Journal of Operations Management 30 (2012) 55–68

Contents lists available at ScienceDirect

Journal of Operations Management
journal homepage: www.elsevier.com/locate/jom

The inﬂuence of exchange hazards and power on opportunism in outsourcing
relationships
Sean M. Handley a,∗ , W.C. Benton Jr. b
a
b

Department of Supply Chain Management & Marketing Sciences, Rutgers Business School, Rutgers University, 1 Washington Park, Ofﬁce 986, Newark, NJ 07102, United States
Department of Management Sciences, Fisher College of Business, The Ohio State University, 2100 Neil Avenue, Columbus, OH 43210, United States

a r t i c l e

i n f o

Article history:
Received 29 October 2010
Received in revised form 2 June 2011
Accepted 10 June 2011
Available online 21 June 2011
Keywords:
Outsourcing
Inter-organizational power
Transaction cost theory
Opportunism

a b s t r a c t
Service provider opportunism is widely noted as a principal risk with outsourcing. Indeed, economic
theory regarding the factors which inﬂuence the outsourcing decision, treats opportunism as a core
behavioral assumption. It is assumed that if given the opportunity, outsourcing providers will act in a selfserving manner despite the potentially negative impact it may have on their customer. Other researchers
have suggested that opportunism is not an unwavering human behavior, but rather can be substantively
inﬂuenced by the management practices which deﬁne the relationship. Building on these arguments,
this study investigates the validity of these divergent positions. Hierarchical linear regression is used to
examine dyadic data on 102 information technology, logistics, and other business process outsourcing
relationships. We test a model which hypothesizes that the buying ﬁrm’s reliance on different bases of
inter-ﬁrm power will have differing effects on the risk of opportunism (shirking and poaching). These
hypotheses are evaluated while concurrently examining the inﬂuence of exchange hazards (relationshipspeciﬁc investments and technological uncertainty) on provider shirking and poaching. The results offer
strong evidence that buyer reliance on mediated forms of power (i.e. rewards, coercive, legal legitimate) enhance the risk of both provider shirking and poaching, while non-mediated power (i.e. expert,
referent) is associated with a diminished level of opportunistic behavior. Interestingly, relationshipspeciﬁc investments have a signiﬁcant effect on some forms of opportunistic behavior but not on other
forms of opportunistic behavior. Technological uncertainty did not have a signiﬁcant impact on provider
opportunism.
© 2011 Elsevier B.V. All rights reserved.

1. Introduction
In 2006, the Texas Department of Information Resources (DIR)
entered into an $863 million, 7½ year outsourcing contract with
IBM to consolidate and manage the state’s data center operations.
The initiative was projected to save the state $178 million by 2014
(Garrett, 2010). However, according to a consultant hired by the
state, savings had accumulated to a mere $10 million in the ﬁrst two
years (Garrett, 2010). Moreover, the contract called for server consolidation to be complete by the end of 2010. As of July 2010, only
10% of the consolidation had been completed (Thibodeau, 2010).
As a result, the Executive Director of the Texas DIR presented IBM
with a “Notice to Cure” alleging 15 contractual breaches (Garrett,
2010; Thibodeau, 2010). According to the notice “The accumulated
effect of under-investment by IBM, poor performance, and con-

∗ Corresponding author.
E-mail addresses: handley@business.rutgers.edu (S.M. Handley),
benton.1@osu.edu (W.C. Benton Jr.).
0272-6963/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.jom.2011.06.001

tinual disregard for the protective obligations of the MSA [Master
Service Agreement], has resulted in harm to State agencies, exposure to unnecessary risks, and failure to achieve the objectives set
and agreed by IBM” (Robinson, 2010). Among the allegations cited
are “IBM failed to provide sufﬁcient and suitably qualiﬁed personnel” and “IBM fails to perform all obligations for technology
refresh. . . has not developed or provided plans to migrate systems
for which IBM has ﬁnancial and operational responsibility to newer
platforms” (Robinson, 2010).
While these alleged IBM actions appear remarkable, opportunistic provider behavior has broadly been noted as a central concern
with outsourcing (Holcomb and Hitt, 2007; McIvor, 2009). The
IBM–Texas DIR example, illustrates the concern that providers may
be inclined to withhold resources or “under-invest” in the relationship if they believe the outsourcing ﬁrm is unable to detect
such action (i.e. shirking). An equally serious risk faced by outsourcing organizations involves intellectual property protection
and conﬁdentiality. A Deloitte Consulting study of business process
outsourcing found that 26% of participants identiﬁed “intellectual property and conﬁdentiality issues as the leading risks of

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S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

Outsourcing Context
* Type of outsourcing (IT,
Logistics, etc.)
* Domestic vs. of f shore
* Contract size
* Longevity of relationship

Exchange Hazards
* Buyer relationship-specif ic investments
* Provider relationship-specif ic investments
* Technological uncertainty

Provider Opportunism
* Shirking
* Poaching
Buyer Power
* Mediated power
* Non-mediated power
Fig. 1. Conceptual model.

outsourcing” and 10% of respondents had explicitly experienced
“conﬁdentiality and intellectual property rights violations” (Landis
et al., 2005, p. 10). For instance, one outsourcing ﬁrm reported
that its vendor was caught selling proprietary software to other
clients. These concerns for conﬁdentiality and intellectual property protection highlight the second form of outsourcing provider
opportunism that will be explicitly evaluated in this study, poaching.
Surprisingly, a review of the extant literature reveals that
empirical research on the drivers of opportunistic behavior is
rather limited. In this study, we test a model (see Fig. 1) which
hypothesizes that the buying ﬁrm’s reliance on different bases of
inter-ﬁrm power will have differing effects on the risk of provider
opportunism. These hypotheses are evaluated while examining
and controlling for the inﬂuence that relevant exchange hazards
have on opportunism. This concurrent consideration of power and
exchange hazards is critical, and has not been studied previously.
Additionally, this study contributes valid and reliable scales for
shirking and poaching; two forms of opportunism. This facilitates
the execution of the current study, and also contributes new scales
for meaningful outsourcing risks. By incorporating shirking and
poaching as two distinct forms of opportunism, more exact insights
and guidance can be developed concerning the effect of exchange
hazards and relationship management practices on the total cost
of outsourcing.
This paper is structured as follows. In Section 2, the literature
related to exchange hazards, inter-ﬁrm power, and opportunism is
discussed. Section 3 provides the theoretical underpinnings for the
research hypotheses. Subsequently, details of the research methods
and analysis are covered in Sections 4 and 5. Section 6 discusses the
research ﬁndings and managerial implications. Concluding comments and future research directions are presented in Section 7.
2. Related literature

ioral assumptions underlying TCT is opportunism (Grover and
Malhotra, 2003; Holcomb and Hitt, 2007). Opportunism is deﬁned
as “self-interest seeking with guile” (Williamson, 1975, p. 6). The
behavioral assumption of opportunism in TCT presupposes that if
given the opportunity, individuals will naturally act in a deceitful, self-serving manner (Ghoshal and Moran, 1996; John, 1984).
Critics of TCT often cite the assumption that opportunism is an
innate human characteristic as a fundamental limitation of the
theory (Wathne and Heide, 2000). Maitland et al. (1985, p. 64)
remark that “opportunism neither is ubiquitous nor is it very
unusual”. If one can accept that opportunism is not an unwavering human characteristic, but rather is substantively dependent
upon the exchange context and management practices employed,
then seeking to understand the factors which exacerbate or attenuate the proclivity for opportunistic behavior is a worthy research
endeavor.
The contributions of the current study lie at the nexus of three
profound bodies of literature: inter-organizational exchange hazards, power, and opportunism. Table 11 provides a representative
sample of the core empirical literature surveyed for this study, and
also conveys the positioning of the current study relative to this
prior research. The primary contribution is that this is the ﬁrst
study to simultaneously consider how exchange hazards and differing power bases inﬂuence the risk of opportunism. This study
is also the ﬁrst to investigate these relationships in the context of
business process outsourcing. Table 1 additionally illustrates that
ours is one of the few studies conducted in a dyadic way. That is,
paired data is collected from both buyer and provider organizations.
Finally, whereas prior studies of opportunistic behavior relied on
broad measures of opportunism, the current study explicitly considers two salient forms of opportunism. The following sections
serve to review the prior contributions and develop the conceptual domains for inter-organizational exchange hazards, power,
and opportunism.

