How Trust, Incentives and IT Shape Global Supplier Networks: Theory and
Evidence from IT Services Procurement
Sinan Aral
NYU Stern School of Business
sinan@stern.nyu.edu
Yannis Bakos
NYU Stern School of Business
bakos@stern.nyu.edu
Erik Brynjolfsson
MIT Sloan School of
Management
erikb@mit.edu
July 2010
Abstract
We develop an integrated, multi-period model of the optimal number of suppliers that
combines considerations from search and coordination theory, transaction cost
economics, and incomplete contracts theory. We assess our theoretical predictions using
a new dataset on the global IT sourcing decisions of 1355 firms in 12 countries collected
using a survey that we specifically designed to test our theory. Our empirical results
support three key theoretical predictions about trust, fit and IT. First, repeated
relationships and trust play a major role in supply chain governance. Several results
together support this conclusion: (a) as firms work with fewer suppliers they also engage
in more repeated relationships with those suppliers; (b) organizational and technological
asset specificityand the need to induce relationship-specific investments are correlated
not only with fewer suppliers, but also with a larger fraction of repeated relationships;
and (c) under conditions of greater asset specificity, engaging a smaller number of
suppliers is even more strongly associated with repeated relationships. Second, the need
to optimize fit between firm needs and supplier skills is associated with the use of a
greater number of suppliers. As firms’ needs become more idiosyncratic and diverse, they
engage more suppliers. Third, different types of IT have different implications for supply
chain structure. Investments in technologies that reduce search costs and transaction costs
are correlated with using more suppliers, while use of vendor-specific IT is associated
with fewer suppliers. Our work extends the literature on the role of IT in supply chain
governance by integrating multiple lines of theory in a repeated setting and using new
large scale empirical evidence to test predictions about how IT use relates to both the
number of suppliers and repetition in supply chain relationships.
Keywords: Buyer-Supplier Relationships, the Optimal Number of Suppliers,
Transaction CostEconomics, Incomplete Contracts, Coordination Theory, Search Costs,
IT Outsourcing, IT Vendors
Acknowledgements: We thank participants at the Workshop for Information Systems and
Economics, seminars at MIT and NYU for useful comments. The MIT Center for Digital
Business provided generous research support under a grant from Cisco Systems.
Electronic copy available at: http://ssrn.com/abstract=1635891
1. Introduction
Although there is widespread interest in supplier networks and the role of information technology
(IT) in business process outsourcing (Bardhan et al 2006, Dong et al 2009), there has been little empirical
investigation of the role of IT in influencing the optimal number of suppliers. There are several theoretical
predictions for when firms are likely to contract with more suppliers (Malone et al. 1987), fewer suppliers
(Bakos & Brynjolfsson 1993a,b) or when they are likely to ‘move to the middle’ (Clemons, Reddi & Row
1993), but comprehensive data on the actual numbers of suppliers firms use in sourcing specific products
and services, and the degree to which these are long-term or one-off relationships, is scarce. A few
notable studies examine single firms (e.g. Levina & Su 2008, Levina & Vaast 2008, Levina & Ross 2003,
Craighead et. al. 2007, Dyer 1997), trends in particular industries (Helper et. al. 2000, Helper & Levine
1992, Helper 1991, Cusumano & Takeishi 1991, Dedrick et al. 2008), or divergent cultural approaches in
the United States and Japan (Cusumano & Takeishi 1991), but a lack of large multinational, multiindustry datasets makes it difficult to resolve conflicting theoretical conclusions about the appropriate size
of supplier networks.1
In addition to the lack of empirical data, there are two important theoretical gaps in the literature
on IT and supply chain structure. First, there is inadequate focus on repeated interactions and the role of
trust in supplier relationships. Despite evidence that most supply relationships involve repeated
interaction andan increased recognition of the importance of trust among both management scholars and
practitioners, theoretical models of the optimal number of suppliers typically consider single period
settings and empirical analysis is typicallyignores whether or not these arrangements represent long-term
repeated relationships. Case study evidence suggests that trust and repeated relationships provide a critical
mechanism through which firms create incentives for suppliers to make non-contractible investments (e.g.
Helper et al. 2000). In turn, reducing the number of suppliers can increase the probability of a repeated
relationship with any one supplier, fostering a climate of trust. Theoretical models of the optimal number
of suppliers that do not consider repeated relationships may place too much importance on reductions in
the supply base as the sole incentive mechanism in supply relationships. We argue that considering a
multi-period setting in which firms can enter into repeated relationships with suppliers to create incentives
and develop trust can generate more realistic predictions that more accurately describe firm supply base
strategies observed in real world settings.
1
While IT outsourcing research has also developed rapidly in the last 15 years, most studies in this literature analyze the “makebuy decision” (when to outsource or develop products in-house), what to outsource, how to outsource (relationship and
contractual issues), and factors that affect the success of IT outsourcing arrangements (see Dibbern et. al. 2004 for a review of
this broader literature).
Page 1
Electronic copy available at: http://ssrn.com/abstract=1635891
Second, current research treats investment in IT as a single variable or considers a single
technology and its potential effects on supply chain structure. However, different types of IT are likely to
have vastly different implications for supply chain governance. While IT can support the coordination of
many arms-length market transactions (Malone et al. 1987), asset specificity in technology or
organizational processes can also create lock in and motivate firms to develop long-term partnerships with
suppliers in order to build trust and motivate relationship-specific investments (Srinivasan et. al. 1994,
Helper 1995). Malone et al. (1987) argue that information technology will lead firms to increase the
number of suppliers they work with by lowering transaction and coordination costs. Bakos (1997) makes
a similar argument based on IT reducing search costs and thus making it cost effective to contract with
larger numbers of suppliers. Conversely, Bakos & Brynjolfsson (1993a,b) show that when noncontractible relationship-specific investments are important, such as certain investments in innovation or
quality, it can be optimal to restrict the number of suppliers in order to increase their incentives to make
such investments. Vendor specific IT can support such long-term trust based relationships by creating
high throughput, dedicated and transparent information exchanges between specific firms, creating lockin and dependence (Srinivasan et. al. 1994, Iacovou et. al. 1995). Prior evidence demonstrates the
importance of distinguishing different types of IT assets in terms of their ability to support distinct
strategic goals related to firm performance (Aral and Weill 2007). Prior work suggests some types of IT
such as enterprise management systems are associated with greater outsourcing, while others such as
operations management systems are not (Bardhan et al 2007). We argue the same is true in supply chain
strategy and the optimal number of suppliers. Rather than having unidirectional implications on the
number of suppliers, IT’s impact on supply chain governance will differ depending on whether it reduces
search and coordination costs or alternatively increases switching costs.
We address these two limitations of current research by developing an integrated, multi-period
model of the relationship between IT and the optimal number of suppliers that combines considerations
from search and coordination theory, transaction cost economics and incomplete contracts theory, and
explicitly considers the long-term nature of most supplier relationships in a repeated game setting. In
doing so, we build on earlier frameworks which have typically considered a single theoretical perspective.
We assess the predictions derived from our model using a new dataset on the global IT sourcing
decisions of 1355 firms in 12 countries, obtained from a questionnaire we designed specifically for this
purpose. As a result we gain insight into the evolving nature of supplier relationships, especially in the
context of global IT sourcing. The data reveal that the vast majority of supplier relationships (83%)
involve repeated interaction over time, which highlights the critical importance of modeling supplier
Page 2
governance as a repeated game. The empirical evidence also supports three important theoretical
predictions.
First, repeated relationships and trust play a major role in supply chain governance. Several
results together support this conclusion: (a) as firms work with fewer suppliers they also engage in more
repeated relationships with those suppliers; (b) organizational and technological asset specificity and the
need to induce relationship-specific investments are correlated not only with fewer suppliers, but also
with a larger fraction of repeated relationships; and (c) under conditions of greater asset specificity,
engaging a smaller number of suppliers is associated even more strongly with more repeated
relationships. A key insight of our model that is supported by these results is that the cooperation and trust
developed through repeated interaction can offer strong incentives for supplier investment without
incurring the disadvantages of moving to fewer suppliers. While reducing the number of suppliers may
encourage higher supplier investments, incentives in this type of an incomplete contracts framework still
only achieve second best outcomes, making considerations of the costs of moving to fewer suppliers
critical.
Second, there are a realcosts of moving to fewer suppliers, including fit costs and a loss of
bargaining power, and these are an important part of firms’ supply chain governance decisions. In our
data, the need to optimize fit between firm needs and supplier skills is associated with the use of a greater
number of suppliers demonstrating the importance of fit costs in supply chain strategy. As firms’ needs
become more idiosyncratic and diverse, they engage more suppliers. In contrast, in a repeated setting,
firms can create incentives through long-term relationships without reducing their supplier base. This
strategy does not require full contractibility but rather promotes the development of norms of reciprocity
and cooperation through repeated interaction.
Third, different types of IT have different implications for supply chain structure – IT is not a
monolith. Investments in technologies that reduce search costs and transaction costs are correlated with
using more suppliers, while use of vendor-specific IT is associated with lock-in and fewer suppliers.We
favor a contingent view in which firms simultaneously choose supply chain strategies and the specific
technologies that support them.
Our work extends the literature on the role of IT in supply chain governance by integrating
multiple lines of theory in a repeated setting and using new large scale empirical evidence to test
predictions about how IT use relates to both the number of suppliers and repetition in supply chain
relationships. We address calls in the literature for longitudinal research to “explicitly evaluate changes in
the number of suppliers due to IT use” and “how firms, with the use of IT, adapt the way they govern
economic activities in supply chains” (Dedrick et al. 2008: 31) by specifically investigating how IT
Page 3
affects the fraction of repeated relationships in a firm’s supplier network (suppliers with whom the firm
has contracted within the last year or within the last five years), and whether the fraction of repeated
relationships in a firm’s supply network is in turn associated with more or fewer suppliers. Our theoretical
model integrates both incentives and costs in a multi-period setting, creating a flexible framework that
captures the heterogeneous strategic considerations inherent in sourcing decisions. Our empirical
evidence lends support to the model, highlighting the importance of repeated relationships and the varied
implications of different types of IT in theories of the optimal number of suppliers.
2. Economic Theories of the Optimal Number of Suppliers
Decisions concerning the size of supplier networks and the length of relationships are influenced
by a host of firm and industry characteristics and contingencies that have been addressed by various
streams of research. Supply chain governance decisions are intimately tied to the boundaries of the firm,
the specificity of technology and processes, the idiosyncrasy of firms’ requirements, incentives for noncontractible relationship specific investments, and the development of informal cooperation and trust
through repeated long-term interaction. In this paper we draw from four distinct but related bodies of
economic theory to motivate and guide our model development: transaction cost economics, coordination
theory, incomplete contracts theory, and multi-period game theory. Each of these perspectives contributes
an essential component to our analysis and the resulting predictions about the optimal number of suppliers
and the degree to which firms engage in repeated relationships with suppliers.
Transaction Costs & Asset Specificity.
