportunistic, which is perhaps why measurement difficulty did not influence degree
of integration decisions.
Human and brand assets. There was also no support for the hypotheses stating that
human and brand assets are more likely to influence governance decisions than other asset
specific investments. Moreover, dedicated and temporal assets had the strongest effect size
estimates. Because dedicated assets are used to support a relationship with another transactor
(i.e., buyer or supplier), I ran a post hoc test to determine if the type of integration was
influencing the results. The results suggest that dedicated assets led firms to choose hybrids over
markets ( rc = .275 p < .05) but such assets had no effect on the choice between markets and
hierarchies ( rc = -.014, ns). This suggests that dedicated assets only influence the choice between
markets and hybrids. Perhaps this finding is not surprising given that dedicated assets are not
likely to be strategically valuable, and therefore, capable of improving firm performance.
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Thus, one plausible explanation regarding dedicated assets is that managers have
discovered that by investing in co-specialized assets and developing collaborative relationships
with other firms, they can maximize transaction value to accrue “relational rents,” (Dyer &
Singh, 1998). Relational rents refer to synergistic benefits, or higher profits, created by more
collaborative exchange relationships (i.e., hybrids) that exceed what firms could generate
through arms-length exchange. But, importantly, the assets are co-specialized, and therefore are
not strategically valuable until combined with those of other firms. Dyer (1996) found that by
investing in and combining such assets with its suppliers, Toyota improved its product quality
and firm performance. Based on this, two key implications emerge. First, governance costs are
low when dedicated assets are present because firms are not concerned about the threat of
opportunism or can minimize such threats via contracts (Zenger & Hesterly, 1997). Or, second,
that transaction cost minimization is a less significant concern when making firm governance
decisions (Dyer & Singh, 1998). Overall, though, the evidence is consistent with the notion that
transaction value maximization is a primary driver of firm governance decisions.
The finding that temporal asset specificity is more strongly related to the degree of
integration than human or brand assets is largely driven by an outlier, and thus, likely the result
of second order sampling error. Second order sampling error occurs when one or more of the
available studies have effect sizes that are not consistent with the population of studies (Hunter
& Schmidt, 1990). For temporal asset specificity, the three identified studies contained
correlations of .22, .13, and .705. The .705 is inconsistent with the other two, indicating the
presence of second order sampling error.
Frequency. The effect size estimate regarding the relationship between frequency and
the degree of integration did not differ from zero, and therefore, did not support TCT’s
prediction. Yet the post hoc test revealed that frequency led firms to choose hybrid governance
over markets with r = .137 (p < .01). This suggests that when firms undertake frequent
transactions, they seek to lower the overall costs of accomplishing a transaction by developing
collaborative relationships with suppliers or buyers, but not through integration. One explanation
is that hybrid governance preserves greater incentive intensity, and thus the sum of all costs
involved in a transaction might be lower than governing frequent transactions with hierarchical
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governance (Williamson, 1985; Zenger & Hesterly, 1997) despite higher transaction costs.
Alternatively, perhaps hybrids simply lower transaction costs for frequent transactions.
Nevertheless, the preferred mode of governance for frequent transactions is hybrids, not
hierarchies, which is contrary to the theory.
Moderating influences. Because of the theory behind the interactive effect of asset
specificity and 1) environmental uncertainty or 2) frequency on the degree of integration
(Williamson, 1985), the lack of empirical evidence for these relationships was also surprising.
The former finding is consistent with David and Han’s (2004); they found little support for the
interactive effect of asset specificity and uncertainty on the degree of integration. There are at
least two plausible explanations. First, there could have been a problem with measurement error
when the studies were grouped into low and high categories for uncertainty and frequency.
Therefore, the classification schemes and the measures for environmental uncertainty and
transaction frequency might not be optimal. Although industry sales variability (e.g., Bergh &
Lawless, 1998; Castrogiovanni, 2002; Dess & Beard, 1984; Harrigan, 1985) was the measure for
environmental uncertainty, it does not necessarily measure how uncertain a specific transaction
is within the larger industry context. Regarding frequency, the experts were provided with the
primary study’s data source, industry context, and the nature of the transaction. For Masten et al.