One of the most heavily relied upon perspectives utilized to
explain the demarcation of ﬁrm boundaries is transaction cost theory (TCT) (Macher and Richman, 2008; Rindﬂeisch et al., 2010).
According to TCT, the most efﬁcient form of governance for a
particular business activity is conditional on salient characteristics of the exchange or transaction (Coase, 1937; Holmström
and Roberts, 1998; Williamson, 1975). One of the key behav-

1
The focus here is on empirical TCT studies that explicitly include opportunism
as the dependent variable of interest and this table is not intended to exhaustively
cover all empirical research testing propositions of TCT as this would necessitate an
entire paper unto itself. The interested reader is referred to David and Han (2004)
for an excellent review.

Table 1
Representative empirical literature.

Current study
Anderson (1988)
Benton and Maloni (2005)
Boyle and Dwyer (1995)
Boyle et al. (1992)
Brown et al. (1983)
Brown et al. (1995)
Brown and Dev (2000)
Carter and Stevens (2007)
Dahlstrom and Nygaard (1999)
Etgar (1976)
Etgar (1978)
Frazier and Rody (1991)
Frazier and Summers (1984)
Frazier and Summers (1986)
Hunt and Nevin (1974)
John (1984)
Keith et al. (1990)
Lusch and Brown (1982)
Maloni and Benton (2000)
Morgan et al. (2007)
Parkhe (1993)
Provan and Skinner (1989)
Wilkinson (1979)

Source of data

Outsourcing dyads
Supervisors of salespeople
Automotive suppliers
Industrial distributors
Automotive tire dealers
Retailers – multiple channels
Farm equipment dealers
Hotel managers
Suppliers in E-reverse auctions
Norwegian oil franchisees
Insurance agents
Retailers – multiple channels
Industrial distributors
Automotive dealers
Automotive dealers
Fast food franchisees
Oil dealers
Food broker ﬁrms
Automotive dealers
Automotive suppliers
Supermarket retailers
Sr Executives in alliances
Farm and power equipment deale
Durable product channel dyads

Used as independent variable

Dependent variable

Exchange
hazard

Basis of power

Power source
satisfaction

X
X

X
X
X
X
X
X

Relationship/
quality
commitment

Exchange
performance

Decision
control

Conﬂict/
interﬁrm
agreement

Other

Opportunism

Provider
Salespeople’s
X

X

X
X

X
X

X

X

X

Hotel’s
Buyer’s
Franchisor’s
X
X
X
X
X
X
X
X
X
X

X
X
X
X
X
X

X
Dealer’s
X
X
X

X

X
Supplier’s
Other party’s
Dealer’s

X
X

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

Study

X

57

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S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

2.1. Exchange hazards
According to TCT, inter-organizational transaction difﬁculties hinge on three principal characteristics: transaction-speciﬁc
investments, uncertainty, and frequency (Williamson, 1979). Our
focus is only on “recurring” outsourcing relationships, and thus
the two pertinent exchange attributes are relationship-speciﬁc
investments and uncertainty. These are easily the most commonly
included variables in empirical studies of the core propositions of
TCT (David and Han, 2004). Relationship-speciﬁc investments are
such that their “value is appreciably lower (perhaps zero) in any
other use other than supporting the transaction between the parties” (Holmström and Roberts, 1998, p. 74). Uncertainty reﬂects
the rate and unpredictability with which the inter-organizational
exchange environment is changing. In an extensive review of
empirical research on TCT, David and Han (2004) conclude that
technological uncertainty is the most commonly used measure
of uncertainty. We follow these precedents and utilize measures
of relationship-speciﬁc investments (both buyer and supplier)
and technological uncertainty to represent the extensiveness of
exchange hazards in outsourcing relationships.
2.2. Bases of inter-ﬁrm power
Power “refers to the ability of one individual or group to
control or inﬂuence the behavior of another” (Hunt and Nevin,
1974, p. 186). French and Raven (1959) offer ﬁve key types of
power: reward, coercive, legitimate, referent, and expert. Raven
and Kruglanski (1970) contend that the individual bases of power
are not used separately, but rather jointly. Empirical evidence suggests that reward, coercive (also called penalties) and legitimate
power sources are employed collectively (Busch, 1980; Frazier and
Summers, 1984; Marwell and Schmitt, 1967) as are referent and
expert (Kasulis and Spekman, 1980). Legitimate power has been
segmented into traditional and legal components (Brown et al.,
1983; Kasulis and Spekman, 1980) with legal legitimate power,
predicated on formal contracts, most typically associated with
reward and coercive power. Reward, coercive, and legal legitimate power are classiﬁed as mediated power (Brown et al., 1995;
Maloni and Benton, 2000). The term mediated refers to explicit
attempts to “bring about some direct action” (Benton and Maloni,
2005). Non-mediated power bases are not explicit actions (Frazier
and Summers, 1984; Maloni and Benton, 2000). Rather, power
in these instances is rooted in the target’s perception that the
power source is an expert and the target’s pride in association
with the power source. Expert and referent power bases are classiﬁed as non-mediated. The mediated bases of power share two
characteristics distinguishing them from the non-mediated bases
of power. First, all three mediated power bases rely on extrinsic forms of pressure to gain compliance from the power target,
whereas non-mediated power relies on the internal processes of
internalization and identiﬁcation and the target’s willingness to
comply (Boyle and Dwyer, 1995; Brown et al., 1995; Frazier and
Summers, 1984; Kelman, 1961). Secondly, the use of each mediated power base represents an explicit attempt by the power
source to bring about some direct action (Benton and Maloni,
2005; Brown et al., 1995; Frazier and Summers, 1984; John, 1984).
This is not true of expert and referent power. Further, coercive
and reward power are very difﬁcult to distinguish conceptually.
For instance, if a buying ﬁrm promises additional business opportunities to a provider for going along with its wishes, this may
be considered promising a reward. However, if the buyer threatens to withhold these same opportunities from an incompliant
provider, it may be interpreted as coercion. Grouping reward
and coercive power together have endured signiﬁcant empirical
scrutiny (e.g. Boyle and Dwyer, 1995; Etgar, 1978; Frazier and Rody,

1991; Frazier and Summers, 1984; Keith et al., 1990; Wilkinson,
1979).
2.3. Opportunism
Williamson (1975, p. 9) deﬁned opportunism as “a lack of candor or honesty in transactions, to include self-interest seeking with
guile”. The term “guile” has been explicated to connote “lying, stealing, cheating, and calculated efforts to mislead, distort, disguise,
obfuscate, or otherwise confuse” (Williamson, 1985, p. 47). This
conceptualization has been reiterated by other researchers (e.g. Jap
and Anderson, 2003; Liu et al., 2009; Wathne and Heide, 2000).
According to TCT, inter-organizational exchanges characterized by
a high risk of opportunism necessitate pronounced resource expenditures to control and monitor the other party. Hence, the relative
efﬁciency of a market mode of governance (e.g. outsourcing) would
be eroded (Hill, 1990; Parkhe, 1993). Our focus is on two germane
forms of opportunism with outsourcing: shirking and poaching
(Aron et al., 2005). Shirking represents the extent to which the service provider is inclined to deliberately underperform or withhold
resources should the customer be unable to detect such action.
Shirking, often associated with the problem of moral hazard in the
principal-agent literature (Alchian and Demsetz, 1972; Eisenhardt,
1989; Masten, 1988), is a form of passive, yet intentional opportunism (e.g. Hadﬁeld, 1990; Jap and Anderson, 2003; Wathne and
Heide, 2000). The aforementioned situation playing out between
IBM and the Texas DIR is a clear example of alleged provider shirking. Poaching is the extent to which the service provider is inclined
to utilize information gained through its relationship with the
customer for its own, perhaps unauthorized, beneﬁt should the
customer be unable to detect such action. Walker (1988) refers
to this concern as diffusion risk, while Clemons et al. (1993) use
the phrase “loss of resource control”. Further, a Deloitte Consulting
study (Landis et al., 2005) highlights the extent to which organizations have experienced conﬁdentiality violations and are concerned
about risks to intellectual property when outsourcing. The constructs of shirking and poaching are clearly “self-interest seeking”
behavior, which fall within the accepted domain of opportunism
(Williamson, 1975, 1985). They have also been explicitly identiﬁed
as salient forms of opportunism with outsourcing (e.g. Aron et al.,
2005; Barney, 1990; Clemons and Row, 1992; Donaldson, 1990; Jap
and Anderson, 2003; Landis et al., 2005; Wathne and Heide, 2000).
Remarkably, empirical research explicitly aiming to explain the
managerial actions and relationship attributes which drive interorganizational opportunism is relatively modest. Prior studies have
explored how asset speciﬁcity, uncertainty, and information asymmetry are related to opportunism (Anderson, 1988; Brown and
Dev, 2000). Provan and Skinner (1989) consider how farm equipment dealer’s dependence on the supplier and supplier control
over decisions impacts dealer opportunism. Parkhe (1993) reports
evidence that a history of cooperation is negatively related to perceptions of opportunism. Exchange formalization and cooperation
are considered as antecedents to opportunism by Dahlstrom and
Nygaard (1999). Carter and Stevens (2007) explore how e-reverse
auction conﬁgurations impact the supplier’s perception of buyer
opportunism. Morgan et al. (2007) consider the impact of supplier
inﬂuence, retailer dependency, supplier dependency, retailer’s ability to monitor supplier, and retailer’s punitive capacity on supplier
opportunism in the grocery industry. John (1984) represents the
only prior study which evaluates how different sources of interﬁrm power inﬂuence the threat of opportunism. Our study can be
distinguished from John (1984) in that we control for the inﬂuence of exchange hazards, explicitly explore the effects of power
on shirking and poaching, study these relationships in a variety
of business functions and industries, and collect data from both
customers and their service providers.