Although markets can offer choice, create competition and minimize the costs of production, they
also entail significant costs. The transaction costs associated with market procurement, including the costs
of negotiating, monitoring and preventing malfeasance in partnerships are at times prohibitive (Coase
1937, Williamson 1975, 1976). These considerations influence the decision of whether to locate activities
inside the firm or rather to purchase services in the market, and also extend to selecting the optimal
number of suppliers in firms’ procurement networks (Bakos and Brynjolfsson 1993a, b). A greater
reliance on the market entails greater transaction costs and contracting with a larger number of suppliers
generally entails arms-length relationships with higher risks of malfeasance. Maintaining relationships
with a smaller number of trusted suppliers generally lowers the risk of opportunism, but is still riskier
than bringing transactions and activities inside the firm. The risk of opportunism increases with asset
specificity, since asset specificity makes it more difficult and costly to find an alternative supplier.
Page 4
Coordination Theory, Search Costs & Fit.
Work in the IT literature has addressed similar questions and applied the notion of coordination
and search costs to the determination of the optimal number of suppliers. Malone et. al. (1987) argue that
coordination costs tend to be higher in markets than within the firm (where there is no choice of
alternative suppliers), Thus, they argue that when IT reduces coordination costsit will disproportionately
benefit market transactions. In particular, lower coordination costs favor a move from single supplier
relationships within firms to multiple-supplier relationships in markets. IT reduces the information
processing costs associated with both the search for market partners and transaction facilitation activities
such as contracting, monitoring and the prevention of malfeasance.
The search costs argument, developed in part by Bakos (1997) as an extension to the neoclassical model
formulated by Salop (1979), contends that searching a larger pool of suppliers increases the probability of
finding the price and product characteristics that best fit the needs of the firm, but that search costs are
proportional to the number of suppliers searched. The larger the pool of suppliers searched, the greater the
probability of a good fit between supplier skills and buyer needs and the higher the cost. As IT makes
information processing less costly, it should theoretically enable firms to search amongst more suppliers
and achieve better fit to their needs at lower cost. The growth of online business to business marketplaces
demonstrates that this theory applies in practice, as firms can search a large number of suppliers and
conduct relatively sophisticated due diligence at low cost (Bakos 1991, Zhu 2004, Overby and Jap 2009,
Zhu and Zhou 2010). In addition, specific technologies for sharing information with partners and
suppliers reduce communication and coordination costs in the execution of the interactions amongst
partnering firms (Aral et. al. 2006). These theories together predict that IT should drive firms to interact
with more suppliers by reducing the costs of finding and interacting with them.
Incomplete Contracts Theory: Property Rights, Relationship Specific Investments & Trust.
Beginning with Williamson’s (1975) observation that not every aspect of a transaction can be
written intolegally-enforceablecontracts, the theory of incomplete contracts has recently been extended to
include considerations of how parties bargain over non-contractible ex post surplus. Incomplete Contracts
Theory (Grossman & Hart 1986, Hart & Moore 1990) asserts that sincecontracts are not completely
verifiable by third parties, property rights provide asset owners with the residual rights of control which in
turn determine the bargaining power of parties to capture theex post surplus they create. Bargaining power
affects the incentives of the relevant parties to make specific non-contractible ex ante investments that
affect value creation in the relationship. Using these insights, Bakos & Brynjolfsson (1993a, b) build a
formal model of buyer-supplier relationships in which it can be optimal for the buyer to limit the number
of suppliers in their network in order to create incentives for the suppliers to make greater nonPage 5
contractible investments and Mithas et al (2008) show that incentive costs of non-contractibility can
inhibit buyer use of reverse auction supplier markets. As intangible aspects of production and competition
become more critical, the need to create these incentives becomes relatively more important. Incomplete
contracts theory predicts that as the need for specific non-contractible investments by suppliers increases,
firms should contract with fewer suppliers in order to give them incentives to make such investments.
Cooperative Behavior in Repeated Interactions
Ideally, theories of supplier relationships should be considered in a repeated game setting because
cooperative behavior typically develops over time. Approaches focusing on a single transaction setting do
not consider how ex post bargaining or ex ante incentives to invest are different in long-term
relationships. Indeed, much of the existing qualitative evidence for the move to fewer supplier
relationships stresses the importance of building trust and levels of information sharing that make such
arrangements more efficient and productive than arm’s length market transactions. Qualitative analyses of
close-knit keiretsu supplier networks in Japan stress thatlong-term loyalty makes such networks and the
firms engaged in them robust to periods of difficulty. Uzzi (1997) and others (e.g. Antonelli 1988, Piore
and Sabel 1984) describe the information sharing and trust that develops over long-term relationshipsand
case studies of value added partnerships stress reciprocity benefits that develop between firms over the
long-term. Helper et. al. (2000) note that firms engaged in longer term relationships also benefit from
“learning by monitoring,” whereby the act of monitoring a partner’s activities and service quality
generates organizational and process learning that contributes to the overall efficiency and quality of the
partnership while at the same time reducing opportunism and increasing trust. This evidence indicates
that, in situations of economic conflict over non-contractible surplus with longer time horizons,
cooperative behavior empirically contradicts selfish individual motives.Our model addresses the influence
of long-term cooperative behavior on buyer-supplier relationships by including considerations of the
discounted value of future partnership and the threat of partnership termination.
The single period setting limits equilibria to what is feasible in a one-shot game. The resulting
outcomes have at least three important shortcomings from the firm’s perspective. First, while reducing the
number of suppliers induces relationship-specific investments, these investments are typically second-best
as suppliers take into account only the portion of the return on their investment that they can retain in
expost bargaining. Suppliers are therefore likely to underinvest. Second, reducing the number of suppliers
reduces the expected fit between the firm’s needs and suppliers’ ability to fill these needs, which in turn
reduces the expected economic surplus produced by the relationship. Third, contracting with a smaller
number of suppliers reduces the buyer’s bargaining power and therefore the share of the expost surplus
they can appropriate. Thus increased incentives for supplier investment come at the cost of decreased
Page 6
profits and correspondingly decreased investment incentives for the firm. As a result, in a single period
setting most firms would likely limit the number of suppliers in order to increase supplier incentives for
relationship-specific investments only in situations where such investments are of critical importance.
On the other hand, in settings with repeated interaction, firms may be able to induce suppliers to
make relationship-specific investments through mechanisms such as reputation, trust, reciprocity and
loyalty. The set of Nash equilibria significantly expands in infinitely repeated games; for instance, the
Folk Theorem provides that any feasible payoff in the stage game can give rise to a subgame perfect
equilibrium if each player is sufficiently patient and is guaranteed a minimum level of utility (Fudenberg
and Maskin 1986). There is a voluminous literature on the emergence of cooperation in multi-period
settings (Axelrod 1984; Fudenberg, Kreps and Maskin1990) but a key factor enabling this expanded
range of outcomes and in particular enabling sustainable cooperative behavior is the possibility of
punishment for non-cooperating participants. Such punishment can be accomplished through reputational
mechanisms (Krepset al. 1982) but also through the threat of expulsion from the game (Hirshleifer and
Rasmusen. 1989), both of which depend critically on the ability to observe defection (which in this case is
represented by suppliers’ under investment).
Our approach addresses the influence of long-term cooperative behavior on buyer-supplier
relationships and the overall importance of trust in these relationships by including considerations of the
discounted value of future partnership and the threat of partnership termination in the traditional property
rights framework.
An Integrated Model of Supply Base Reduction and Augmentation: Organizational and Technological
Determinants.
We develop an integrated model of supplier networks that incorporates transaction costs,
coordination costs and incentives for non-contractible supplier investments. The model also explicitly
takes into account the ongoing nature of supplier relationships by employing a multi-period framework in
which the firm has repeated interactions with suppliers that need to make specific investments in the
relationship over time.Table 1 illustrates the considerations identified above in determining the optimal
number of suppliers. Specifically, firms need to trade off the benefits of more vs. fewer suppliers as
shown in the rows of the table.A larger number of suppliers creates opportunities for a better fit between
firm needs and supplier skills as well as improved bargaining power, while a smaller number of suppliers
increases suppliers' incentives to make relationship-specific investments.
Page 7
Organizational Determinants
ƒ
More
Suppliers
ƒ
ƒ
Fewer
Suppliers
ƒ
ƒ
Better fit between firm needs
and vendor skills;
Improved bargaining power
Relationship-specific
investments;
Non-contractible investments;
Trust
Technological Determinants
ƒ
Lower search and
coordination Costs
ƒ
Technological lock-in
Table 1: Organizational and Technological Determinants of the Optimal Number of Suppliers
Information technology affects this tradeoff by lowering search and coordination costs, which
would tend to increase the number of suppliers, but also by creating opportunities for technological lockin to vendor specific technologies, which is most likely associated with decreases in the number of
suppliers. Furthermore, IT may affect the significance of the organizational drivers identified, for instance
by increasing the complexity of a firm’s needs and thus the importance of fit. As a result, the optimal
outcome will depend on the specific characteristics of the setting and the technology, demonstrating the
need for a flexible integrated model.
A Repeated Game Model of the Optimal Number of Suppliers
We model a multi-period setting with a firm and N potential suppliers. The multi-period setting
permits outcomes that are not feasible in a single-period game, allows punishment of non-cooperating
suppliers in future periods, and emphasizes the importance of reputation and trust. Suppliers are
heterogeneous (for instance, they offer differentiated products or skills) and thus differ in their ability to
help the firm fill its demand; thus while suppliers are ex ante indistinguishable, they differ in their ex-post
“fit.” A supplier’s fit cost φˆ i (α ) can be computed after a period’s demand is realized, and is drawn from a
probability distribution φ (α ) where α is a fit parameter representing the cost of imperfect fit, i.e., all else
equal a higher α leads to a higher fit cost. We denote by f (α,n) the expected value realized by the firm
net of the fit cost when employing n suppliers; ∂f ∂α = −∂φ ∂α and thus ∂f ∂α < 0 , i.e., f is
decreasing in α .
Given the heterogeneity of potential suppliers, the firm benefits from having the option of
selecting a different supplier in each period, thus maximizing the fit between its chosen supplier and the
realized demand. Furthermore, being able to select from a larger number of suppliers will improve
expected fit; thus the function f (α,n) is increasing in n. For instance, as in Bakos (1997) the fit parameter
Page 8
could indicate the distance between a supplier’s “location” on a unit circle and the location of an ideal
supplier,
given
the
realized
demand
in
a
certain
period;
in
that
case
in
expectation f (α,N) = V − α /2N where α is the “transportation” cost along the unit circle and V is the value
derived when employing a perfectly fitting supplier.
Before the start of the game (in period 0) the firm selects n ≤ N long-term suppliers among the N
potential suppliers. Identifying a new supplier is costly, and so is maintaining the base of long-term
suppliers, for instance because of qualification and coordination costs. As a result the firm incurs a
onetime cost K and a per-period cost κ for each long-term supplier, for a total per period “coordination”
cost (K + κ )n for the first period and κ n for each subsequent period.
Conduct of the game
In each period the firm realizes the demand from its customers, selects thelong-term supplier with
the best fit (the “selected” supplier) to help fill this demand and places an order, the order gets fulfilled,
production takes place, and the resulting surplus is divided among the firm and its suppliers. Specifically,
the selected supplier gets paid an amount Y and is expected to make an observable but nonverifiable
relationship-specific investment X (X ≤ Y ) . If the selected supplier makes the relationship-specific
investment the firm enjoys the surplus corresponding to the supplier’s actual fit φˆ during this period;
however, since X is nonverifiable, the firm has no legal recourse against a selected supplier that fails to
make the investment X, even thoughthe investment is observable and thus it knows that the supplier did
not invest. In other words, X can be observed but cannot be contractually enforced in a court of law. All
participants are risk-neutral and discount future costs and benefits at a discount factor δ per period, where
0 < δ ≤ 1.