(1989), for example, the following information was provided: the data source was a managerial
survey, the context was the automobile industry, and the nature of the transaction was component
procurement. Because automobile component inputs, such as steel, must be procured on an
ongoing basis, the experts classified this transaction as “frequent” as opposed to “infrequent.”
But other primary study information might have been more difficult to interpret, leading experts
to incorrectly classify some frequent transactions as infrequent and vice versa. If this was the
case, then the meta-analyses would contain large measurement errors, and the relationships under
investigation might go undetected.
Second, given the small effect of asset specificity and the null uncertainty findings, there
is also the possibility that the relationships do not exist. As the results showed earlier, asset
specific investments have a weak influence on degree of integration decisions. Given the null
findings regarding environmental uncertainty, it is likely that this weak relationship does not
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change even when environmental uncertainty is present. Further, perhaps this result is not
surprising given that real options theory asserts that firms would avoid asset specific investments
in the presence of uncertainty because making such investments limits flexibility (Folta, 1998).
In addition, the lack of support regarding the interactive effect of asset specificity and
frequency on the degree of integration suggests that frequent transactions supported by asset
specific investments also do not influence transaction costs, at least not to the extent that firms
would adopt more integrated governance modes. In short, because of measurement error or
because the relationships do not exist, there was no evidence supporting the moderating
influence of uncertainty or frequency on the relationship between asset specificity and the degree
of integration. Regarding measurement error, I strived to provide experts with adequate
information (i.e., data source, study context, and nature of transaction) to decide how frequently
a transaction occurs, but it is possible that the experts misclassified some transactions. Yet a
more likely explanation is that the small relationship between asset specificity and the degree of
integration is not influenced by how frequently transactions occur.
Effect sizes stronger within than among industries. There was no evidence of different
effect size estimates among studies containing single or multiple industry samples. A likely
explanation for this lack of evidence is the omission of industry life cycle stage; theory suggests
that firms are highly integrated in the early stages, but less integrated in the later stages of the
industry’s life cycle (Mahoney, 1992). At least two studies in this dissertation’s data set offer
support for this notion. Using archival data, Bigelow (2004) studied transactions in automobile
manufacturing from 1920-1934, and found a stronger effect between physical asset specificity
and the degree of integration (r = .165). In contrast, Walker and Weber (1984) surveyed
automobile manufacturers approximately fifty years later, and reported a negative correlation (r
= -.10) for the same relationship. In a similar vein, Jacobides and Winter’s (2005) analysis of
U.S. mortgage banks and Swiss watch manufacturers showed that over time, mortgage banks
adopted less integrated governance modes while Swiss watch manufacturers adopted more
integrated governance. Therefore, industry life cycle appears to influence governance choice, but
the influence might be contingent on the industry under investigation. Consideration of the
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nature of the industry might therefore explain the null results, and future research in this area
seems warranted.
Broader Implications of Results
As this study shows, key TCT predictions have minimal support in just one area.
Thus, TCT is not the “empirical success story” that Williamson (1996: 55) described. Indeed, the
notion that asset specificity is positively related to the degree of integration was only supported
by modest evidence, and could also be the result of strategically valuable resources rather than
specificity per se. Environmental and behavioral uncertainty had no effect on such decisions, and
frequency only influenced governance decisions when considering the move from market to
hybrid governance. Finally, the interactive effects of asset specificity and environmental
uncertainty or frequency were not supported. Collectively, this evidence suggests that transaction
costs do not matter to the extent described by Williamson (1985, 1996). Instead, the metaanalyses involving the 95 TCT studies containing bivariate relationships suggest that TCT is not
an empirical success story. Accordingly, there are several implications of the results.