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

3. Research hypotheses
3.1. Relationship-speciﬁc investments and provider opportunism
Unilateral investments in specialized physical and human
assets are generally thought to exacerbate opportunistic behavior. As discussed by Williamson (1979), upon one ﬁrm to an
exchange making relationship-speciﬁc investments, the relationship is “transformed” to have monopoly-like properties. Klein et al.
(1978, pp. 298–299) assert “even if there were free and open competition for entry to the market, the specialization of the installed
asset to a particular user (or more accurately the high costs of
making it available to others) creates a quasi rent”. If the party
making the relationship-speciﬁc investment is the buying ﬁrm,
a small numbers bargaining situation is created. Although the
supply market for a particular service may be adequately competitive in general, the installment of unique assets makes switching
providers more costly. This creates a “lock-in” in situation whereby
the outsourcing ﬁrm becomes increasingly hostage to the provider.
Accordingly, TCT would suggest that the outsourcing provider is
more rationally motivated to exploit the situation to their advantage (Das and Teng, 1996). Supporting these arguments, Morgan
et al. (2007) offer empirical evidence from the grocery industry
regarding the positive relationship between inter-organizational
dependence and opportunism by the other party. Service providers
may seek to appropriate a greater portion of the economic rent
generated from the exchange by overt actions such as opportunistic contract renegotiations (Wathne and Heide, 2000). However,
the provider may also take covert actions which are opportunistic
in nature. Shirking is a prime example of the hidden opportunistic actions available to providers. By not deploying the necessary
resources or making agreed to investments, providers can potentially achieve greater value from the relationship, at least in the
short-term (Wathne and Heide, 2000). Speciﬁcally, it is posited:
More extensive buyer relationship-speciﬁc
Hypothesis 1a.
investments are associated with an increased risk of shirking by
the service provider.
Another form of concealed opportunism potentially enacted
by the provider is poaching. The lock-in situation created by the
buying ﬁrm making relationship-speciﬁc investments presents an
opportunity for potential provider opportunism and potentially
poaching. Effective management of outsourcing arrangements
often necessitates the codiﬁcation and transfer of knowledge
(Kogut and Zander, 1992), along with specialized investments
which facilitate inter-organizational coordination (Clemons et al.,
1993). Combined, the transfer of knowledge and investment in
specialized resources enhance the opportunity for the provider to
utilize the transferred knowledge in an unauthorized, self-serving
manner. Clemons et al. (1993, p. 18) concisely articulate this risk
“Increased explicit coordination historically required investment in
transaction-speciﬁc capital. Physical and human resources had to
be customized to the relationship. Moreover, increased integration
of operations made it more difﬁcult to control access to proprietary
information and expertise, increasing the risk that these resources
would be misappropriated”. Consequently, it is hypothesized:
More extensive buyer relationship-speciﬁc
Hypothesis 1b.
investments are associated with an increased risk of poaching by
the service provider.
The previous theoretical arguments related to the outsourcing
ﬁrm’s relationship-speciﬁc investments. It is also plausible that the
service provider is required to make investments that are unique
to the outsourcing relationship. It is argued that provider asset
speciﬁcity is negatively related to their opportunistic behavior. As
opportunism will generally be viewed as counter to acceptable

59

relational norms by the outsourcing ﬁrm, it can be expected that
discovery of opportunistic behavior will be accompanied by some
form of buyer retaliation (Provan and Skinner, 1989). This retribution may be in the form of legal actions or even relationship
termination (Brown and Dev, 2000). If the outsourcing provider has
made signiﬁcant investments in idiosyncratic, non-transferrable
resources, they stand to incur much greater ﬁnancial harm should
the relationship end. This enhanced risk should naturally deter the
provider from acting opportunistically (Rokkan et al., 2003). It is
expected that these arguments will hold for both provider shirking
and poaching. Formally:
Hypothesis 2a. More extensive provider relationship-speciﬁc
investments are associated with a decreased risk of shirking by the
service provider.
Hypothesis 2b. More extensive provider relationship-speciﬁc
investments are associated with a decreased risk of poaching by
the service provider.
3.2. Technological uncertainty and provider opportunism
As the predominant technology underlying a transaction
evolves more rapidly, it becomes increasingly difﬁcult to craft a
complete and precise outsourcing contract (Crocker and Reynolds,
1993; Williamson, 1979). This difﬁculty is problematic for
outsourcing organizations as the development of contractual safeguards is “the primary alternative to vertical integration as a
solution to the general problem of opportunistic behavior” (Klein
et al., 1978, p. 302). Firms which eschew vertical integration in favor
of outsourcing, typically rely on formal contractual provisions as
a key mechanism for controlling provider behavior (Handley and
Benton, 2009; Poppo and Zenger, 2002). However, high levels of
technological uncertainty make developing enforceable contractual provisions more challenging, thus mitigating the effectiveness
of this prominent managerial control lever. Not only does technological uncertainty attenuate the effectiveness of formal contracts,
but it also instigates the small numbers bargaining concerns
described in the prior section. With the outsourcing of business
processes traditionally performed within the ﬁrm, the internal
operating unit which had historically performed the activity represents a viable source of supply. Indeed, a Deloitte Consulting
investigation found that 64% of study participants had brought at
least one outsourced business process back in-house (Landis et al.,
2005). Due to capability erosion, a high rate of technological volatility renders this option less practical over time. Anderson and Parker
(2002) assert that an increased rate of technological change expedites the process of “knowledge obsolescence”. A reduction in the
available sources limits the competitiveness of the supply market,
which in turn increases the risk of a buyer lock-in situation. For
these reasons, technological uncertainty is expected to intensify
concerns of provider shirking and poaching.
Hypothesis 3a. Higher technological uncertainty is associated
with an increased risk of shirking by the service provider.
Hypothesis 3b. Higher technological uncertainty is associated
with an increased risk of poaching by the service provider.
3.3. Buyer power and provider opportunism
Several empirical studies have explored how the degree of
dependence on the relationship and/or the reliance on different
bases of power effect the level of control over key marketing or
operational decisions (Brown et al., 1983; El-Ansary, 1975; ElAnsary and Stern, 1972; Etgar, 1976, 1978; Lusch and Brown, 1982).
It has also become recognized that the use of different power bases
has differing effects on inter-organizational relationships. Benton