The selected supplier in each period chooses whether tocooperate (invest X) or defect (fail to
invest). If the supplier defects, that supplier is never used in the future and is replaced by a new supplier.
Given there arenlong-term suppliers, a trustworthy supplier will be selected once every n periods in
expectation. A cooperating supplier receives a payoff of Y − X every n-th period, for a present value
of (Y − X) (1− δ ) . A defecting supplier receives Y and then is fired. In order to induce cooperation, a
n
firm must offer the selected supplier a flow of benefits whose net present value exceeds the value the
supplier can keep by simply defecting. Solving (Y − X) (1− δ ) ≥ Y we see that, a firm must offer the
n
selected
supplier
a
period
is ∏(n) = f (α ,n) − κn − Xδ −n ,
payoff Y ≥ Xδ −n .
which
is
maximized
Thus
for
the
a
firm’s
finite
expected
number
of
payoff
long-term
suppliers n* ≥ 1.Furthermore, the optimal number of suppliers n *increases as α increases, as
Page 9
κ decreases, as X decreases and as δ increases. This result shows that the firm selects the largest number
of long-term suppliers that still supports the cooperative equilibrium among these suppliers. In that case,
the supplier receives the higher of two payoffs: the minimum payoff required to sustain cooperation and
what can be obtained because of the supplier’s bargaining power. Assuming that in a period where the
firm has access to n suppliers the coordination cost of κn is sunk, the cooperation of the firm and the
supplier selected each period generates expected incremental value of f (α,n) − X . Following Hart and
Moore (1990), we can apportion the surplus based on each participant’s Shapley Value, so that the firm
will appropriate ( f (α,n) − X)
f (α,n) − X
n
and the suppliers will collectively appropriate
, which will
n +1
n +1
be collected by the one supplier selected in each period. Thus in each period the selected supplier will get
paid Y = max⎛
⎝
f (α ,n) − X
⎛ f (α ,n) − X
⎞
⎞
+ X, Xδ −n and will enjoy profit equal to Y = max
, X(δ −n − 1) .
⎝
⎠
⎠
n +1
n +1
2n
n −1
⎞
The firm’s corresponding profit will be min⎛
f (α ,n) − κn −
X, f (α ,n) − κn − Xδ −n .
⎝ n +1
⎠
n +1
In conclusion, the number of suppliers may be limited by the coordination cost if κ is large, or by
the need to support cooperation if X is large or δ is small. The firm may need to offer the suppliers more
than the minimum required to sustain cooperation if their Shapley Value is higher (for instance because n
is small or f (α ,n) is strongly increasing in n). Since the selected supplier gets paid Xδ − n , the firm will
−n
choose the supplier’s required investment to maximize V ( X ) − Xδ , and thus unless δ = 1the supplier
will underinvest relative to the first-best investment that would maximize the total surplus.
Partially Contractible Investments
We can easily allow supplier investments to be partially contractible. Specifically, let μ denote
the degree in which supplier performance can be codified, and thus included in a verifiable contract, with
0 ≤ μ ≤ 1. If a supplier’s performance is verifiable in degree μ , then a supplier that fails to make its noncontractible investment will receive a payoff of (1− μ )Y . Thus the case analyzed in the base model
corresponds to μ = 0 . The minimum supplier payment required to induce cooperation is given by
X
X
y−X
Y=
,
and
this
gives
. As
n and ∏(n) = f (α ,n) − κn −
=
Y(1−
μ
)
n
μ
+
(1−
μ
)
δ
μ
+
(1−
μ )δ n
1− δ
μ increases, the
optimal number of suppliers n *, which is the maximum number of suppliers that will induce
cooperation, increases, i.e., the firm can employ a larger number of suppliers, as a selected supplier has a
lower incentive to defect.
Page 10
Partially Monitorable Investments
We can also allow supplier investments to be partially monitorable. Specifically, let m denote the
degree in which supplier performance can be monitored, with 0 ≤ m ≤ 1. If a supplier’s performance is
monitorable in degree m , then a supplier that fails to make its non-contractible investment will be
detected (and fired) with probability m . The case analyzed in the base model corresponds to m = 1. The
minimum supplier payment required to induce cooperation satisfies (Y − Xn ) = Y + (1− m)δ n (Y − Xn ) , as
(1 − δ )
(1− δ )
defection will only be detected with probability m , and thus in addition to Y , the defecting supplier will
continue to receive with probability 1− m the value of the game next time it is selected, on expectation
⎛ 1− δ n ⎞
⎛ 1− δ n ⎞
after n periods. Thus Y = ⎜1 +
⎟ X . As m increases, the
n ⎟ X and Π(n) = f (α ,n) − κn − ⎜1 +
mδ ⎠
⎝
mδ n ⎠
⎝
optimal number of suppliers n * increases, becausea defecting supplier is more likely to be detected and
thus a lower future expected benefit from cooperation is required to induce cooperation. Another way to
interpret this result is that as m decreases, the firm benefits from interacting more frequently with its
suppliers as this gives it more opportunities to detect a defecting supplier, making it more likely that any
given supplier will cooperate. Thus, as m decreases, fewer (and more trusted) suppliers will be employed
to maintain the threat of detection and punishment, and therefore cooperation.
Ability to contract outside the set of long-term suppliers
If value can be created without the observable but nonverifiable supplier investment, it may be
advantageous to the firm to go outside its set of n long-term suppliers when the realized fit from these
suppliers is unsatisfactory. For instance, suppose that a fraction β ( 0 < β < 1) of the potential value from
contracting with a supplier may be realized without that supplier making the investment X, and the firm
may explore a “new” (i.e., not long-term) supplier from the set of N − n such suppliers by incurring a per
trial cost κ˜ . The basic model corresponds to setting β = 0 . The expected surplus from working with a
new supplier is βφ − κ˜ , and thus the firm will explore new suppliers when this is higher than the surplus
2n
⎛ n −1 ˆ
⎞
f (α,n) −
min
X, fˆ (α,n) − Xδ −n from working with the best of the long-term suppliers, where
⎠
⎝ n +1
n +1
fˆ (α ,n) is the realized value of f (α,n) and the coordination cost κn is treated as sunk.
We denote by ν the probability that the firm will realize higher ex-post surplus by using a new
supplier. For low enough importance of fit (i.e., high β ) and low enough coordination cost with new
suppliers (i.e., low κ˜ ), ν > 0 , in which case ∂ν ∂β > 0 and ∂ν ∂κ˜ > 0 ; furthermore for high enough
Page 11
β and low enough κ˜ the firm will entirely forego long-term supply relationships and set n* = 0 , resulting
in
ν = 1. In general, in each period there will be ν new and 1− ν long-term supplierson average.
Model Predictions
The theoretical model developed in this section has two main implications.First, it establishes the
importance of factors like trust that arise in a multi-period setting and explores how supplier cooperation
can be sustained in such a setting. In a single period model, supplier incentives are provided by reducing
the number of suppliers to the point where they acquire significant bargaining power (Bakos and
Brynjolfsson 1993a, b). In the incomplete contracting framework a participant’s bargaining power and
corresponding incentives are proportional to its Shapley Value (Hart and Moore 1990), which with n
suppliers is n (n +1) for the firm and 1 n(n +1) for each supplier. By contrast, in a multi-period setting,
supplier incentives are proportional to δ −n . For reasonable discount rates and for typical numbers of
suppliers, repeat relationships are significantly more powerful in providing incentives than mechanisms
based on reducing the number of suppliers (see Figure 1). While a mechanism based on the Shapley
Value quickly loses its power as the number of suppliers increases beyond 2 or 3, a mechanism based on
repeated interaction can provide considerable incentives with a significantly higher number of suppliers.2
This allows firms to provide suppliers' incentives without inordinate fit penalties. Furthermore, when fit
considerations dictate a larger number of suppliers, the incentives of these suppliers can be increased by
raising their discount factors, for instance by shortening the length of time between successive
interactions.
Thus our theory argues that an important motivation for reducing the number of suppliers is to
make it easier to maintain repeated relationships with the remaining suppliers. This is an argument that is
absent in the most cited prior papers on this topic (e.g. Malone, Yates and Benjamin, 1987; Bakos and
Brynjolfsson, 1993a,b; Clemons, Reddi and Row, 1993). While it is theoretically possible to reduce the
number of suppliers while also reducing the amount of repetition (e.g. by practicing “serial monogamy”
with one supplier at a time while never returning to a previous supplier), the trust argument we make
predicts that a reduction in suppliers will be empirically correlated with increased repetition.
2
While firms may employ hundreds or thousands of suppliers in total (e.g., see Dedrick et al. 2008), supplier
incentives are determined by the number of alternative suppliers for a particular product or service, as modeled in
our setting.
Page 12
Figure 1: Shapley Value vs multi-period incentives under different discount factors
Second, our model explains how the characteristics of the setting affect the optimal number of
suppliers and the fraction of repeated relationships, and leads to a number of testable hypotheses about
both organizational factors and IT. Specifically, it predicts that the optimal number of suppliers will
increaseas the importance of fit increases, as coordination costs decrease, as non-contractible relationshipspecific supplier investments decrease, and as the contractibility and measurability of supplier
performance increases. Regarding the fraction of repeated relationships, it predicts that in addition to its
inverse relationship to the number of suppliers, it will increase as the importance of non-contractible
relationship-specific supplier investments increases and when coordination costs with new suppliers are
higher.We examine each of these model predictions in more detail in the sections that follow.
IT & Coordination Costs. Coordination theory predicts that firms that employ more information
and communication technologies to communicate with partners and suppliers should be able to coordinate
with more partners at constant or lower cost. Therefore firms that invest in the types of technology that
reduce the coordination cost κ , such as extranets that enable them to identify and evaluate potential
suppliers, third party hosted intranets that facilitate communication with suppliers or project management
software that facilitates coordination of planning and production, are expected to use more suppliers. In
addition, these investments are likely to decrease the coordination cost with new suppliers κ˜ and thus
lead to a lower fraction of repeated relationships.
Scope, Requirements Heterogeneityand the Fit Parameter. Finding suppliers with good fit is
more challenging for firms that have a larger scope of operations and highly heterogeneous or
Page 13
unpredictable supplier needs. Such firms are likely to place a premium on supplier fit (i.e., have
higher α ), and therefore are more likely to employ more suppliers.
Codifiability and Measurability. When the terms and requirements of supplier contracts are
relatively easy to spell out and communicate in a legal contract (i.e., contractibility μ is high), and when
the activities and deliverables of these suppliers can be easily measured and monitored, (i.e.,
measurability m is high), there is less need to provide incentives for noncontractible investments (Fitoussi
and Gurbaxani 2008), and therefore in these situations we would expect firms to employ more suppliers.
Asset Specificity and Supplier Investments. As the specificity of a supplier relationship increases
(i.e., the more unique the requirements or processes faced by the suppliers), the greater the relationship
specific investments required of the suppliers (i.e., the higher are Xand β ) and therefore we would expect
firms to employ fewer suppliers and have a higher fraction of repeated relationships.