The first implication stems from the findings on asset specificity and uncertainty, and
involves TCT’s assumptions about bounded rationality and the threat of opportunism. Bounded
rationality was supposed to encourage more integration for uncertain transactions (Williamson,
1975). Because managers cannot accurately predict outcomes when uncertainty is present,
uncertainty was supposed to lead these managers to choose hierarchical governance over markets
and hybrids. Recently, however, real options adherents have suggested that managers can
estimate risk and uncertainty (Folta, 1998; Leiblein, 2003; Miller & Folta, 2002). Further, by
deferring investment (i.e., using hybrids or markets instead of hierarchy) under conditions of risk
and uncertainty, it is possible that the value of deferring exceeds the value of earlier investment.
More recent developments in TCT seem to account for these insights. Indeed, Williamson’s
(1999: 1089) more recent work argues that “transaction cost economics ascribes foresight rather
than myopia to human actors.” Thus, bounded rationality does not appear to strongly influence
degree of integration decisions.
The second implication is related to TCT’s assumption of opportunism. The evidence
suggests that the threat of opportunism might not be a large determinant of governance choice, at
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least in countries where intellectual property rights are protected. Thus, the threat of opportunism
only seems to matter in extreme cases. There are several reasons why this assumption might not
reflect reality in most contexts. First, opportunism is constrained by reputational effects in
markets (Hill, 1990). Thus, if a firm acts opportunistically, other prospective transactors might
avoid them. Because it could do more harm than good, there is little incentive to act
opportunistically. Accordingly, most economic actors do not act this way. Second, advancements
in information technology and accounting procedures have reduced threats posed by
opportunism by allowing transaction performance to be measured more easily (Zenger &
Hesterly, 1997). If transaction performance can be easily measured, it reduces the chances for
one firm to take advantage of another. Third, firms might not want to risk the long-term benefits
of focal relationship for short-term profits. Taken together, the reputation effects associated with
acting opportunistically, the improvement in measurement capability, and the fact that
opportunist actions can be short-sight seem to have reduced the threat of opportunism. Thus,
instead of seeking advantages at the other’s expense, transacting firms seem to be developing
greater trust (Dyer, 1997; Poppo & Zenger, 2002). By facilitating closer collaboration, this trust
might lead to improved performance (Dyer, 1996; Dyer & Singh, 1998). Therefore, either firms
have found ways to confront the threat of opportunism, or it is simply not the pervasive condition
that Williamson (1975, 1985) described. This suggests that future research should de-emphasize
the threat of opportunism unless it is an extreme case, a suggestion offered by many others
(Conner & Prahalad, 1996; Davis, Schoorman, & Donaldson, 1997; Ghoshal & Moran, 1996;
Hodgson, 2004).
The third and related implication is that it is important to consider the institutional
contexts in which the firms and transactions are embedded. These include the culture, regulatory
issues, and, perhaps more broadly, the society in which the transactions take place. A core
proposition in institutional theory (Dimaggio & Powell, 1983) is that firms copy others in an
effort to reduce uncertainty and attain legitimacy and resources. Thus, it is possible that patterns
emerge when looking at strategic decisions (Huy, 2001), such as degree of integration, in
particular countries/cultures as firms cope with different country regulatory bodies or cultures.
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This issue opens up opportunities for future research wherein researchers could clarify whether
different patterns of integration emerge across different institutional contexts.
If TCT sheds minimal light on the means by which firm governance decisions are made,
then an important theoretical question that emerges is what drives integration decisions. More
recent theoretical developments, such as those purported by RBT, point towards firm
capabilities. In a recent study, for example, Madhok (2002) suggested that integration decisions
are largely shaped by capabilities. Specifically, he asserted that resources particulars, which are
valuable and inimitable firm resources or more broadly, capabilities, should be considered when
determining optimal degree of integration decisions. To determine the strength of one resource
particular - production cost efficiency - a post hoc test was conducted. The post hoc test
indicated that production costs shape integration decisions with r c = .128 (p < .05). This effect
size estimate was larger than estimates for environmental uncertainty, behavioral uncertainty,
and frequency, and larger in magnitude than asset specificity, suggesting that capabilities shape
governance choice. Consistent with Madhok’s assertions, a key implication, then, is that
capabilities impact governance choice. Therefore, researchers must broaden their approaches
when trying to explain degree of integration decisions.