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S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

and Maloni (2005) have suggested that mediated power is associated with both negative and competitive uses of power while
non-mediated power is more positive, relational in nature. Firms
that use mediated approaches to force compliance from providers
are attempting to extrinsically motivate providers to go along with
their wishes (Brown et al., 1995). This focus on extrinsic motivations has been associated with a decrease in intrinsic motivation
and less favorable view of the relationship (John, 1984). Boyle et al.
(1992) also found that threats and legalistic pleas have a strong
negative effect on relationalism. Frazier and Summers (1984) ﬁnd
that threats, promises, and legalistic pleas are negatively related
to inter-ﬁrm agreement in automotive OEM–dealer relationships.
Similarly, Brown et al. (1995) determine that the use of mediated
power has a positive effect on the power target’s instrumental
commitment (i.e. compliance), but a negative effect on normative
commitment (i.e. internalization and identiﬁcation). These ﬁndings suggest that mediated power can gain short-term compliance
at the expense of damaging longer-term intrinsic commitment
to the relationship. Finally, Maloni and Benton (2000) offer evidence that mediated power has a generally negative impact on
buyer–supplier relationships. These consistent empirical ﬁndings
support the notion that extensive use of mediated power damages
the social fabric of the relationship and leads to a deterioration
of the power target’s commitment and cooperation (Frazier and
Summers, 1986; Skinner et al., 1992). Moreover, it is suggested that
a power source’s use of coercion and punitive action will likely be
reciprocated in some form (Kumar et al., 1998). Building on these
arguments, we posit that more extensive use of mediated power by
the outsourcing ﬁrm undermines the provider’s commitment and
willingness to cooperate. Opportunism has been described as the
opposite of cooperation (Das and Teng, 1998). Therefore, it can be
expected that the use of mediated power will enhance the risk of
shirking and poaching by the outsourcing provider.

that non-coercive practices were associated with greater franchisee satisfaction in the fast food industry. Similarly, Frazier and
Rody (1991) found that non-coercive inﬂuence strategies were
associated with more productive conﬂict resolution. Evidence has
also been produced regarding the positive affect of non-mediated
power on normative relationship commitment (Brown et al., 1995;
Maloni and Benton, 2000). Finally, Benton and Maloni (2005) offer
support for a positive relationship between non-mediated power
bases and buyer–supplier relationships in the automotive industry. This collection of empirical results provides robust support for
the positive association between the use of non-mediated power
and the development of committed and cooperative inter-ﬁrm
relationships. Moreover, several scholars have noted the role of
committed, collaborative relationships in diminishing concerns of
opportunism (e.g. Helper et al., 2000; Nahapiet and Ghoshal, 1998).
Some have gone so far as to describe opportunism as the antithesis
of trust and cooperation (Bradach and Eccles, 1989; Das and Teng,
1998; Gulati, 1995; Gulati and Nickerson, 2008). Therefore, if the
use of non-mediated power by outsourcing organizations bolsters
cooperative, trusting relationships, it should mitigate concerns of
provider opportunistic behavior including shirking and poaching.

Hypothesis 4a. More extensive buyer reliance on mediated power
sources is associated with an increased risk of shirking by the service
provider.

The target buyer population for this research was large USbased companies with domestic and/or offshore business process
(e.g. information technology, logistics, accounting, and human
resources) outsourcing engagements. We started with ﬁrms listed
in the Russell 3000 equity index,2 and utilized multiple feebased online databases to identify the names, titles, and e-mail
addresses of senior professionals at these organizations (Lead411;
Spoke.com; ZoomInfo.com). Qualiﬁed informants were identiﬁed
by reviewing job titles, and job descriptions were available, to
ascertain the likelihood of involvement with outsourcing. Contact
information was obtained for members of management at 2356
of the targeted 3000 ﬁrms. Contacted outsourcing ﬁrm representatives were asked to complete an online survey, and also provide the
research team with an introduction to the key contact at their service provider. The participants were asked to include assessments
of each relationship in the study. Speciﬁcally, potential participants
were asked not to “cherry pick” certain relationships, but rather
to include all relevant relationships in the category for which they
were responsible. These conditions substantially increased the time
commitment being asked of the participants. Communications had
with the contacts at 176 organizations that elected not to participate did not indicate an observable bias. Approximately 75% cited
a policy against sharing such sensitive information or a lack of
familiarity with the ﬁrm’s outsourcing efforts (i.e. due to retirement, changing positions, too new in the position, or simply not
involved with outsourcing). It is anticipated that non-respondents

Hypothesis 4b. More extensive buyer reliance on mediated power
sources is associated with an increased risk of poaching by the service provider.
With mediated power inﬂuence, compliance occurs without an
attendant change in the power target’s opinions or beliefs regarding
the requested action (Kelman, 1958; Lusch and Brown, 1982). However, with non-mediated power compliance is achieved by means
of a more “durable” intrinsic effect on the power target’s beliefs
and opinions (Frazier and Summers, 1984; Kelman, 1961). Important contributions in the marketing channel literature (Brown et al.,
1995) and social psychology literature (Kelman, 1958, 1961) suggest that non-mediated power inﬂuence is achieved via two social
processes: identiﬁcation and internalization. Identiﬁcation refers
to the power target’s desire to maintain a satisfying and selfdeﬁning relationship with the other party (Brown et al., 1995).
In other words, they genuinely wish to maintain the relationship
with which they are proud to be engaged in. With internalization, the inﬂuence is accepted because it is “congruent” with the
power target’s own “value system” (Kelman, 1961). This is consistent with theoretical assertions and empirical evidence supporting
the positive relationship between shared beliefs or norms and
inter-organizational trust and commitment (Dwyer et al., 1987;
Heide and John, 1992; Morgan and Hunt, 1994). Inﬂuence derived
through the identiﬁcation and internalization processes associated
with non-mediated sources of inter-ﬁrm power is said to “enhance
the saliency of certain social norms (e.g. equity and reciprocity) in
the relationship” (John, 1984, p. 281). This relationship has received
substantive empirical support. Hunt and Nevin (1974) determined

Hypothesis 5a. More extensive buyer reliance on non-mediated
power sources is associated with a decreased risk of shirking by the
service provider.
Hypothesis 5b. More extensive buyer reliance on non-mediated
power sources is associated with a decreased risk of poaching by
the service provider.
4. Research methods
4.1. Sample

2
This index reﬂects the largest 3000 publicly held US companies (as of
October 2009) and represents approximately 98% of the US equity market (Russell
Investments, 2010). It is noted that this group does not include large private ﬁrms.
As such, the target sampling frame is representative of large, public US-based companies.

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

61

Table 2
Sample descriptive data.
Sector
Financial services
Consumer discretionary
Technology
Health care
Producer durables
Materials and processing
Energy
Utilities
Consumer staples
Firm size (2009 annual revenues)
Less than $500.00M
$500.00M–1.00B
$1.01B–5.00B
$5.01B–10.00B
Over $10.00B

5%
22%
22%
15%
17%
5%
7%
2%
7%
23%
18%
22%
8%
28%

Title of respondent (buyer)
CXO
Sr. or Executive Vice President
Vice President
Sr. Director
Director
Sr. Manager
Manager
Title of respondent (provider)
CXO
Sr. or Executive Vice President
Vice President
Sr. Director
Director
Sr. Manager
Manager
Other

who we did not communicate with share a proﬁle with those who
did provide justiﬁcation for why they were not valid participants.
Ultimately, an agreement to participate was obtained from 78 of the
contacted ﬁrms. Buyer-side data was collected on 134 outsourcing
relationships. Subsequently, the service providers were contacted
to complete a different online survey to reﬂect their view of the relationship. Service providers were explicitly told that their responses
would never be shared with their customers. In total, 105 completed questionnaires were returned from the service providers,
resulting in the research team obtaining a rich set of both buyer and
service provider (i.e. dyadic) data on 102 outsourcing relationships.
Potential non-response bias was assessed by comparing early and
late respondents (Armstrong and Overton, 1977) on various demographic variables. Moreover, a buying ﬁrm comparison was made
between respondents and all ﬁrms in the Russell 3000 in terms of
sector representation. These analyses did not raise signiﬁcant concerns about a response bias. It is thus concluded that the obtained
sample (see Table 2) adequately represents the target population.
4.2. Measurement instrument
In this study, the exchange hazards present in outsourcing
relationships are reﬂected by three constructs. Buyer relationshipspeciﬁc investment is measured as a composite of two items, one
representing human asset speciﬁcity and the other representing
physical asset speciﬁcity. These items were adapted from two items
used in a prior scale of idiosyncratic investments (Anderson and
Weitz, 1992), and are measured from the standpoint of the buying ﬁrm. Provider relationship-speciﬁc investment is also measured
as a composite of two items for human and physical asset speciﬁcity; except this time measured from the provider’s perspective.
A multi-item scale reﬂecting technological uncertainty is included to
represent the rate and predictability with which the primary process technologies are changing (Bourgeois and Eisenhardt, 1988;
Miller and Friesen, 1983). The three items used in this scale reﬂect
this rate and predictability of technological change. This scale is
adapted from the prior work of Dröge et al. (2003) and is measured
from the service provider’s vantage point.
The power bases included in this study are: reward, coercive,
legal legitimate, referent, and expert (French and Raven, 1959). In
keeping with established empirical precedent, the bases of reward,
coercive, and legal legitimate are classiﬁed as mediated power. Likewise, non-mediated power comprises the referent and expert power
bases. The composite measures of mediated and non-mediated
power, rather than the individual components for each of these
power bases, are utilized to test the research hypotheses. In the
case of each power base, adapted versions of the multi-item scales