Asset specificity is also a factor in the IT used to coordinate supplier relationships as technology
highly specific to the relationship or the vendor can lead to lock-in. Vendors typically take strategic
actions intended to increase switching costs (Chen and Forman 2006). For instance, prior research has
demonstrated that asset specific information technologies such as Electronic Data Interchange (EDI),
which are generally tailored to a given firm or pair of firms, can lock firms into a relationship (Srinivasan
et. al. 1994, Iacovou et. al. 1995, Zhu et al 2006). In our setting, buyer firms that rely on vendor specific
technologies to coordinate their relationships will have high supplier setup cost K and thus may find it
more difficult to engage new suppliers. We therefore expect that the use of vendor specific technologies is
associated with fewer suppliers.
3. Data
To test our theory, we partnered with the research firm Illuminas to develop and implement a
global web-based survey of IT sourcing decisions. The survey was conducted from November 14, 2007 to
December 5, 2007 and the respondents were involved in the management of IT suppliers in 1355 firms in
12 countries. We surveyed a stratified sample of global firms in order to capture a representative sample
of firms of various sizes in the United States, Europe, the Asia Pacific region, and in emerging markets.
Our goal was to sample 250 firms from the United States and 100 firms from each of France, Germany,
the United Kingdom, Australia, China, India, Korea, Japan, Brazil, Mexico and India.
The survey solicited data on IT sourcing behavior (e.g. number of bids solicited, number of
vendors contracted with, number of vendors used for implementation, and the ongoing nature of
relationships), organizational and technological determinants of supply base size (e.g. asset specificity,
technology use, codifiability of contracts, mutual monitoring, decentralization of decision rights), IT
Page 14
investments and use (e.g. total IT expenses per employee, percent of IT expenditures outsourced,
deployment of coordination IT and vendor specific IT) and firm characteristics (e.g. firm size, location of
headquarters and global establishments).
The sample was stratified to include 25% small enterprises (between 20-100 employees), 25%
medium sized enterprises (between 100-999 employees in the United States and between 100-499 firms in
the rest of the world), and 50% large enterprises (greater than 1,000 employees in the United States and
greater than 500 employees in the rest of the world). The resulting sample included 333 firms with less
than 100 employees (25%), 425 firms with between 100 and 999 employees (31%), and 479 firms with
more than 1000 employees (35%) (Mean Number of Employees = 10,463, S.D. = 39,705.56, Min = 20,
Max = 500,000), for a total of 1355 firms. The numbers of respondents by region and industry are
displayed in Table 2. Descriptive statistics by country and industry are provided in Tables 3 and 4.
These data provide some of the first empirical evidence on IT supplier networks across different
countries and to our knowledge represent the largest global survey of IT procurement and governance. IT
procurement is a natural context in which to study supply chain governance as vendor selection,
contracting, incentives and fit are critical to sourcing strategies in this setting (Gurbaxani 1996). We
intend to make the data publicly available (subject to oversight by the survey firm) in order to promote
replication of empirical results and to encourage further work on global IT sourcing strategies.
Page 15
Country
Respondents
Percentage
253
18.6%
Professional
Services
162
12.0%
294
21.7%
Manufacturing(No
n-Tech)
158
11.7%
98
7.2%
Manufacturing
(Tech)
117
8.6%
97
7.2%
Software
Development
132
9.7%
99
7.3%
Finance,
Insurance, Real
Estate
122
9.0%
Emerging
Markets
309
22.8%
96
7.1%
Brazil
107
7.9%
Education
82
6.1%
Mexico
102
7.5%
Government
78
5.8%
Russia
100
7.4%
Telecom
85
6.3%
Asia Pacific
400
29.5%
Healthcare
69
5.1%
Australia
98
7.2%
Wholesale
63
4.6%
China
107
7.9%
Non-Tech Retail
44
3.2%
India
97
7.2%
Transportation
52
3.8%
Korea
98
7.2%
Non-Profit
25
1.8%
99
7.3%
Energy
31
2.3%
-
-
Other
39
2.9%
US
Europe
France
Germany
UK
Japan
-
Total
1355
100%
Table 2a: Geographic Distributionof Survey
Respondent Firms
Industry
Construction
Respondents Percentage
Total
1355
100%
Table 2b: Industry Distributionof Survey
Respondent Firms
Page 16
Observations (N=)
Variable
Number of Vendors
Total Employees
Total IT
Expenditures $M
Percent IT
Outsourced
Application Breadth
Percent IT Implem.
Multi-site
Percent IT Implem.
Multinational
Decentralized
Sourcing Decisions
Asset Specificity
Terms Codified
Terms Easy to
Understand
Performance
Measurable
Activities
Monitorable
Project Mgmt
Software
Extranets
Third Party Intranets
Vendor Specific
Web Portal
US
253
Mean
(S.D.)
6.23
(11.56)
14591
(42831)
32.1
(109)
29.7
(23.0)
6.36
(2.87)
29.74
(26.80)
16.02
(22.13)
.25
(.43)
4.08
(.90)
3.47
(1.18)
3.66
(1.10)
3.65
(1.12)
3.43
(1.13)
.30
(.46)
.11
(.31)
.12
(.32)
.18
(.38)
Japan
99
Mean
(S.D.)
3.22
(6.38)
6145
(16296)
35.3
(130)
45.0
(27.8)
4.80
(2.74)
48.98
(38.07)
9.10
(18.66)
.11
(.32)
3.73
(.85)
3.35
(1.04)
3.31
(.93)
3.20
(.99)
3.04
(.97)
.16
(.37)
.11
(.32)
.13
(.34)
.12
(.33)
Germany
97
Mean
(S.D.)
6.13
(13.49)
8441
(50477)
9.2
(32.1)
27.1
(17.9)
4.83
(2.35)
25.09
(25.18)
11.56
(18.03)
.20
(.40)
3.68
(.99)
3.09
(1.19)
3.42
(1.01)
3.28
(1.05)
3.19
(1.11)
.15
(.35)
.10
(.30)
.12
(.32)
.20
(.40)
France
98
Mean
(S.D.)
4.74
(10.79)
13694
(32826)
39.8
(210)
37.18
(24.02)
4.90
(2.51)
29.24
(28.78)
15.48
(24.60)
.19
(.40)
3.89
(1.00)
2.97
(1.11)
3.15
(.96)
3.16
(1.02)
3.08
(1.08)
.17
(.38)
.11
(.32)
.09
(.29)
.13
(.34)
UK
99
Mean
(S.D.)
4.74
(6.63)
10458
(39124)
9.4
(29.8)
35.85
(27.39)
5.94
(2.62)
34.20
(31.02)
14.88
(22.57)
.19
(.40)
3.96
(.82)
3.16
(1.19)
3.49
(1.08)
3.45
(1.08)
3.19
(1.11)
.23
(.43)
.17
(.38)
.10
(.30)
.10
(.30)
Australia
98
Mean
(S.D.)
4.74
(8.68)
5297
(13936)
14.9
(33.2)
43.69
(23.37)
6.11
(2.89)
41.36
(32.95)
11.67
(17.93)
.18
(.38)
4.06
(.76)
3.12
(1.08)
3.46
(1.08)
3.41
(1.05)
3.19
(1.02)
.17
(.37)
.13
(.34)
.09
(.29)
.12
(.33)
China
107
Mean
(S.D.)
3.61
(3.06)
5014
(13909)
7.5
(27.9)
41.87
(21.20)
6.30
(2.22)
34.99
(24.13)
11.18
(15.59)
.27
(.45)
4.05
(.75)
3.52
(.92)
3.70
(.70)
3.76
(.79)
3.54
(.87)
.32
(.47)
.34
(.48)
.25
(.44)
.30
(.46)
Table 3: Descriptive Statistics By Country
Page 17
India
97
Mean
(S.D.)
6.39
(13.71)
5714
(15310)
13.0
(33.3)
41.55
(23.40)
5.99
(2.74)
34.64
(23.58)
15.39
(17.92)
.34
(.48)
4.30
(.88)
3.27
(1.19)
3.61
(1.02)
3.62
(.97)
3.45
(1.02)
.25
(.43)
.19
(.40)
.21
(.41)
.23
(.43)
Korea
98
Mean
(S.D.)
4.30
(6.49)
8774
(49328)
18.3
(39.5)
41.41
(22.77)
3.98
(2.58)
31.23
(23.79)
17.27
(17.76)
.33
(.47)
4.00
(.95)
3.42
(.87)
3.48
(.74)
3.58
(.77)
3.35
(.92)
.25
(.44)
.13
(.33)
.14
(.35)
.17
(.38)
Russia
102
Mean
(S.D.)
4.90
(5.30)
13221
(58603)
4.9
(21.8)
25.19
(21.79)
4.52
(2.67)
32.54
(33.45)
4.57
(8.84)
.10
(.30)
3.90
(.87)
3.68
(1.16)
3.48
(.92)
3.26
(1.05)
3.40
(.92)
.31
(.46)
.10
(.30)
.12
(.32)
.19
(.40)
Brazil
107
Mean
(S.D.)
7.00
(21.05)
14735
(56861)
23.9
(91.7)
31.00
(18.55)
5.06
(2.62)
37.92
(29.96)
11.93
(17.52)
.31
(.46)
4.07
(.89)
3.55
(1.04)
3.74
(.93)
3.72
(.93)
3.52
(.98)
.27
(.44)
.18
(.39)
.13
(.34)
.18
(.39)
Mexico
100
Mean
(S.D.)
10.10
(17.00)
4387
(16589)
16.8
(48.1)
25.33
(14.79)
3.40
(2.65)
35.09
(25.30)
8.42
(16.75)
.16
(.37)
3.87
(.99)
3.13
(.92)
3.51
(.95)
3.52
(.91)
3.35
(.92)
.31
(.47)
.19
(.40)
.18
(.39)
.18
(.39)
Observations (N=)
Variable
Number of Vendors
Total Employees
Total IT Expenditures
$M
Percent IT Outsourced
Application Breadth
Percent IT Implem.
Multi-site
Percent IT Implem.
Multinational
Decentralized Sourcing
Decisions
Asset Specificity
Terms Codified
Terms Easy to
Understand
Performance
Measurable
Activities Monitorable
Project Mgmt Software
Extranets
Third Party Intranets
Vendor Specific Web
Portal
Prof.
Serv.
162
Mean
(S.D.)
4.85
(10.35)
3014
(12538)
7.6
(24.1)
32.28
(20.52)
5.40
(2.79)
35.99
(32.64)
10.06
(17.95)
.16
(.37)
4.02
(.82)
3.31
(1.08)
3.54
(1.01)
3.59
(.99)
3.48
(1.06)
.25
(.44)
.14
(.34)
.17
(.37)
.19
(.39)
Manf.
158
Mean
(S.D.)
3.94
(5.20)
12994
(50127)
26.8
(116.0)
32.64
(23.57)
4.96
(3.04)
27.00
(25.68)
14.41
(23.63)
.21
(.41)
4.00
(.82)
3.41
(1.12)
3.49
(.92)
3.45
(1.01)
3.29
(.99)
.25
(.43)
.12
(.33)
.09
(.29)
.13
(.34)
Tech
Manf.
117
Mean
(S.D.)