A fourth implication also results from the minimal support for TCT’s key predictions. An
important theoretical question is whether the linkages among each construct is consistent with
the theory. Specifically, in Figure 1, it was suggested that transaction attributes (e.g., asset
specificity or uncertainty) create exchange hazards (e.g., threat of opportunism). To combat these
exchange hazards, firm incur transaction costs (e.g., negotiate and draft contracts), and choose
the governance mode that minimizes such costs. Minimizing these costs improves firms’
adaptive abilities and performance. Although these linkages are supposed to be related, they
remain a ‘black box’ because they are largely assumed away and thus not measured in TCT
research. Instead, extant research focuses on the direct relationship between transaction attributes
and the degree of integration (cf. David & Han, 2004). Thus, researchers should measure other
constructs (e.g., exchange hazards or transaction costs) and test how they impact degree of
integration decisions. In short, there is a need to measure and test the missing intermittent
linkages offered by TCT through more fine-grained research, such as through a path analysis.
107
Because TCT has been the dominant theory used to explain governance choice, the
findings of this study present future researchers with a large challenge—can we develop more
precise theory to explain how and why firms govern transactions in the way they do. It appears,
that to develop more precise theory, future researchers should relax TCT’s behavioral
assumption of opportunism, focus more on, and integrate insights provided by other theories.
This appears to be the direction that some researchers are taking (e.g., Leiblein & Miller, 2003;
Madhok, 2002; Poppo & Zenger, 1998). In addition to incorporating firm capabilities (e.g., RBT
insights) into firm governance decisions, it seems that insights from real options theory can also
be useful (Leiblein, 2003). Further, as indicated by the results, it seems that researchers must also
account for institutional contexts in which firms and their respective transactions are embedded,
and the production costs of accomplishing transactions. In sum, by incorporating alternative
views, it appears that a more complete picture can be drawn to show how firms govern
transactions.
Managerial Implications
The findings of 77 studies suggest that there is small but positive relationship between
asset specificity and the degree of integration. However, the results also show a much stronger
effect that indicates that most managers govern transactions involving asset specific investments
with hybrids, not markets or hierarchies. Thus, managers appear to view hybrid governance as a
superior means to: 1) reduce transaction costs, 2) reduce overall costs, or 3) maximize
transaction value when asset specific investments support a transaction (Dyer, 1997; Dyer &
Singh, 1998). This contradicts TCT. The results suggest that managers have found ways to
constrain the threat of opportunism without resorting to hierarchy even when asset specificity is
present (Dyer, 1997; Dyer & Singh, 1998; Zenger & Hesterly, 1997). In hybrid agreements, for
example, clauses can be specified that outline penalties, such as termination of the agreement,
should one transactor take advantage of the other. Additionally, hybrid agreements can outline
how disputes will be resolved through private ordering. Private ordering, which refers to
resolving disputes outside the court system (e.g., mediation, arbitration), can often resolve
disputes both timely and equitably (Dyer, 1997). Finally, due to technological improvements,
firms have more ability to measure transaction performance, which also seems to limit the threat
108
of opportunism (Zenger & Hesterly, 1997). Van Hoek (2001) found firms’ increased ability to
measure transaction performance has led to a greater use of hybrid governance.
Thus, perhaps even when asset specific investments support transactions, managers have
ways to constrain the threat of opportunism regardless of how a transaction is governed, such as
through clauses outlined in hybrid agreements or through improved measurement capabilities.
Further, the use of hybrid governance preserves incentive intensity, which can reduce overall
costs and improve performance (Zenger & Hesterly, 1997). Given this, if managers use
mechanisms to constrain the threat of opportunism, they should govern asset specific
investments through hybrids because it preserves incentive intensity, and might reduce a firm’s
costs or maximize transaction value. Accordingly, managers should not follow TCT’s
prescription to govern asset specific investments by hierarchy.