5%
5%
9%
11%
37%
9%
24%
5%
11%
22%
8%
24%
8%
15%
8%

Function of outsourced activity
Information technology
Logistics/supply chain
Other business process
Domestic vs. Offshore Outsourcing
Domestic only
Offshore
Annual contract size
Less than $1 million
Between $1.0 and $24.9 million
Between $25.0 and $49.9 million
Between $50.0 and $99.9 million
$100.0 million or more

31%
49%
20%
49%
51%
20%
59%
14%
4%
3%

developed by Maloni and Benton (2000) are used for this study.
Each power base is measured from the service provider survey, and
thus reﬂects the provider’s perspective of the means by which their
customer gains their compliance.
To the best of our knowledge, no scales currently exist for
our two opportunism constructs of shirking and poaching. Therefore, new multi-item scales were developed to measure each from
the perspective of the potentially opportunistic party; the service
provider. A three-round q-sorting exercise was used to reﬁne the
items prior to the main data collection. q-Sorting involves asking participants to match randomly ordered survey items with
the most appropriate construct deﬁnition provided. Shirking and
poaching were included among a set of seven constructs and
twenty-six items.3 The ﬁrst two rounds were conducted with academic colleagues, and the ﬁnal round engaged six practitioners
each with vast experience in managing outsourcing relationships.
The results after each round were used to identify poorly worded
items or construct deﬁnitions, and guide needed adjustments. By
the ﬁnal round, the individual items were correctly matched with
their intended constructs at an acceptable rate (overall placement rate exceeded 90%) (Moore and Benbasat, 1991; Perreault
and Leigh, 1989), offering evidence of content validity. Moreover,
the psychometric results (mean proportion of substantive validity = 0.90; mean coefﬁcient of substantive validity = 0.81) reﬂected
sufﬁcient levels of face or substantive validity (Menor and Roth,
2007).
Contextual control variables were captured via the buying
ﬁrm’s survey. Two dummy variables distinguish between three
functional types of outsourcing: information technology, logistics, and general business process (e.g. ﬁnance and accounting and
human resources) outsourcing. Another dummy variable reﬂects
outsourcing engagements that are purely domestic (domestic = 1)
as opposed to those serviced by offshore providers. A measure of
contract size in terms of annual contract value (1 = <$1 million;
2 = $1.0–24.9 million; 3 = $25.0–49.9 million; 4 = $50.0–99.9 million; 5 = >$100.0 million) is also included. Finally, we include the
existing longevity of the outsourcing relationship (in years). This
control variable has been incorporated in prior studies of inter-ﬁrm
power (Brown et al., 1995; Frazier and Rody, 1991).
4.3. Measurement validity and reliability
The measurement items and their respective scales are included
in Appendix A (excluding control variables). The multi-item scales

3

The other ﬁve constructs are not included in the current study.

62

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

Table 3
Item descriptive statistics.
Constructs and items

Mean

Std. dev.

Buyer relationship-speciﬁc investments (˛ = 0.75)
BRSI1
4.32
BRSI2
3.64
Provider relationship-speciﬁc investments (˛ = 0.63)
PRSI1
5.98
PRSI2
4.88
Technological uncertainty (˛ = 0.65)
3.70
TU1
TU2
2.77
TU3
2.79
Mediated power
Reward power (˛ = 0.73)
2.57
RP1
3.70
RP2
RP3
2.56
Coercive power (˛ = 0.71)
3.36
CP1
CP2
3.73
CP3
3.65
Legal legitimate power (˛ = 0.85)
3.01
LLP1
2.73
LLP2
3.14
LLP3

Constructs and items
Non-mediated power
Referent power (˛ = 0.74)
RFP1
RFP3
Expert power (˛ = 0.63)
EP1
EP2
EP3
Provider shirking (˛ = 0.80)
PS1
PS2
PS3
PS4
PS5
PS6
Provider poaching (˛ = 0.85)
PP1
PP2
PP3

1.65
1.83
1.05
1.76
1.32
1.18
1.33

1.28
1.55
1.48
1.61
1.58
1.79

Mean

Std. dev.

5.90
6.08

1.05
1.05

6.24
6.05
5.43

0.78
0.77
1.05

1.43
1.40
1.33
1.75
1.51
1.50

0.68
0.85
0.60
1.12
0.89
0.73

1.29
1.26
1.43

0.75
0.72
0.92

1.67
1.65
1.61

Table 4
Bi-variate correlations.
Variable

1

2

3

4

5

1: Buyer relationship-speciﬁc investments
2: Provider relationship-speciﬁc investments
3: Technological uncertainty
4: Mediated power
5: Non-mediated power
6: Provider shirking
7: Provider poaching
8: IT outsourcing
9: Logistics outsourcing
10: Domestic outsourcing
11: Contract size
12: Longevity of relationship
13: Shirking desirability
14: Poaching desirability

1.00
0.22**
0.00
0.06
0.02
0.11
−0.04
0.17*
0.02
−0.11
0.22**
−0.03
−0.15
−0.19*

1.00
0.18*
0.32***
0.10
−0.21**
−0.12
0.12
0.13
−0.17*
0.37***
0.13
0.03
−0.05

1.00
0.08
0.09
0.04
−0.02
0.02
0.01
−0.04
0.11
0.06
−0.12
−0.08

1.00
−0.37***
0.27***
0.16
0.14
0.179*
−0.15
0.38***
0.14
0.13
0.09

1.00
−0.35*** 1.00
−0.19**
0.69*** 1.00
−0.06 −0.11 −0.23**
−0.01
0.21**
0.27***
−0.07
0.06
0.12
−0.03
0.05
0.05
−0.09
0.11
0.13
−0.15 −0.02 −0.17*
−0.14 −0.02 −0.10

*
**
***

6

7

8

9

10

1.00
−0.66*** 1.00
−0.33*** 0.26*** 1.00
−0.06
0.38*** −0.17*
−0.19*
0.30*** 0.08
−0.01
0.04 −0.04
0.01 −0.03 −0.06

11

1.00
0.23**
0.21**
0.17*

12

1.00
0.09
0.05

13

14

1.00
0.92*** 1.00

p < 0.10.
p < 0.05.
p < 0.01.

were subjected to a conﬁrmatory factor analysis (CFA) using LISREL
8.80.4 Preliminary analysis indicated that one of the items in the referent power scale had an unacceptably low correlation with other
items in the scale and was therefore removed from the analysis.
Missing data was accommodated using full-information likelihood
(FIML). The results from the CFA demonstrate acceptable measurement model validity. Acceptable overall model ﬁt is reﬂected by
the RMSEA = 0.07 and 2 /df = 1.51. The loading of each item on its
intended construct is highly signiﬁcant (p < 0.01), exhibiting strong
convergent validity. To evaluate discriminant validity, a series of
matched constrained and unconstrained models were tested for
a signiﬁcant difference using a 2 test (Bagozzi et al., 1991). In
each constrained model, a pair of latent constructs is speciﬁed to
have a correlation of 1.0 while the unconstrained models allow
all inter-factor correlations to be free parameters. Each 2 test for
differences in the models were signiﬁcant at p < 0.01 offering evi-

dence of discriminant validity (O’Leary-Kelly and Vokurka, 1998).
To validate grouping reward, coercive, and legal legitimate power
bases as a composite measure of mediated power, and expert and
referent bases as a composite measure of non-mediated power;
an exploratory factor analysis (EFA) was performed using the ﬁve
individual power bases as the indicators. This EFA resulted in two
distinct factors clearly reﬂecting the intended mediated and nonmediated bases of power. The Cronbach’s ˛ values for each factor
show sufﬁcient construct reliabilities (Carmines and Zeller, 1979;
Nunnally, 1978). Table 3 provides the descriptive statistics for
each item (excluding the control variables previously explained)
and the Cronbach’s ˛ ﬁgures for each multi-item scale. Correlations among the constructs used in the analyses are presented in
Table 4.