9.04
(18.62)
16227
(53612)
17.1
(36.6)
38.39
(22.27)
5.81
(2.98)
29.29
(21.29)
21.40
(24.27)
.38
(.49)
3.95
(.90)
3.27
(1.01)
3.38
(.98)
3.39
(.99)
3.38
(.99)
.35
(.48)
.24
(.43)
.23
(.42)
.30
(.45)
Fin/
Ins/RE
122
Mean
(S.D.)
4.66
(4.34)
12744
(37124)
3.4
(11.3)
31.03
(22.21)
5.64
(2.91)
38.12
(29.10)
17.10
(19.30)
.20
(.40)
3.98
(.93)
3.24
(1.21)
3.51
(.96)
3.36
(1.03)
3.32
(1.07)
.25
(.43)
.13
(.34)
.17
(.38)
.20
(.40)
Constrc.
96
Mean
(S.D.)
5.13
(8.81)
2150
(7439)
9.2
(29.4)
35.13
(21.94)
5.10
(2.94)
30.01
(26.52)
9.97
(15.28)
.22
(.42)
3.92
(.95)
3.43
(1.00)
3.46
(.93)
3.46
(1.00)
3.28
(.98)
.27
(.45)
.17
(.37)
.11
(.32)
.17
(.37)
Telecom
85
Mean
(S.D.)
7.04
(10.52)
10654
(25825)
23.7
(4.27)
35.12
(17.84)
4.74
(2.85)
37.86
(22.09)
15.25
(18.47)
.28
(.45)
3.81
(.94)
3.19
(1.09)
3.58
(.97)
3.49
(.96)
3.26
(.95)
.22
(.42)
.15
(.36)
.08
(.28)
.13
(.34)
Health
Care
69
Mean
(S.D.)
4.51
(6.30)
14618
(37420)
15.8
(59.8)
32.48
(25.76)
5.61
(2.68)
32.43
(30.23)
10.44
(18.50)
.16
(.36)
4.00
(.94)
3.38
(1.10)
3.55
(1.06)
3.51
(1.08)
3.38
(1.08)
.20
(.41)
.12
(.32)
.07
(.26)
.14
(.35)
Table 4: Descriptive Statistics By Industry
Page 18
Wholesale
63
Mean
(S.D.)
3.87
(4.84)
2946
(8233)
13.8
(37.8)
38.17
(27.38)
5.62
(2.88)
28.90
(26.75)
12.77
(18.42)
.14
(.35)
3.87
(1.00)
3.22
(1.18)
3.35
(1.18)
3.40
(1.14)
3.03
(1.09)
.21
(.41)
.14
(.35)
.08
(.27)
.14
(.35)
Retail
44
Mean
(S.D.)
3.91
(4.60)
2756
(10575)
2.1
(5.4)
29.25
(26.65)
6.07
(2.79)
43.68
(32.89)
9.74
(19.90)
.14
(.35)
3.75
(1.04)
3.07
(1.17)
3.30
(1.05)
3.34
(1.12)
3.20
(.98)
.11
(.32)
.09
(.29)
.09
(.29)
.16
(.37)
Energy
31
Mean
(S.D.)
10.27
(21.50)
18854
(42225)
149.0
(380.0)
34.1
(28.56)
4.65
(2.71)
42.33
(36.33)
6.93
(10.75)
.23
(.43)
4.13
(.72)
3.58
(.92)
3.68
(.87)
3.55
(.89)
3.48
(.93)
.29
(.46)
.06
(.25)
.13
(.34)
.16
(.37)
Govt.
78
Mean
(S.D.)
3.79
(3.44)
27431
(67728)
35.0
(102.0)
38.09
(25.19)
5.18
(2.25)
41.25
(35.15)
7.48
(17.17)
.12
(.32)
4.18
(.89)
3.85
(1.14)
3.76
(1.18)
3.78
(1.10)
3.62
(1.20)
.22
(.42)
.14
(.35)
.08
(.27)
.18
(.39)
Edu.
82
Mean
(S.D.)
4.10
(6.44)
7674
(50588)
14.7
(63.7)
32.67
(22.76)
5.34
(2.64)
21.93
(26.36)
9.24
(15.91)
.24
(.43)
3.94
(.98)
2.99
(1.17)
3.38
(1.03)
3.26
(1.10)
2.98
(1.08)
.12
(.33)
.10
(.30)
.12
(.32)
.22
(.41)
Figure 2: Global Trends in the Number of Suppliers
Trends in Global IT Supply and Procurement
Firms in our sample work with 5.5 suppliers of IT products and services on a regular basison
average, with some firms working with up to 200 suppliers at a time. The data reveal several interesting
trends. First, a plurality of these firms are maintaining a stable supply base, while significant minorities
are moving to fewer suppliers ormoving to more suppliers: 40% of firms have increased the number of
suppliers they solicit bids from compared to five years ago (11% have reduced the number they solicit
bids from), 32% have increased the number of suppliers they purchase IT hardware and software from
(14% have reduced this number), 30% have increased the number of suppliers they use for IT
implementation (15% have reduced this number), and 28% have increased the number of suppliers they
engage for the purchase of contract IT services (15% have reduced this number). A substantial fraction of
firms (42% to 50%) have maintained the same number of suppliers over the last five years (see Figure 2).
Globally, firms solicit bids from 6 vendors per solution on average – twice as many as the average of 3
vendors they actually purchase from.
The data indicate that in determining the number of suppliers, firms adopt one of two diverging
approaches. Firms reporting a reduction in the number of suppliers employ statistically different mean
numbers of suppliers than firms reporting an increase in the number of suppliers (4.5 compared to 7.1, tstatistic = -1.87, p < .05) suggesting that moves to fewer and more suppliers among two subpopulations in
Page 19
our sample represent two distinct diverging strategies. Firms reporting a move to fewer suppliers over the
last five years cite a desire to ‘build strategic partnerships with a smaller number of trusted vendors’ and
a need to ‘simplify IT deployments’ as the top two reasons for reducing their number of suppliers,
providing some evidence that trust and repeated interaction are correlated with smaller supplier
networks.Firms reporting a move to more suppliers over the last five years cite the need to ‘gain (access
to) a wider array of business and technical expertise’ to fit the needs of the firm and the ‘organization’s
overall growth needs’ as the top two reasons for increasing their number of suppliers, providing evidence
of the influence of fit needs on increasing the number of suppliers.
80
“Thinking about the vendors from whom you solicited bids to
purchase your IT solution, how many were . . .”
64
60
Global
48
US (A)
44
41
40
42
Europe (B)
40
Emerging Markets (C)
Asia (D)
Japan (E)
20
13
11
13
14
16
11
9
11
11
13
10
5
7
8
8
7
7
3
0
Currently working with
your organization
79
80
Used in the last 5 years,
but not currently working
with your organization
Researched, certified or
vetted but never worked
with before
Your organization had no
contact with prior to this
project
“Thinking about the vendors with whom you ultimately contracted to
purchase, how many were . . . ”
60
55
53
55
55
50
40
20
13
10
9
9
9
7
9
7
8
3
8
6
2
7
7
6
5
1
0
Currently working with
your organization
Used in the last 5
years, but not currently
working with your
organization
Researched, certified or
vetted but never worked
with before
Figure 3: Country Differences
Page 20
Your organization had no
contact with prior to this
project
Second, we see strong evidence of repeated interactions and ongoing relationships. Globally, 72%
of the bids solicited by firms in our sample were from vendors they are currently working with or have
worked with in the last five years, while only 11% were from vendors they have never had contact with
before. Evidence of repeated interactions is also strong when looking at actual purchases. Globally, 74%
of the suppliers used by firms in our sample are vendors they are currently working with or have worked
with in the last five years, while only 10% are vendors with no prior contact. After components are
purchased, a majority of firms work with the same vendors for implementation (64%) and for ongoing
service (54%) as those they purchased the IT solution from. Thus the majority of buyer-supplier
relationships in markets for IT products and services develop through repeated interactions over time and
spot markets characterized by one-off purchases from unknown vendors are rare.
Third, as shown in Figure 3, there are considerable differences in supplier strategiesacross
countries, and in particular the number of suppliers firms contract with (notably Japan vs. US vs. Europe)
and across industries (notably high tech vs. low tech). For example, Japanese firms in our sample tend to
maintain significantly fewer supplier relationships and tend to work with the same suppliers repeatedly.
On average, Japanese firms work with 3.2 IT suppliers on a regular basis, while U.S. firms work with 6.2
IT suppliers, a statistically significant difference (t-statistic = -2.45, p < .01).
Figure 4: Size of IT Supply Networks by Industry
Page 21
In addition, for Japanese firms, 79% of the suppliers they contract with are firms they have
worked with in the past and are currently working with, while in the U.S. this figure is only 53%, in
Europe 55%, in emerging markets 50%, and in Asia 55%. Across countries, the average number of IT
suppliers firms work with on a regular basis ranges from 3.2 in Japan to 10.1 in Mexico. There are also
considerable differences across industries as shown in Figure 4. High tech sectors tend to work with
significantly more IT suppliers on a regular basis (Energy = 10.3 suppliers, Software Development = 9.1,
High-Tech Manufacturing = 9.0, Telecom = 7.0), while low tech sectors tend to engage fewer suppliers
on average (Non-Tech Manufacturing = 3.9, Non-Tech Retail = 3.9, Wholesale = 3.9, Government = 3.8).
Finally, our data show that there is a high degree of asset specificity in supply relationships for IT
products and services and that contracts are difficult to codify, monitor and measure. Three quarters of
organizations believe that vendors must acquire ‘significant information, knowledge and skills specific to
their company to adequately deliver on either formal or informal terms and requirements,’ while nearly
three-in-five believe that the ‘activities, skills and knowledge they require of their vendors are
significantly different from other firms in their industry.’ A majority of organizations believe that ‘most or
all of the specific terms and requirements of their IT vendor relationships’ are not ‘codifiable’ (55%),
‘monitorable’ (66%), or ‘measurable’ (50%). These broad trends and statistics highlight the heterogeneity
in firms’ strategies and the need for integrative theory that captures the complexity and diversity of
sourcing governance decisions. In the next two sections we develop an integrative empirical framework
that addresses the heterogeneity of sourcing strategies observed in our data.
4. Empirical Estimation
Variable Construction
Dependent Variables
Our primary empirical goal was to estimate how certain firm characteristics, suggested by theory
and our analytical model, are correlated with the number of suppliers firms employ and the degree to
which they engage in repeated relationships with their suppliers over time. Thus the dependent variables
in our empirical estimations are the fraction of suppliers in a repeated relationship, which we measured by
asking respondents to specify the percentage of suppliers (i.e., a value between 0 and 100) with whom
their firm is already working with or has worked with in the last five years (Percent of Repeated
Relationships), and the number of IT vendors firms work with on a regular basis (Number of Vendors).
We also used Number of Vendors as an independent variable, to investigate its effect on Percent of
Repeated Relationships.
Page 22
Independent Variables
IT & Coordination Costs. To measure the use of information and communication technologies to
communicate with partners and suppliers, we asked respondents to report their firms’ use of a) project
management software, b) extranets, and c) third party hosted intranets to communicate with suppliers, all
measured by yes/no answers and recorded as binary dummy variables. We added these three responses to
construct a variable measuring the use of IT for coordination with suppliers (Coordination IT).