Despite the finding that transactions involving asset specificity are most often governed
by hybrids, it was also found that matching asset specific transactions to the appropriate degree
of integration improves firm performance. But this finding involved only a small subset of 11
studies that examined performance. One possible explanation for this finding is that the studies
that showed that asset specific investments shaped performance were actually capturing
strategically valuable resources and not generic asset specific investments per se. This appears to
be the case. Table 12 lists 16 asset specificity measures from the 11 studies included in the
results. Of these 16 measures, 11 appear to be proxies for asset specific investments that are also
strategically valuable. Reuer’s (2001) asset specificity measure, for example, was research and
development intensity (i.e., ratio of R&D investments divided by sales). Such investments might
be valuable and rare because they can lead to innovation, and hence, better firm performance
(Dierickx& Cool, 1989). Yet because 11 of the 16 measures take on such characteristics, the
findings might be interpreted as support for RBT’s assertions -- that strategically valuable
capabilities should be leveraged by hierarchy to improve firm performance (Argyres, 1996). This
interpretation casts further doubt on the notion that non-strategically valuable assets should be
governed by hierarchy, regardless of how much asset specificity is involved. A key implication,
therefore, is that non-strategically valuable assets should not be governed by hierarchy but
strategically valuable asset specific investments should. Accordingly, this suggests that managers
109
should not follow TCT’s prescription to govern asset specific investments by hierarchy unless
the assets are also strategically valuable.
TABLE 12 – Asset Specificity Measures Strongly Related To Performance
Author
Measure Description
Affuah, 2001
Aulakh & Kotabe, 1997
Aulakh & Kotabe, 1997
Does firm have development and marketing experience*
Are our products more technologically advanced, higher quality, and unique than competitors*
Significant investment geared toward customizing product to foreign market and it is costly to
change existing way of doing business
Brouthers et al., 2003
Specialized training, general equipment investments, proprietary nature of goods and services*
Combs & Ketchen, 1999 Is the company well respected for quality and service*
Combs & Ketchen, 1999 Is there difficultly redeploying equipment
Combs & Ketchen, 1999 How long does it take to train employees and managers*
Fink, 1995
Efficiency, quality, and timeliness of services and knowledge*
Fink, 1995
Level of relationship specific equipment, training, and production systems
Gainey & Klass, 2003
Is training tailored to firm's needs*
Mayer & Nickerson, 2003 Does work involve mainframe
Mayer & Nickerson, 2003 If persons outside product team are involved in execution
McGee et al., 1995
Managers' marketing experience / number of managers*
Reuer, 2001
R & D to sales ratio*
Steensma & Corley, 2001 Degree of differentiation product offers*
Tan & Vertinsky, 1996
Advertising to sales ratio*
Tan & Vertinsky, 1996
R & D to sales ratio*
Note: Asterisks appear next to the asset specificity measures that take on characteristics of
strategically valuable resources.
Thus, there are two key managerial prescriptions offered by this dissertation. First, if
managers can find ways to constrain the threat of opportunism, they should govern transactions
supported by non-strategically valuable asset specific investments through hybrids. This is
because it is likely that governing such assets through hybrids either reduces costs through
greater incentive intensity or maximizes transaction value by leveraging each transactors’
capabilities (Dyer & Singh, 1998). In either case, however, both transactors are likely better off.
A second prescription is that managers should govern strategically valuable asset specific
investments through hierarchy because it improves firm performance. Stated differently, if asset
specific investments are at the core of a firms’ performance, those assets should be governed
through hierarchy. In sum, managers should govern generic asset specific investments through
hybrids and govern strategically valuable asset specific investments through hierarchy.