4.4. Common methods and social desirability
4

As the sole multi-item scale measured from the outsourcing organizations, the
buyer relationship-speciﬁc investment construct was not included in this CFA. It
should be noted however that this construct individually demonstrated sufﬁcient
validity and reliability.

Hypotheses 1a and 1b are not exposed to concerns of a common methods bias as the dependent and independent variables
are collected from different sources. This is not the case for the
remaining hypotheses. Common methods bias is partially con-

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

trolled for by the design of the survey instrument (e.g. reverse coded
questions, spatial separation of the dependent and independent
variables, and question-order randomization). As recommended
by Podsakoff et al. (2003, p. 898), we also examined the data for
empirical evidence of common methods bias by conducting a CFA
which included a construct representing an unmeasured methods
factor. Each manifest variable was speciﬁed to load onto this factor in addition to its theoretical construct. This assessment did not
offer evidence that a severe common methods bias exists.5
One may posit that asking providers about their ﬁrm’s tendency
to shirk or poach presents the possibility of a social desirability
bias (SDB). As such, great efforts were made in all communications
to emphasize the conﬁdential and voluntary nature of the research.
Moreover, for all items related to shirking and poaching, an indirect
questioning technique was used.6 Respondents were instructed to
answer with regard to how “members of your organization” would
behave rather than how they personally would behave. In studying
how both personality characteristics and measurement item traits
are related to socially desirable responses, Randall and Fernandes
(1991, p. 814) conclude “an assessment of perceived item desirability of the dependent variable appears to be preferable in future
research”.7 Hence, we measured the extent to which the respondents perceive that each of the shirking and poaching items “would
make most people feel uneasy”. These variables of shirking and
poaching desirability are included in the regression analysis to
partial-out the effect of the potential SDB that exists.
5. Analysis
Our primary analytic objective is to isolate the unique explanatory power of exchange hazards and the differing bases of power
on provider opportunism, while controlling for potentially confounding characteristics of the outsourcing context. This requires
a method which can cleanly partition the variance in a controlled
manner. Additional consideration needs to be given to attributes of
our data. The data representing the independent variables contains
a mixture of single- and multiple-item Likert scale variables, along
with nominal variables. Thus, the chosen statistical technique must
be able to efﬁciently accommodate multiple scales of measurement. Also, our dyadic data has multiple observations from some
outsourcing organizations. This relationship needs to be accounted
for in the analysis. Hierarchical linear regression (HLR) is an appropriate variance partitioning technique (Cohen et al., 2003), which
allows the researcher to enter blocks of variables in a deliberate
sequence. This leads to an unambiguous picture of how a set of
variables uniquely contributes to the explanation of the variance
in the dependent variable by isolating the explanatory power contributed by other variables in the model. HLR is also able to handle
a mixture of different scales of measurement (Cohen et al., 2003, p.
8). Finally, performing the regression analysis in Stata 11.0, one can
specify that robust standard errors be used to account of the intragroup correlation among the multiple observations from the same
customer (Stata, 2009). Therefore, HLR is an appropriate statistical
method for this study.
Two separate HLR models will be analyzed, with shirking and
poaching as the dependent variables, respectively. In both models,
blocks of related independent variables will be entered sequentially

5
The analysis did not result in substantive changes to the factor loadings. Each
item continued to load signiﬁcantly on its intended theoretical construct. In every
case, the item loadings were substantially higher on their intended construct than
on the unmeasured methods factor.
6
In the consumer behavior literature, Fisher (1993) presents strong evidence that
indirect questioning signiﬁcantly mitigates SDB.
7
This conclusion is similar to the recommendations offered by Bradburn et al.
(1978).

63

in order to clearly identify the incremental predictive power of each
group of variables. First, the set of contextual control variables will
be entered into the HLR model. After controlling for these effects,
the factors related to exchange hazards will be entered, followed
by the variables for mediated and non-mediated power. Finally,
the fourth stage of each HLR model will add the item desirability
variables for the respective opportunism constructs. The statistical
results of these analyses are displayed in Table 5. Multi-collinearity
was assessed by examining the variance inﬂation factors (VIFs) of
each independent variable. No VIF exceeded 2.39 in either model;
easily within the desired range (Cohen et al., 2003; Marquardt,
1970).
6. Results and discussion
Table 6 summarizes the results of the statistical examination
of our research hypotheses. Each of the following sub-sections
presents the statistical results for each hypothesis, discusses the
results, and concludes with a consideration of the managerial implications of the ﬁndings.
6.1. Inﬂuence of exchange hazards on provider opportunism
As hypothesized, the regression coefﬁcient associated with
the effect of buyer relationship-speciﬁc investments on provider
shirking is positive and signiﬁcant in the fourth stage of the shirking model (ˇ = 0.171; p < 0.05) (see Table 5). However, the fourth
stage results of the poaching regression model illustrate that the
regression coefﬁcient of buyer relationship-speciﬁc investments
on provider poaching is insigniﬁcant and negative (ˇ = −0.014;
p > 0.10). Thus, H1a is supported while H1b is not. The results of the
full shirking model in Table 5 offer strong support for the hypothesized negative effect of provider relationship-speciﬁc investments
on provider shirking (ˇ = −0.333; p < 0.01). Meanwhile, the hypothesized negative effect of provider relationship-speciﬁc investments
on provider poaching is not supported at conventional levels of
signiﬁcance (ˇ = −0.170). These results hold up H2a but not H2b.
Finally, technological uncertainty did not demonstrate a signiﬁcant
positive effect on the risk of provider shirking (ˇ = 0.103; p > 0.10)
or on the risk of provider poaching (ˇ = 0.002; p > 0.10) Therefore,
neither Hypothesis 3a nor Hypothesis 3b is supported.
The exchange hazards studied appear to have a signiﬁcant effect
on the threat of provider shirking, but not poaching. None of
the hypothesized effects of relationship-speciﬁc investments and
technological uncertainty on provider poaching were found to be
signiﬁcant. While exchange hazards, such as relationship speciﬁc
investments, may foster an environment conducive to classic concerns of opportunism, the speciﬁcity may be reﬂective of a unique
outsourcing service. This uniqueness may diminish the external
value of the portion of the buyer’s intellectual property to which the
provider is exposed. Thus, the risk-reward tradeoff of poaching is
unattractive to the provider. Of these exchange hazards, provider
relationship-speciﬁc investments had the most signiﬁcant effect
on provider shirking. Buyer relationship-speciﬁc investments had
a more modest signiﬁcant effect on shirking. The more modest
ﬁndings regarding the positive effect of buyer relationship-speciﬁc
investments on the risk of provider opportunism (relative to the
extremely strong effects of provider relationship-speciﬁc investments) may at ﬁrst seem surprising given the heralded role of asset
speciﬁcity in TCT. Yet, it is important to note that the data for this
study includes only business activities that have been outsourced.
TCT would suggest that if buyer relationship-speciﬁc investments
are too high, then outsourcing should not occur. Thus, one would
expect somewhat of a ceiling on the level of buyer asset speciﬁcity
observed in our data. This may explain the slightly dampened pos-

64

Table 5
Shirking and poaching regression results.
Variable

Provider shirking (P)

Constant
IT outsourcing (B)
Logistics outsourcing (B)
Domestic outsourcing (B)
Contract size (B)
Longevity of relationship (B)
Buyer relationship-speciﬁc
investments (B)
Provider
relationship-speciﬁc
investments (P)
Technological uncertainty
(P)
Mediated power (P)
Non-mediated power (P)
Shirking desirability (P)
Poaching desirability (P)
F
Prob > F
R2