Scope, Requirements Heterogeneityand the Fit Parameter. To measure firm scope, the likely
heterogeneity of firms’ IT requirements and the difficulty firms face in fulfilling or “fitting” their IT
needs, we measured the breadth of the application types each firm implemented and the degree to which
their IT implementations involved a single site or multiple sites in one or more countries. To measure
application breadth, which estimates firms’ variety of IT needs, we asked respondents whether they had
implemented any of a list of ten solution types. We calculated application breadth as the number of these
different application types that the firm implemented (Application Breadth).3 To measure the scope of
implementations we asked respondents what percentage of their IT implementations involved a single site
vs. multiple sites in one or more countries (ImplementationPercent Single Site).
Codifiability and Measurability. In order to measure the degree to which the terms and
requirements of vendor contracts are codifiable and the degree to which suppliers’ activities and output
are observable, we created two distinct yet complementary survey questions for each dimension and we
asked respondents to “think about formal expectations, including contracts and formally specified
deliverables.”
To measure codifiability we solicited responses along two dimensions: the degree to which terms
and conditions could be legally codified in a contract and the degree to which the terms and requirements
were easy to communicate and understand. To assess the former we asked respondents: “When thinking
about the specific terms and requirements you agree to with IT vendors, how many of those terms and
requirements would you classify as codified – meaning that a written and agreed upon document specifies
in detail your terms and requirements” (Codifiable Terms). To assess the latter we asked: “When thinking
about the specific terms and requirements you agree to with IT vendors, how many of those terms and
3
To measure application breadth we asked respondents whether their firm had implemented any of the following applications
and simply counted the number of application types they had implemented: Voice over IP, Video Conferencing, Network
Infrastructure Solutions, Network Security Solutions, Server or Data Center Solutions (including Server Virtualization or
Storage), Disaster Recovery or Business Continuity Solutions, Application Integration or Middleware Solutions, Enterprise
Software Solutions, Website Development, Managed IT Services Solutions, Industry or Vertical Specific Solutions.
Page 23
requirements would you classify as easy to communicate and understand – meaning that your terms and
requirements are easy to explain and vendors find them easy to understand” (Clear Requirements).
In order to assess measurability we measured the degree to which vendors’ activities and outputs
can be easily measured and monitored. We asked respondents whether the specific contractual terms and
requirements they agree to with IT vendors can be classified as “measurable in outcome” (Measurable
Performance) and also assessed the monitorability of vendors’ activities by asking the following
question:“When thinking about the specific terms and requirements you agree to with IT vendors, how
many of those terms and requirements would you classify as monitorable – meaning that vendor activities
specified in your terms and requirements can be easily observed and monitored.” (Monitorable Activities)
Asset Specificity and Supplier Investment. Asset specificity is a proxy for the amount of
relationship specific investments that suppliers must make in order to fulfill a given buyers’ requirements.
In order to assess this dimension of asset specificity and to proxy for relationship specific investments
required of suppliers we asked respondents to agree or disagree with the following statement and rated
their agreement or disagreement on a five point Likert scale: “Your vendors must acquire significant
information, knowledge and skills specific to your company to adequately deliver on either formal or
informal terms and requirements.” (Asset Specificity) We normalized the above five variables
(CodifiableTerms, ClearRequirements, Measurable Performance, Monitorable Activities, Asset
Specificity) by subtracting from each observation the mean and dividing by the standard deviation of the
corresponding variable.
Asset specificity is also at play in the information technologies used to coordinate the
relationships between buyer and suppliers. Technologies used to coordinate the relationship that are
highly specific to the relationship or the vendor can lead to lock-in by increasing the switching cost to a
different supplier. We measure the use of such vendor specific IT in supply relationships by asking
respondents whether they utilized a) “custom client web-portals provided by your vendors” (Vendor
Specific Portals), b) “collaboration and document management tools” (Vendor Collaboration Tools) and
c) “vendor relationship management tools” (Vendor Management Tools) to “communicate with your IT
vendors,” all measured by yes/no answers and recorded as binary dummy variables. We added these three
responses to construct a variable measuring the use of vendor-specific IT to support supplier relationships
(Vendor Specific IT).
Page 24
Control Variables
Firm Scale. In order to control for scale effects we asked respondents to report firms’ total
number of employees (Total Employees). We normalized this variable by subtracting from each
observation the mean and dividing by the standard deviation of the variable.
IT Expenses. In order to control how a firm’s total IT budget might affect the number of IT
suppliers, we asked respondents to report their firms’ “total IT expenditures including all computers,
software, data communications (including via phone line), and people” (Total IT $). We normalized the
total IT expenditures variable by subtracting the mean from each observation and dividing by the standard
deviation.
Outsourcing. Firms that rely more on market procurement may have experience with outsourcing
and may have developed processes for selecting and vetting suppliers and a proclivity for market
procurement. To control for unobservable firm characteristics correlated with a reliance on the market, we
control for the percentage of IT expenditures outsourced (% Outsourced), measured independently of the
number of suppliers firms work with.
Table 5 shows the variables we constructed from the survey data, the corresponding parameters in
our theoretical model, and the corresponding expected sign of the regression coefficientsin predicting the
fraction of repeated supplier relationships. This last column is based on the hypotheses derived from our
theoretical model and thus summarizes these hypotheses. Table 6 shows the corresponding relationships
for thenumber of suppliers.
Constructed Variable
Percentage of Repeated
Relationships
1. Total Employees
2. Total IT $
3. Percent IT Outsourced
4. Number of IT Vendors
5. Asset Specificity
6. Number of IT Vendors
XAsset Specificity
Characteristic of
Organizational
Setting
Long-term supplier
relationships
Firm scale
Firm scale
Firm scale
Number of suppliers
Asset specificity
Increased importance
of trust when
relationship-specific
supplier investments
are important
Coordination cost
Corresponding Model
Parameter
Fraction of Repeat
Suppliers ( 1− ν ))
Control variable
Control variable
Control variable
Number of suppliers
(N)
Importance of supplier
investment ( β )
Interaction term
Predicted
sign of
coefficients
Dependent
Variable
---β4 < 0
β5 > 0
β6 < 0
β7 < 0
Coordination cost for
˜
new suppliers( κ )
Table 5: Empirical VariablesPredicting the Fraction of Repeated Relationships
7. Coordination IT
Page 25
Constructed Variable
Characteristic of
Organizational Setting
Corresponding
Model Parameter
Number of suppliers
Number of suppliers
(N)
Control variable
Control variable
Control variable
Fit parameter ( α )
Predicted
sign of
coefficients
Dependent
Variable
---β4 > 0
Fit parameter ( α )
β5 < 0
Supplier investment
(X)
Contractibility ( μ )
Contractibility ( μ )
Measurability ( m )
β6 < 0
Number of IT Vendors
1. Total Employees
2. Total IT $
3. Percent IT Outsourced
4. Application Breadth
Firm scale
Firm scale
Firm scale
Scope/heterogeneity of
requirements
Scope/heterogeneity of
requirements
Asset specificity
5. IT Implementation
PercentSingle-Site
6. Asset Specificity
7. CodifiableTerms
8. ClearRequirements
9. Measurable
Performance
10. Monitorable Activities
11. Coordination IT
12. Vendor Specific IT
Codifiability
Codifiability
Measurability
Measurability
Coordination cost
Switching cost/Lock-in
Measurability ( m )
Coordination cost ( κ )
Supplier setup cost
(K)
Table 6: Empirical Variables Predicting the Number of Suppliers
β7 < 0
β8 > 0
β9 > 0
β10 > 0
β11 > 0
β12 < 0
Empirical Specifications
To better understand the roles of trust, cost and incentives in supplier networks we estimated the
relationships between our parameters of interest, the fraction of suppliers with whom firms engage in
repeated relationships, and the number of IT vendors in firms’ IT supply networks.
First, in accordance with Table 5,we estimated three linear models predicting the fraction of
repeated relationships firms use based on measures of firm size, IT expenditures, the fraction of IT
budgets outsourced, the number of IT vendors, asset specificity and coordination IT on using Ordinary
Least Squares (OLS) specifications:
FractRepRelationships= α + β1TotalEmployees + β 2TotalIT $ + β 3 %Outsourced +
β 4 NumberofSuppliers + β 5 AssetSpecificity +
β 6 NumberofSuppliers × AssetSpecificity +
∑β
j
C
j
Country j + ∑ β nI Industry n + ε
n
We then estimated, in accordance with Table 6, five models based on the following linear
regression ofmeasures of firm size, IT expenditures, the fraction of IT budgets outsourced, application
Page 26
breadth, the percentage of single-site IT implementations, asset specificity, contractibility and individual
information technologies on the number of IT vendors in firms’ IT supply networksusing Ordinary Least
Squares (OLS) specifications:
α + β1TotalEmployees + β 2TotalIT $ + β 3 %Outsourced +
β 4 AppBreadth + β 5 %SingleSite + β 6 AssetSpecificity + β 7CodifiableTerms +
β 8ClearRequirements + β 9 MeasurablePerformance + β10 MonitorableActivities +
NumberofSuppliers=
+ β11CoordinationIT + β12VendorSpecificIT + ∑ β Cj Country j + ∑ β nI Industry n + ε
j
n
Specification Tests and Robustness Checks
We conducted several specification tests and robustness checks of our empirical models to make
sure our analyses were not confounded by data anomalies or specification errors. First, we conducted
Breusch-Pagan and Cook-Weisberg tests for heteroskedasticity, which reject the null hypothesis of
spherical disturbances ( χ =1237.47, p< .01). We therefore implemented Huber-White heteroskedasticity
2
consistent standard errors and note that these reduced the significance of our estimates and generated
much more conservative results. Second, we calculated centered and uncentered variance inflation factors
(VIFs) for the independent variables specified in our models to test for multicollinearity in our
specifications. A variance inflation factor of 10 has been proposed as a cutoff for acceptable levels of
variance inflation due to multicollinearity (Kutner et al. 2004). No variance inflation factors for any of the
independent variables in our models exceeded 6.0 and only 3 of 43 variables exceeded 4.5, indicating
little chance of multicollinearity affecting our results. Third, we clustered standard errors by country and
by industry as additional checks against our data being clustered by region of collection or by industry.
These specifications did not produce estimates that differed in any meaningful way from those we report,
which include dummy variables for the 12 countries and 15 industries in our sample. We also conducted
robustness checks by including controls for industry/country simultaneously to test the effects of
particular industries in particular countries with no change in our results. Finally, to check for the
influence of outliers on our parameter estimates we calculated standardized dfbetas for each of our 43
variables in our two main specifications. Dfbetas show how much a coefficient would change if any given
observation were dropped from the data. Calculating dfbetas for each independent variable produces
variables that ranged from a minimum to a maximum change in the coefficients produced by removal of
any single observation, one for each independent variable. Only one observation in one of 43 variables,
Total IT Expenditure, produced a dfbeta greater than the cutoff of 2 proposed by Belsley et al. (1980),
meaning no influential outliers exist in any of our other independent variables. This one observation
produced a dfbeta of 5.2. We dropped the observation from the regressions to see how it would affect
Page 27
parameter estimates and found no qualitative change in any parameter estimates or the statistical
significance of any parameters. We therefore retained the observation for analysis.