110
CONCLUSION
Although Williamson (1996) has argued that TCT predicts well, and should therefore be
used to understand degree of integration decisions, the empirical results do not offer much
support for these notions. Instead, this study’s findings suggest that transaction costs are not an
important determinant of degree of integration decisions. Even asset specificity, TCT’s “big
locomotive,” explains only a small fraction of variance. Thus, although transaction costs matter,
they do not appear to matter that much. Nevertheless, this study validates some useful aspects of
TCT, but like Ghoshal and Moran’s (1996) admonitions about a decade ago, it suggests that the
theory cannot be accepted as dogma without question. In light of the results, therefore, this study
offers a strong suggestion that capabilities, flexibility, institutions, and production costs might
play a large role in degree of integration decisions.
111
APPENDIX A – Studies Included in Meta-Analyses
Affuah, 2001
Andersen & Buvik, 2001
Anderson & Coughlan, 1987
Anderson et al., 2000
Artz & Brush, 2000
Aulakh & Kotabe, 1997
Balakrishnan & Wernerfelt, 1986
Belderbos, 2003
Bergh & Lawless, 1998
Bienstock & Mentzer, 1999
Bigelow, 2004
Blodgett, 1992
Brouthers et al., 2003
Brouthers, 2002
Buvik & Andersen, 2002
Buvik & Gronhaug, 1999
Buvik & Haugland, 2004
Buvik & John, 2000
Chelariu, 2002
Coles & Hesterly, 1998
Combs & Ketchen, 1999
Cusumano & Takeishi, 2001
D'Aveni & Ravenscraft, 1994
Delios & Beamish, 1999
Dhanaraj & Beamish, 2004
Dickson, 1997
Dragonetti et al., 2003
Dunbar & Phillips, 2001
Dwyer & Welsh, 1985
Fan, 1996
Fink, 1995
Gainey & Klass, 2003
Gebelt, 1992
Girlea, 2001
Gulati, 1995
Harrigan, 1985
Heide & John, 1990
Heide & John, 1992
Heide & Miner, 1992
Hughes, 1999
John & Weitz, 1989
Johnson, 1988
Joshi & Campbell, 2003
Joshi & Stump, 1999
Kobrin, 1991
Kotabe et al., 2003
Lassar & Kerr, 1996
Lee, 1998
Leffler & Rucker, 1991
Leiblein & Miller, 2003
Leiblein et al., 2002
Lu, 2002
Masten et al., 1989
Masters & Miles, 2002
Mayer & Nickerson, 2003
Mayer, 1999
Mayer, 1999
McGee et al., 1995
McInnes, 1999
McNally & Griffin, 2004
Mesquita, 2002
Mitchell & Singh, 1996
Mutinelli & Piscitello, 1998
Nicholls-Nixon & Woo, 2003
Nickerson et al., 2001
Orr, 1998
112
Osborn & Baughn, 1990
Oxley, 1999
Parkhe, 1993
Penner-Hahn, 1998
Poppo & Zenger, 2002
Pouder, 1994
Rasheed & Geiger, 2001
Reuer & Arino, 2002
Reuer et al., 2002
Reuer, 2001
Russo, 1992
Salmond, 1987
Sawhney et al., 1999
Schilling & Steensma, 2002
Shane, 1998
Shrader, 2001
Sriram et al., 1992
Steensma & Corley, 2001
Steensma., 2000
Stump, 1995
Subramani & Venkatraman, 2003
Subramani, 1997
Sutcliffe & Zaheer, 1998
Takeishi, 2001
Tan & Vertinsky, 1996
Walker & Weber, 1984
Walker & Weber, 1987
Weiss & Anderson, 1992
Widener & Selto, 1999
Zaheer & Venkatraman, 1994
Zaheer & Venkatraman, 1995
Zahra & Nielsen, 2002
APPENDIX B: Coding Rules for TCT Meta-analysis
1) Identifier: Write the last names of each author.
2) Year: Write the year the study was published. This applies to both journal articles and
dissertations. If it is an unpublished manuscript, write the year of the most recent paper update.
3) Paper Source: Circle the paper source.