3

2

Coeff

Std error

−0.320
0.160
0.509**
0.008
−0.039
0.017

0.336
0.212
0.248
0.218
0.123
0.030

1.46
0.217
0.052

Std error

Coeff

4
Std error

Coeff

1

2

3
Coeff

4

Coeff

Std error

Coeff

Std error

Coeff

Std error

Std error

−0.216
−0.166
0.386
0.078
−0.028
0.017

0.362
0.214
0.267
0.221
0.123
0.029

−0.379
−0.083
0.451
0.048
0.022
0.020
0.000

0.356
0.207
0.279
0.220
0.121
0.030
0.087

−0.120
−0.252
0.329
0.046
−0.033
0.015
0.019

0.313
0.258
0.289
0.204
0.102
0.027
0.103

−0.197
−0.256
0.300
0.035
0.012
0.015
−0.014

Coeff

0.332
0.257
0.292
0.210
0.118
0.027
0.101

Std error

−0.552*
0.252
0.605**
−0.047
0.027
0.025
0.149**

0.323
0.237
0.273
0.227
0.127
0.030
0.088

−0.123
−0.027
0.409
−0.059
−0.062
0.016
0.179**

0.254
0.222
0.268
0.188
0.117
0.028
0.093

−0.146
−0.027
0.405
0.061
−0.049
0.016
0.171**

0.261
0.221
0.267
0.189
0.121
0.028
0.090

−0.333***

0.121

−0.340***

0.130

−0.339***

0.130

−0.153

0.131

−0.162

0.138

−0.170

0.137

0.091

0.102

0.109

0.107

0.103

0.105

0.005

0.103

0.014

0.107

0.002

0.111

0.247***
−0.236***

0.095
0.081

0.247***
−0.241***
−0.042

0.094
0.083
0.084

0.160**
−0.120*

0.092
0.088

0.159**
−0.137*

0.087
0.088

−0.143**

0.071

1.67
0.126
0.152

3.40
0.0015
0.291

1. Standard errors are calculated using robust clustering on the customer variable.
2. One-tail t-test used for the effects associated with the hypotheses, two-tail tests used otherwise.
3. (B) indicates that the variable was measured from the buying ﬁrm’s perspective (i.e. the buyer’s survey).
4. (P) indicates that the variable was measured from the provider’s perspective (i.e. the provider survey).
The numbers in bold indicate statistically signiﬁcant regression coefﬁcients.
*
p < 0.10.
**
p < 0.05.
***
p < 0.01.

3.19
0.0019
0.292

3.65
0.0062
0.080

2.88
0.0091
0.099

2.71
0.009
0.146

2.66
0.008
0.164

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

1

Provider poaching (P)

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68
Table 6
Hypothesis testing summary.
Hypothesis

Result

H1a: Buyer RSI → (+) provider shirking
H1b: Buyer RSI → (+) provider poaching
H2a: Provider RSI → (−) provider shirking
H2b: Provider RSI → (−) provider poaching
H3a: Technological uncertainty → (+) provider shirking
H3b: Technological uncertainty → (+) provider poaching
H4a: Buyer mediated power → (+) provider shirking
H4b: Buyer mediated power → (+) provider poaching
H5a: Buyer non-mediated power → (−) provider shirking
H5b: Buyer → (−) provider poaching

Supported
Not supported
Supported
Not supported
Not supported
Not supported
Supported
Supported
Supported
Supported

RSI, relationship-speciﬁc investment.

itive effect of buyer relationship-speciﬁc investments on shirking.
Provider relationship-speciﬁc investments in essence increase the
cost associated with the provider getting caught acting opportunistically. Since these assets are more difﬁcult to redeploy or have
diminished secondary value, termination of the relationship would
be very unattractive. Thus, they are less motivated to act opportunistically. Contrary to our hypotheses, technological uncertainty
was not found to signiﬁcantly inﬂuence the risk of opportunism.
The impact of technological uncertainty has received mixed support in prior ﬁrm boundary studies as well (McIvor, 2009). These
results again highlight the primacy of asset speciﬁcity in driving
some of the key risks of outsourcing.
The managerial implications of these ﬁndings are signiﬁcant for
organizations considering outsourcing, as well as for teams tasked
with managing existing outsourcing relationships. For outsourcing
engagements which necessitate the buyer investing in specialized
assets, the risk of provider shirking is more pronounced. One practical takeaway is that ﬁrms should be cautious about outsourcing
processes that require specialized investments on their part. However, in many cases the decision to outsource has already been
made. In such situations, outsourcing organizations could consider
a few different courses of action in an attempt to reduce the risk of
opportunism. First, if practical the outsourcing ﬁrm should strive
to incorporate incentive mechanisms in their formal contract with
the provider. This is targeted toward aligning the interests of the
two parties, which attenuates the motivation of the provider to act
in a manner that is contrary to the interests of the outsourcing ﬁrm.
Secondly, the outsourcing organization should evaluate options to
increase their ability to monitor the provider’s actions. This focus
on improved monitoring directly aims to reduce information asymmetry and thereby diminish the opportunity for shirking. Indeed,
incentive contracting and implementation of monitoring mechanisms are often cited as the primary means of dealing with agency
concerns such as shirking (Beatty and Zajac, 1994; Eisenhardt,
1989; Tosi et al., 1997). While potentially effective at reducing
shirking risk, these safeguards are typically costly to implement.
This study’s ﬁnding regarding the negative relationship between
provider relationship-speciﬁc investments and shirking points to
an alternative, and perhaps less costly, way of reducing the risk
of provider shirking. Outsourcing managers can seek to enhance
the provider’s investment in resources specialized to the relationship. This could include idiosyncratic investments in information
technology, employee training, inter-organizational process integration, etc. which increase the cost to the provider should the
relationship terminate.
6.2. Inﬂuence of buyer power on provider opportunism
As with the assessment of the exchange hazard hypotheses, the
full (fourth stage) models in Table 5 will be used to evaluate the
shirking and poaching hypotheses. As hypothesized, the regression

65

coefﬁcient of the impact of mediated power on provider shirking is
positive and signiﬁcant (ˇ = 0.247; p < 0.01), as is the effect of mediated power on poaching (ˇ = 0.159; p < 0.05). These results offer
evidence in support of H4a and H4b. The two coefﬁcients related
to the inﬂuence of non-mediated power on shirking (ˇ = −0.241;
p < 0.01) and poaching (ˇ = −0.137; p < 0.10) are also both statistically signiﬁcant in the intended direction. This bolsters H5a and
H5b.
Gaining provider compliance through intrinsic forms of motivation associated with non-mediated power not only positively
effects the provider’s normative commitment and satisfaction
with the relationship (Brown et al., 1995; Maloni and Benton,
2000; Benton and Maloni, 2005), but also has a more tangible impact. Inﬂuence achieved through non-mediated forms of
power diminishes the concern of opportunistic behaviors which
have been identiﬁed as being detrimental to the value of outsourcing engagements. Over time, this allows the outsourcing
ﬁrm to effectively manage the relationship with less need for
costly control and monitoring mechanisms (Ring and Van De Ven,
1994). Conversely, ﬁrms that rely on the use mediated forms of
power may be doing irreparable harm to relationship quality and
inadvertently raising the risk of shirking and poaching. At a minimum, this burdens the outsourcing ﬁrm with difﬁcult to observe
agency costs and it potentially results in excessive expenditures
on formal control and monitoring devices (Wathne and Heide,
2000).
These ﬁndings are notable from a managerial perspective.
Extant literature has observed that the use of mediated power
“represents the competitive and negative uses of power” (Benton
and Maloni, 2005). The use of mediated power establishes a zerosum, adversarial environment in the relationship. It sets the stage
for the provider seeking to wring value from the relationship at
the expense of their customer, as their interests have become
more divergent. Before utilizing mediated forms of power, outsourcing managers need to fully consider the potential risks and
long-term costs associated. Once the adversarial environment has
been established, buying organizations will need to deal with unintended provider actions and face the costs associated with the
increased need for formal safeguards against opportunism. While
mediated power appears to exacerbate the provider’s “rational”
motivation for opportunistic behavior, reliance on non-mediated
power reduces the risk of opportunism. With non-mediated power,
the provider acts in a manner consistent with the interests of the
buyer not because they are forced to, but rather because they
willingly choose to. Although the opportunity for opportunism
may exist, they elect to not take advantage of the situation. As
such, it is clear that outsourcing managers should strive to gain
provider compliance through non-mediated means. Similar to the
establishment of provider relationship-speciﬁc investments, buyer
reliance on non-mediated power can serve as an alternative to formal control mechanisms (e.g. incentive contracting and monitoring
mechanisms) in the ﬁght against provider shirking and poaching. Non-mediated power is based on the provider being proud
to be associated with the customer, or viewing the outsourcing
ﬁrm as an expert from whom they can learn and improve. The
buying organization being willing to serve as positive business
reference for the provider, or being willing to devote resources
to helping the provider improve their products and services are
examples of means by which the outsourcing ﬁrm could enhance
their power in the relationship through non-mediated avenues.
These actions can result in the provider wanting to go to great
lengths to preserve the relationship and avoid any behavior which
may threaten its continuation. Over the long-term, reliance on
non-mediated power favorably alters the risk–reward relationship
with outsourcing. The opposite is true for reliance on mediated
power.