The empirical results, shown in Tables 7 and 8,are robust to these specification tests and
robustness checks and lend support to the hypotheses derived from our integrated theoretical model, as
can be seen by comparing Tables7 and 8 with the rightmost columns of Tables 5 and 6.
5. Regression Results
Determinants of Repeated Relationships with Suppliers
We first examined the determinants of the degree to which firms engage in repeated relationships
with their suppliers. Our model predicts that repeated relationships will be inversely correlated with the
number of employed suppliers. When the importance of fit is high and firms engage a large number of
suppliers in order to maximize fit at the expense of incentives, they are less likely to engage in long-term
contracts and more likely to switch suppliers when fit can be increased by doing so. Our data support this
hypothesis, as reported in Table 7. Model 1 shows a strong correlation between employing fewer
suppliers and engaging them in long-term relationships: firms that take on more suppliers are less likely
to contract with them repeatedly (β4 =-.300, p< .01).Employing one more supplier is associated with a
0.3% decrease in the fraction of repeated relationships on average. These results support the hypothesis
that as firms contract with a large number of suppliers they engage in fewer repeated relationships.
Model 2 shows that, consistent with our predictions about trust, higher asset specificity is
associated with a greater fraction of repeat relationships with suppliers (β5 = 3.95, p< .01).Firms that
require greater specialized information, knowledge and skills from suppliers to adequately deliver on
sourcing requirements not only rely on fewer suppliers for their IT procurement needs, but also enter into
longer term contracts with them, engaging a greater fraction of their supply base in repeated relationships.
A one standard deviation increase in asset specificity is associated with a 4% increase in the fraction of
suppliers with whom firms have previously contracted within the last five years. This supports the
hypothesis that firms requiring suppliers to acquire information, knowledge and skills specific to the
relationship, rely to a greater extent on long term contracting and repeated relationships.
Page 28
Dependent Variable:
Model:
1. Norm. Total Employees
2. Norm. Total IT $
3. Percent IT Outsourced
4. Number of Vendors
5. Norm. Asset Specificity
6. Number of Vendors X
Norm. Asset Specificity
7. Coordination IT
Controls
F-Value (d.f.)
R2
Fraction of Repeated Relationships
1
3
3
-.314
-.142
-.181
(1.01)
(.967)
(.969)
2.01**
1.39**
1.40**
(.757)
(.727)
(.696)
-1.01**
-.111**
-.102**
(.051)
(.0514)
(.052)
-.300*** -.325*** -.277***
(.0966)
(.0957)
(.0966)
3.95***
4.08***
(1.62)
(1.62)
-.245*** -.241***
(.0758)
(0763)
-2.25*
(1.36)
Industry
Industry
Industry
Country
Country
Country
3.98***
(31)
4.44***
(33)
2.84***
(34)
.10
.11
.12
Observations
496
496
496
Notes: OLS estimation with robust standard errors.
*** p < .01; ** p < .05; * p < .10
Table 7: Determinants of the Fraction of Repeat Relationships
We suspect that establishing trusted relationships with suppliers is an important reason for firms
with specific assets, technology and processes to rely on fewer suppliers. Suppliers must make greater
non-contractible relationship specific investments when buyers display greater asset specificity in their
processes and technology. Given the threat of hold up suppliers face when making investments in
learning, process reengineering and technology that are not transferable to other clients, incentives are
necessary to motivate them to make non-contractible investments in the supply relationship. These
incentives can be created by reducing the number of suppliers, thus increasing supplier bargaining power
(Bakos & Brynjolfsson 1993a, b), or by entering long-term relationships (Piore and Sabel 1984, Helper et.
al. 2000). In our sample, of the firms that have moved to fewer suppliers in the last five years 42% say
they did so to build “a strategic network of trusted vendors” (the second most popular response after “to
simplify IT deployments” which was reported by 44% of these firms).
This view is further supported by the interaction between the number of suppliers and asset
specificity, which is highly significant and negatively correlated with the fraction of repeated
relationships (β6 = -.245, p< .01). Thus asset specificity strongly moderates the negative relationship
between the number of suppliers and the fraction of repeated relationships. In other words, under
Page 29
conditions of greater asset specificity, the negative relationship between the number of suppliers and the
percentage of repeated relationships is even more pronounced: when firms’ assets are more specific and
the need for relationship specific investments by suppliers is greater, the percentage of repeated
relationships increases faster as the supply base shrinks.This supports the hypothesis that creating trust
is an important motivation for repeated relationships with suppliers.
Finally in Model 3 we find thatcontrolling for total IT expenditures, the use of information and
communication technologies for the express purpose of communicating with IT vendors isnegatively
correlated with long term repeated relationships. Specifically, the use of Coordination IT is negatively
associated with longer-term relationships (β7 = -2.25, p< .1). This finding is consistent with the hypothesis
thatthe use of ITto reduce the costs of coordination withsuppliers is correlated with a smaller fraction
of repeated relationships with these suppliers, and corroborates the idea that as firms use advanced IT
that reduces coordination and search costs they tend to contract with more suppliers and to have less
repeated interaction with them, switching between suppliers more often as they seek a better fit for their
demand.
Determinants of the Number of Suppliers
We subsequently examined the determinants of the number of suppliers, including specific
variables designed to measure a) the importance of fit, b) asset specificity and trust, c) contractibility and
monitorability, d) use of coordination technologies, and e) switching costs and lock-in due to vendorspecific IT. We present these results in Table 8.
Page 30
Dependent Variable:
Number of IT Vendors
Model:
1
2
5
-.285
(.502)
2.84**
(1.28)
.0388***
(.0149)
.534***
(.160)
-.0298***
(.0112)
-1.38***
(.465)
-.0147
(.434)
.233
(.532)
1.05**
(.522)
-.354
(.636)
Industry
Country
2.19***
(32)
Industry
Country
2.21***
(33)
Industry
Country
2.20***
(35)
Industry
Country
2.11***
(37)
R2
.21
.22
.23
.23
.26
Observations
599
598
598
598
598
2. Norm. Total IT $
3. Percent IT Outsourced
4. Application Breadth
5. Percent IT Implementations
Single-Site
6. Norm. Asset Specificity
7. Norm. Codifiable Terms
8. Norm. ClearRequirements
-.280
-.296
(.510)
(.504)
2.84**
2.82**
(1.29)
(1.28)
.0394*** .0394***
(.0152)
(.0152)
.563***
.533***
(.157)
(.151)
-.0295*** -.0307***
(.0110)
(.0112)
-1.14*** -1.29***
(.443)
(.461)
.00157
(.365)
.675*
(.414)
4
-.237
(.531)
2.75**
(1.25)
.0315**
(.0141)
.403***
(.160)
-.0234**
(.0103)
-1.33***
(.455)
.00173
(.428)
.0375
(.515)
1.09**
(.523)
-.396
(.627)
2.60***
(.758)
-.985**
(.499)
Industry
Country
1.94***
(39)
1. Norm. Total Employees
-.255
(.506)
2.80**
(1.33)
.0381***
(.0152)
.523***
(.159)
-.0276***
(.0110)
3
9. Norm. Measurable
Performance
10. Norm. Monitorable
Activities
11. Coordination IT
12. Vendor Specific IT
Controls
F-Value (d.f.)
Notes: OLS estimation with robust standard errors. *** p < .01; ** p < .05; * p < .10
Table 8: Determinants of the Number of Suppliers
Control Variables
We control for firm size, total IT expenditures and the percentage of IT outsourced. As shown in
Table 8, we do not find evidence of a scale effect on the IT supply base (firm size is unrelated to the
number of suppliers). We do find a positive and statistically significant relationship between Total IT
Spending and the Number of Suppliers (e.g., in Model 1, β2 = 2.80, p < 0.05). IT spending, however, is a
proxy for firms’ total demand for IT, and thus could potentially drive a larger number of IT suppliers
through firms’ demand for more IT. For this reason we treat Total IT spending as a control variable and
Page 31
rely on our specific variables of coordination IT and vendor-specific IT to assess the impact of IT on the
number of suppliers, controlling for total IT demand.
We measure the percent of IT outsourcing mainly to control for unobservable firm effects that
may correlate with the size of the supply base. For example, managers that choose to outsource large
portions of their total IT budget may be inclined to choose a larger supplier base. Thus the fraction of IT
budget outsourced captures characteristics of firms and managers that make them more likely to rely on
the market rather than internal firm organization. Still, the positive correlation between the percentage of
IT outsourced and the number of vendors controlling for firm size and total IT spending (β3 = .381, p <
.01 in Model 1) provides corroboration of the importance of fit costs in predicting supply base size. As
firms increase the fraction of the IT budget that is outsourced they necessarily outsource a greater number
of IT assets and IT labor activities and theirrequirements become more diverse requiring a greater variety
of specialized services. To fit these needs, firms maintain a larger supplier base.
Fit Effects
We see strong evidence of fit effects in the relationship between the breadth of applications used
by the firm and the number of suppliers they employ. In Model 1, after controlling for the size of the firm
and total IT expenditures,implementation of two additional application classes is associated with one
additional supplier on average (β4 = .523, p < .01). This result lends support to our theoretical conclusion
that as the scope of requirements increases more suppliers are needed to fulfill those requirements.
Similarly, there is a relationship between single-site IT implementations (which likely indicate relatively
few idiosyncratic needs compared to multi-site and multi-national implementations) and a smaller number
of suppliers (β5 = -.0276, p < .01).This parameter estimate gives an indication of the relationship between
idiosyncratic needs that vary by the number ofimplementation sites and the need for multiple suppliers to
fulfill those needs: a35% increase in the number of single-site implementations is associated with
employing one less supplier on average. These results lend additional support to the relationship between
fit and search costs and a greater number of suppliers due to the changing nature of requirements across
national boundaries. For instance, in the case of multi-national sites, different national contexts entail
variability in corporate culture, reporting structures and legal requirements for IT systems
implementations that necessitate contracts with vendors with idiosyncratic knowledge of national norms,
laws and regulations. For example, in the United States, vendors of IT systems must be intimately
familiar with Sarbanes-Oxley reporting requirements in order to adequately deliver systems with legal
reporting capabilities that are specific to the U.S. The European Directive on Data Privacy creates similar
knowledge specificity for IT services delivered in European countries. As such, we would expect to see a
Page 32
greater number of vendors needed to fit idiosyncratic firm needs as the percentage of multinational IT
implementations increases.
Overall, the findings on application breath effects, number of implementation sites, and
outsourcing effects support the hypothesis that increased importance of fit induces a firm to employ
more suppliers.
Asset Specificity and Trust
In Model 2 we introduce measures of the asset specificity of vendor investments, and we find
strong support for the role of asset specificity in reducing the number of suppliers. In measuring asset
specificity we asked respondents to rate the degree to which “vendors must acquire significant
information, knowledge and skills specific to the firm to adequately deliver on either formal or informal
requirements.” Firms that require greater specialized information, knowledge and skills from suppliers to
adequately deliver on sourcing requirements rely on fewer suppliers for their IT procurement needs. A
one standard deviation increase in asset specificity is associated with approximately 1.3 fewer suppliers
on average (β6 = -1.29, p< .01). This supports the hypothesis that firms requiring suppliers to acquire
information, knowledge and skills specific to the relationship, rely on fewer suppliers.