4) Research Method: Circle the research method used. If the data collected are from a survey
(i.e., primary data), please circle 1=Survey. If the data collected are archival (i.e., collected from
a secondary source, such as the newspaper, directories, or online databases), please circle
2=Archival. If TCT variables come from different sources, please circle 3=Mixed. Lastly, if the
data came from a student experiment (i.e., hypothetical situation), please circle 4=Experiment.
5) Cross-sectional: If the dependent variable is collected for one time period (e.g., 1975) please
circle 1=Cross-sectional. If the data for the dependent variable (i.e., degree of integration) were
collected in a later time period, circle 2=Non cross-sectional (lagged). If the data for the
dependent variable were collected over several time periods, circle 3=Non cross-sectional
(longitudinal).
6) Time Frame: Please note the year or years the data were collected. This will not correspond
with the publication year. So if the survey is administered in 1996, but asks about decisions from
1994, please indicate 1994.
7) N-size: The number of firms under investigation. If this is one or a few firm(s), but several
respondents or transaction decisions, please note both. Also, please just note the number of
firms. Some studies may pool data (e.g., look at 100 firms over 10 years) and provide a larger
sample size. Strictly speaking, we are just concerned about the firms and number of transactions.
8) Industry and/or SIC code: Please circle if the study notes the sample sector, industry, or
industries. Also, please note if the nature of industry is noted, such as aircraft or automobile
manufacturing. If fortune type companies are used, indicate multiple.
9) Public versus Private: Please note if the study states whether the sample was drawn from
publicly versus privately held firms or a mix.
10) Country: Please note the country in which the sample was drawn. Most studies will draw on
US firms, but there will be cases when studies draw on firms from other countries. Please make
113
distinction between US firms, foreign firm home country, a mix of foreign countries (e.g.,
Greece, Turkey, and Germany), or a mix of US and foreign.
11) Organization size: Please note the average revenues, employees, assets, or log
transformation that indicates that average firm size for the sample.
12) Nature of Transaction/Transaction Decision: Please write in what is being transacted and
the type of decision. For example, are these inputs for aircraft, the decision to outsource or
insource MIS, or a market entry decision (e.g., expand into China).
13) Transaction attribute: The transaction attribute that is being examined. The study will say
something like “We tested environmental uncertainty or human asset specificity.” Thus, circle
the type of transaction attribute being examined and provide a description of the measure that is
used. For example, a study might say that environmental uncertainty was measured by forecast
variability or industry sales variability. Thus, circle environmental uncertainty and fill in forecast
variability or industry sales variability in column 3. Please write the reliabilities (will likely say
the alpha [α] of the measure), and the label/measure used in the 3rd column. If the transaction
attribute is not listed, please fill this in, and provide the reliability and label.
14) Exchange hazards: Most studies will not provide measures of the threat of opportunism or
bounded rationality. But if they do, please provide the reliabilities and label for the measure.
15) Transaction costs: Similarly, most studies will not provide transaction cost measures. But if
they do, please provide the reliabilities and label for the measure. As an FYI, ex ante costs are
those expenses related to the setup of a transaction. Ex post costs are expenses incurred once
contracts are executed.
16) Governance Choice/Degree of Integration: This refers to the choice between
alternative governance modes. In other words, the dependent variable will likely be the
choice between market contracting (also labeled arms length, outsourcing, buy) or hierarchical
governance (also labeled firm, hierarchy, make, or vertical integration, markets and hybrids, or
hybrids (also labeled joint ventures, alliances, partnerships, franchises, and licensing) and
hierarchy. Please provide the reliabilities and label for the measure. Finally, the measures in the
studies should all be coded from the lowest (i.e., markets) to the highest (i.e., hierarchy) degree
114
of integration. Some studies will be reverse-coded. If there are any questions about this, please
note it.
17) Performance: Most studies will not measure performance. But if they do, please provide the
reliabilities and label for the measure (e.g., level of coordination). If the study only examines
performance, this should be noted in one of the performance columns on correlation sheet.
18) Other: I included this category for any relationships that you think were not specified in the
coding sheet, but might be important. One key data point that I would like to collect are
production costs. But few studies will have these data.