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S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

7. Conclusions and future research
As outsourcing continues to be a prevalent business practice
and many ﬁrms are left disappointed with the results, it is imperative that a better understanding of the drivers of the total cost
of outsourcing is developed. Shirking and poaching are speciﬁc
forms of provider opportunism which can play a value-destroying
role in outsourcing. The principal goal of this study was to examine the effects that exchange hazards and outsourcing ﬁrm power
have on service provider shirking and poaching. Prior studies of
inter-organizational opportunism have not concurrently examined
the effects of exchange hazards and power in a common model.
Pursuit of this research objective resulted in a substantive contribution to the outsourcing literature. Our statistical results offer
evidence that outsourcing engagements characterized by higher
levels of buyer relationship-speciﬁc investments and inﬂuence
derived from mediated power bases face a higher risk of provider
opportunism. Conversely, higher levels of provider relationshipspeciﬁc investments and buyer reliance on non-mediated power
bases are associated with a diminished risk of provider opportunism. These conditions not only reduce the risks of outsourcing,
but also demand lower investments in monitoring and formal control mechanisms. This results in a more favorable risk–reward
relationship for the outsourcing organization. Additionally, this
study contributes reliable, validated scales for shirking and poaching. The analysis demonstrated the value of looking at speciﬁc forms
of opportunism. Relationship-speciﬁc investments have a significant association with provider shirking, but not with poaching.
Only by distinguishing between shirking and poaching were we
able to ﬂesh out these insights. The results of this study represent
a valuable start to developing a deeper and more nuanced understanding of how relationship management practices, in addition to
commonly noted exchange hazards, can exacerbate or attenuate
the risk of opportunism with outsourcing.
The contributions of this study can be extended by future
research efforts. Exchange hazards and the basis of outsourcing
ﬁrm power were found to have a meaningful impact on service provider opportunism. It is tenable that other environmental
conditions and ﬁrm practices can instigate or mitigate provider
opportunism. Subsequent studies could explore how contingency
effects between outsourcing context and management practices
can more fully explain provider opportunism. In addition, our
measure of relationship-speciﬁc investments included two general
items representing the two most germane forms of speciﬁcity identiﬁed in the literature: physical and human asset speciﬁcity (Grover
and Malhotra, 2003; Williamson, 1979). Future studies focusing
on speciﬁc types of outsourcing may beneﬁt from more detailed
measures of speciﬁcity leading to additional insights. Moreover,
consideration could be given as to what factors appear to inﬂuence
buying ﬁrm opportunism. Finally, future efforts should be directed
toward evaluating the drivers of other forms of opportunism and
outsourcing transaction costs such as monitoring and coordinating.
A better understanding of factors inﬂuencing these costs can result
in improved, more realistic outsourcing business case development
and more effective relationship management practices.
Appendix A.
Buyer relationship-speciﬁc investments (1 = strongly disagree;
4 = neutral; 7 = strongly agree)
(BRSI1) We have made substantial investments in personnel dedicated to this provider.
(BRSI2) We have made substantial investments in capital equipment and technology dedicated to this provider.

Provider relationship-speciﬁc investments (1 = strongly
disagree; 4 = neutral; 7 = strongly agree)
(PRSI1) We have made substantial investments in personnel dedicated to «Customer».
(PRSI2) We have made substantial investments in capital equipment and technology dedicated to «Customer».
Technological uncertainty
(TU1) For the services we provide to «Customer», our core process
technologies change: (1 = very slowly; 7 = very quickly).
(TU2) For the services we provide to «Customer», our core process
technologies become obsolete: (1 = very slowly; 7 = very quickly).
(TU3) For the services we provide to «Customer», the trends in our
core process technologies are: (1 = very easy to monitor; 7 = very
difﬁcult to monitor).
Reward power (1 = strongly disagree; 4 = neutral; 7 = strongly
agree)
(RP1) «Customer» offers incentives to our ﬁrm when we are initially reluctant to cooperate with a new program.
(RP2) «Customer» will favor us on other occasions if we go along
with their requests.
(RP3) «Customer» offers us rewards so we will go along with their
wishes.
Coercive power (1 = strongly disagree; 4 = neutral; 7 = strongly
agree)
(CP1) If we do not do as they ask, we will not receive very good
treatment from «Customer».
(CP2) If we do not agree with «Customer»’s suggestions, they could
make things difﬁcult for us.
(CP3) «Customer» makes it clear that failing to comply with their
requests will result in penalties against us.
Legal legitimate power (1 = strongly disagree; 4 = neutral; 7 =
strongly agree)
(LLP1) «Customer» often refers to the terms of our contract to gain
our compliance on particular requests.
(LLP2) «Customer» makes a point to refer to our legal agreement
when attempting to inﬂuence us.
(LLP3) «Customer» uses sections of our formal agreement as a
“tool” to get us to agree to their demands.
Referent power (1 = strongly disagree; 4 = neutral; 7 = strongly
agree)
(RFP1) We admire the way that «Customer» runs their business.
(RFP 2) We often do what «Customer» asks because we are proud
to be afﬁliated with them. (Removed from ﬁnal analysis)
(RFP 3) We talk up «Customer» to our colleagues as a great business
with which to be associated.
Expert power (1 = strongly disagree; 4 = neutral; 7 = strongly
agree)
(EP1) We see «Customer» as an expert in their industry.
(EP 2) We respect the judgment of «Customer»’s representatives.
(EP 3) Our ﬁrm believes that «Customer» retains business expertise
that makes them likely to suggest the proper thing to do.

S.M. Handley, W.C. Benton Jr. / Journal of Operations Management 30 (2012) 55–68

Provider shirking (1 = very unlikely; 7 = very likely)
(PS1) If «Customer» were not able to detect it, how likely are members your organization to. . . Not assign your best people to your
business or account with «Customer».
(PS2) If «Customer» were not able to detect it, how likely are members your organization to. . . Provide a lower than agreed to level
of resources.
(PS3) If «Customer» were not able to detect it, how likely are members your organization to. . . Withhold information that may be
beneﬁcial to «Customer».
(PS4) If «Customer» were not able to detect it, how likely are members your organization to. . . Not share in the beneﬁts of process
improvements.
(PS5) If «Customer» were not able to detect it, how likely are members your organization to. . . Delay making agreed to investments
in employee training.
(PS6) If «Customer» were not able to detect it, how likely are members your organization to. . . Delay making agreed to investments
in new technology.
Provider poaching (1 = very unlikely; 7 = very likely)
(PP1) If «Customer» were not able to detect it, how likely are
members your organization to. . . Use potentially proprietary
information obtained through your relationship with «Customer»
to gain favor with other clients.
(PP2) If «Customer» were not able to detect it, how likely are
members your organization to. . . Use potentially proprietary
information obtained through your relationship with «Customer»
to help win new business with other customers.
(PP3) If «Customer» were not able to detect it, how likely are
members your organization to. . . Use potentially proprietary
information obtained through your relationship with «Customer»
to develop new services that you can offer in the marketplace.

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