Contractibility and Performance Measurement
Models 3 and 4addvariables measuringcontractibility and monitorability. Specifically, the
variables introduced in Model 3 measure the degree to which a written contract can specify all necessary
terms and requirements of the vendor relationship and the degree to which the terms and conditions are
“easy to communicate and understand.” The variables introduced in Model 4 measure the degree to
which, given a written contract, vendors’ activities “can be easily observed and monitored,” and whether
“the quality of the products and services vendors are contracted to provide … can be easily assessed and
measured.”
We find no statistically significant relationship between contractibility and the number of
suppliers; nor do we find evidence of a relationship between the comprehensibility of contracts and the
number of suppliers. We do however find clear evidenceindicating that when the performance of a supply
relationship is measurable, for example because the quality of the products and services provided by
suppliers can be easily assessed, firms tend to employ more suppliers(β9 = 1.05, p < .05). A one standard
deviation increase in measurability is associated with one additional supplier on average. This supports
the hypothesis that greaterability to measure performance, which is one aspect of monitorability,is
associated with employing more suppliers.
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There are several possible explanations for this pattern of relationships. First, the ability to assess
and measure outcomes is a prerequisite for contractibility and is likely to be more important than the
observability of activities. Punishment and reward in repeated interactions is enabled by the observability
of the quality of output. If quality is unobservable or not measurable, it becomes difficult to write a
verifiable contract that requires the fulfillment of products and services to specification. Second, if quality
is measurable, firms have several incentive mechanisms,beyond writing clear understandable and
verifiable contracts, with which they can motivate suppliers to deliver the needed level of quality. For
example, firms could enter intorepeated, long-term interactions with suppliers they trust, and reward (or
punish) the provision (or lack) of quality by extending (or terminating) the relationship, which can
promote cooperation without requiring well specified contracts as demonstrated in our analytical model.
Third, as performance becomes more measurable, buyer firms need only observe supplier output every so
often, interacting with them on fewer occasions. However, since performance measurements are noisy,
firms must constantly interact with their suppliers to monitor performance (and investment). As
performance becomes more measurable, firms can therefore take on more suppliers (and interact with
each less frequently), to increase their bargaining power and lower their fit costs, without an increased
threat of non-cooperative behavior by the suppliers.
Finally in Model 5 we introduce the deployment of coordination IT and vendor-specific IT, while
controlling for total IT expenditures.
Coordination IT
As predicted by Malone, Yates and Benjamin (1987) and Bakos (1997), increased use of three
“coordination technologies” (i.e. extranets, project management software, and third party hosted intranets
like Hyper Office) is associated with a significant increase in the number of suppliers (β11 = 2.60, p <
0.01).4 On average in our sample, each additional coordination technology adopted, on a scale from 0 to
3, was associated with 2.6 additional suppliers. Thus firms that adopted all three coordination
technologies on average employed almost 8 more suppliers in their IT procurement than firms that had
adopted none of the technologies, all while holding total IT demand constant. We cannot necessarily
conclude that coordination technology caused the increase in the number of suppliers—it is possible, even
likely, that both decisions were made jointly and the causality runs in both directions. However, the high
significance level in the association suggests that it is not a mere coincidence that these decisions co-vary,
representing some of the most direct empirical evidence to date that supports the predictions of
coordination theory in buyer supplier relationships. In particular, these findingsare consistent with the
4
Each of these technologies was also statistically significant at at least the 0.1 level when considered individually.
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main prediction of coordination theory—that IT facilitates less costly communication and collaboration
with suppliers, enabling firms to coordinate multiple relationships at constant or lower costs,and that the
use of IT to support coordination with IT vendors is correlated with a larger number of suppliers.
Vendor-Specific IT
On the other hand, we find that use of three vendor-specific technologies (i.e., vendor-specific
portals, collaboration and document management tools, and vendor management systems), which can
create switching costs and cause lock-in to specific vendors, is associated with a smaller number of
suppliers (β12 = -0.985, p < .0.05). On average, each additional vendor-specific technology adopted, on a
scale from 0 to 3, is associated with 1 fewer supplier. Thus firms that adopted all three vendor specific
technologies on average employed 3 fewer suppliers than firms that had adopted none of these
technologies, again controlling for total IT demand. This supports the hypothesis that technologies that
increase a firm’s switching costs are associated with the use of fewer suppliers. This result corroborates
both the notion that asset specificity encourages use of a smaller supplier base in conjunction with
relationship specific investments and highlights that different types of IT have different implications for
supply chain governance. In particular, vendor-specific technologies that increase switching costs are
likely to lead to fewer suppliers while technologies that lower search and coordination costs are likely
to lead to an increased number of suppliers.
6. Discussion
Our theoretical framework integrates existing theories about increases and decreases in the
number of suppliers and the degree to which firms engage in repeated relationships with suppliers.
Specifically, we analyze the role of fit, search costs, coordination theory, transaction costs and asset
specificity, incomplete contracts, and cooperation in multi-period settings. In addition, our multi-period
model allows us to consider the critical role of trust in supplier relationships.
Weassess the implications of our model using empirical evidence from a unique data set obtained
via a tailored survey of 1355 large multinational firms’ IT sourcing decisions. Using multiple regression,
we identified the key characteristics of the organizational, industrial and technological setting which were
associated with both the use of repeated relationships and more or fewer suppliers. Of particular note, we
found that a reduction in suppliers was strongly associated with an increase in repeated relationships,
consistent with our integrative model. Moreover, each of the key variables suggested by our integrative
model had the predicted signsin the empirical analysis. Specifically, organizational and technological
asset specificity, the desire to develop trust and the need to induce non-contractible investments were
correlated with fewer suppliers, while reductions in search and transaction costs, the need for fit between
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firms’ needs and suppliers’ skills, and the ability to measure supplier performancewere correlated with
more suppliers. Our analysis reveals that non-contractible specific supplier investments and trust are
particularly important in leading to smaller supplier networks.
Our model and empirical results underscore the need for an integrated approach to understanding
supplier networks. In particular, there are multiple forces at work, both technological and organizational.
Prior models, which focused on only one component, provide useful insights, but are ultimately incapable
of fully capturing the trade-offs faced by firms as they make decisions about their supplier networks. Our
model shows how information technology can reduce search and transaction costs, facilitating
relationships with a larger number of suppliers, but can also lock firms in to a smaller set of suppliers.
The empirical analysis shows that both effects are significant. Furthermore, organizational idiosyncrasies,
like the benefits of improved fit between needs and capabilities, suggest that increasing the number of
suppliers will be beneficial, but that at the same time, the need to foster trust pushes firms toward working
with fewer suppliers, at least when there are important non-contractible relationship specific investments
at stake. Again, our model captures both types of effects and the data are rich enough to identify and
distinguish them.
Perhaps the most important theoretical contribution is the explicit analysis of a multi-period game
between a firm and its suppliers. The repeated nature of the relationship brings to the fore the importance
of trust, as highlighted by the empirical coefficients on asset specificity and performance measurement.
Greater asset specificity increases the risks associated with untrustworthy relationships, and as theory
predicts, drives firms toward working with a smaller set of trusted suppliers. Conversely, when
performance measurement is improved, firms can more easily expand their supplier base while
implementing incentive contracts with a high signal to noise ratio.
We also find empirical support for trust-based mechanisms in our analysis of repeated
relationships. The desire to fit idiosyncratic needs that vary across countries and offices encourages firms
to reduce the number of repeated relationships they enter into. In contrast, we find, consistent with our
predictions about trust, that higher asset specificity is associated with more long term contracting and a
greater fraction of repeat relationships with suppliers. Firms that require greater specialized information,
knowledge and skills from suppliers to adequately deliver on sourcing requirements not only rely on
fewer suppliers for their IT procurement needs, but also enter into longer term contracts with them,
engaging a greater fraction of their supply base in repeated relationships.Finally, we find the use of
coordination ITisnegatively correlated with long term repeated relationships. This finding corroborates
the idea that as firms use advance IT to lower search and coordination costs they tend to contract with
Page 36
more suppliers and to have less repeated interaction with them, switching between suppliers more often as
they seek a better fit for their demand.
In light of the multiple and potentially conflicting determinants of the number of suppliers and the
repeated nature of supply relationships, it is not surprising that some firms have been increasing the
number of suppliers they work with even as others work with fewer suppliers. Firms face different
organizational and technological environments and thus their optimal strategies vary. Our integrated
model highlights these trade-offs, unifying the relevant theories and promoting a contingent rather than a
deterministic view of the role of IT in supply chain governance. Our empirical analysis of a new, largescale dataset supports the notion that each of the components of the integrated theory has something to
contribute to our overall understanding of trust, costs and incentives in global supplier networks.
Limitations and Future Work
Although we have analyzed some of the first large scale data on IT sourcing decisions and
developed a unified model of the size of global supplier networks, integrating several theories that
provide conflicting predictions about supply base augmentation and reduction, two important limitations
to our analyses remain.
First, as with most empirical examinations of sourcing strategies, we do not have data on supplier
markets. Clearly, the state of the sourcing market can influence firms’ choices regarding which suppliers
they choose, the number of suppliers they work with on a regular basis and the length of their contracts.
Competitive markets can increase firms’ options and the relative stability of established supplier firms can
affect the degree to which buyers rely on a particular firm for extended periods of time (Gao et al 2010).
In addition, the emergence of new low cost IT suppliers in emerging markets like India and Russia could
also affect firms’ sourcing decisions as well as the size of their supplier networks. Our controls for
industry and country effects should in part control for differences in supplier markets at the country and
industry level, but some effects could remain. Unfortunately, we do not have access to primary data
concerning the market structure of IT supply. We encourage data collection on supply side market
structure and future work on supplier networks that takes market structure into account.
Second, our data are not longitudinal. We solicit data on trends in both bid solicitation and
contracting, such as the move to fewer or more suppliers over the last five years and whether a given
supply relationship has been repeated over time, but we do not observe the same firms’ sourcing decisions
over time in panel data. Although data on the degree to which relationships are repeated allow us to test
our theoretical model, the lack of temporal variation in supply base data and accompanying firm
characteristics makes it difficult to make causal claims about what drives firms’ sourcing decisions. While
Page 37
our data give indications about the types of firms that typically have larger or smaller supplier networks
and repeated or short lived relationships with suppliers, unobserved heterogeneity may still exist and we
cannot claim to have identified the causal mechanisms that explain these relationships.
7. Conclusion
This paper addressed two key gaps in current research on the role of IT in supply chain
governance: the lack of theoretical and empirical focus on repeated relationships and the role of distinct
sets of information technologies with varying implications for supply chain structure.
We developed an integrated, multi-period model of the optimal number of suppliers that
combines considerations from search and coordination theory, transaction cost economics, and
incomplete contracts theory, and assessed our theoretical predictions using a new tailored dataset on the
global IT sourcing decisions of 1355 firms in 12 countries. Our empirical results are consistent with this
model. In particular,a reduction in the number of suppliers can be explained bythe desire to develop trust
and the need to induce non-contractible relationship-specific investments and by organizational and
technological asset specificity. On the other hand, investments in coordination technologies that reduce
search costs and transaction costs are correlated with more suppliers, as are the need to optimize fit
between firm needs and supplier skills. A key insight of both our model and empirical analysis is that the
cooperation and trust developed through repeated interaction can offer strong incentives for relationshipspecific supplier investments.
Page 38
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