115
APPENDIX C: Coding Sheet For TCT Meta-Analysis
1 – Identifier
2 – Year
3 - Paper source
4 – Research method
5 - Cross-sectional
6 – Time Frame
7 - N-size
8 – Industry and/or SIC code
9 – Public versus Private
10 – Country
11 – Organization size
Name of authors
Year of publication
1=Journal
2=Dissertation
3=Unpublished Manuscript
4=Book
1=Survey
3=Mixed
2=Archival
4=Experiment
1=Cross-sectional
2=Non cross-sectional (lagged)
3=Non cross-sectional (longitudinal)
Year(s) Data were collected ___________
# of firms
# of transactions
1=Manufacturing Specify: ____________, ____________
2=Service
Specify: ____________, ____________
3=Multiple
4=Institution (e.g., Government)
5=Not for Profits
0=Not specified
1=Public
3=Mixed
2=Private
0= Not specified
1=U.S.
2=Foreign (single)
Specify: ____________
3=Multiple
1=Average Revenues__________
2=Average Employees__________
3=Average Assets___________
4=Other (e.g., log transformation)_____________
0=Not specified
116
NOTES:
Short Description of Sample:
If just 2 to 3 countries, please note:
12 – Nature of Transaction/
Transaction Decision
13 – Transaction attribute
14 – Exchange hazards
15 – Transaction costs
16 – Governance
choice/Degree of integration
17 – Performance/
18 – Other
(e.g., IT versus Inputs or Market
1) Specify Description:
Entry)
Circle
Circle is it on the supplier side or
2) Growth decision (i.e., franchise) or a strategic change (i.e., outsource
existing function such as IT)?
distribution side or not specified?
Measure(s) Description
α
1
1=Site asset specificity
2
2=Physical asset specificity
3
3=Human asset specificity
4
4=Brand asset specificity
5
5=Dedicated asset specificity
6
6=Temporal asset specificity
7
7=Environmental uncertainty
8
8=Behavioral Uncertainty
9
9=Frequency
10
10=Other
Measure(s) Description
α
1=Threat of opportunism
2=Bounded rationality
Measure(s) Description
α
1=Ex ante
2=Ex post
3=Not specified
α Measure(s) Description (if any question of reverse coding, please note)
1=Market/Hierarchy
2=Market/Hybrid
3=Hybrid/Hierarchy (JV, Fran)
α
Measure(s) Description
1=Average Accounting (e.g., ROA, RSales)
2=Average Market (ROE, Mkt2Book)
3=Average Survey
4=Average of all performance measures
Label
Mean Std Dev. α
Correlation with (fill in)
e.g., Adaptive capabilities
117
APPENDIX D: Correlations Between Measures
TCT Measure
13- 13- 13- 13- 13- 13- 13- 13- 13- 14- 14- 15- 15- 16- 16- 16- 17- 17- 17- 17- 171
2
3
4
5
6
7
8
9
1
2
1
2
1
2
3
1
2
3
4
5
13-1 Site AS
13-2 Physical AS
13-3 Human AS
13-4 Brand AS
13-5 Dedicated AS
13-6 Temporal AS
13-7 Environmental Un
13-8 Behavioral Un
13-9 Frequency
OTHER OTHER OTHER OTHER – Small #s
14-1 Threat
14-2 Bounded
15-1 Ex ante TCs
15-2 Ex post TCs
16-1 Market/Hierarchy
16-2 Market/Hybrid
16-3 Hybrid/Hierarchy
17-1 Avg Accounting
17-2 Avg Mkt
17-3 Avg Survey
17-4 Average All
17-5 Avg Adaptation
OTHER
118
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BIOGRAPHICAL SKETCH
T. Russell Crook received his doctorate in Strategic Management from Florida State
University. He recently accepted a position as an Assistant Professor of Management in
the College of Business Administration at Northern Arizona University. His research
interests include why organizations govern certain transactions and how this shapes
performance, supply chain management, and methodological issues in strategic
management.
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