Professional Nursing Organization and CertificationGuidel.docx

Professional Nursing Organization and Certification:
GuidelinesPurpose
The purpose of this assignment is to investigate a professional
nursing organization that offers certification in an area of
clinical or nonclinical expertise. You are to select a
professional nursing organization and determine if it offers a
certification that will be of benefit to you in your current
position or a future position to which you aspire. The
organization and certification may be in either a clinical or
nonclinical area; however, leadership development opportunities
are important.
For example, you may aspire to be a Certified Registered Nurse
Anesthetist (CRNA). Or, you may be currently interested in
becoming a Certified Wound Care Specialist or Medical-
Surgical Nurse.
You will complete the form provided in Doc Sharing carefully
providing all the information requested.Course Outcomes
This assignment enables you to meet the following course
outcome(s).
CO 1: Apply leadership concepts, skills, and decision making in
the provision of high quality nursing care, healthcare team
management, and the oversight and accountability for care
delivery in a variety of settings. (PO #2)
CO 6: Develop a personal awareness of complex organizational
systems and integrate values and beliefs with organizational
mission. (PO #7)
CO 8: Apply concepts of quality and safety using structure,
process, and outcome measures to identify clinical questions as
the beginning process of changing current practice. (PO #8)
Directions
1. Review the following (or similar) website and selects a

professional nursing organization offering a certification of
interest to you in your current position or one to which you
aspire. www.nurse.org/orgs.shtml
2. Thoughtfully and completely answer the questions on the
form.
3. Submit the form with your answers to the Dropbox by 11:59
p.m. (MT) on Sunday of Week 2.
4. Review the section on Academic Honesty found in
Chamberlain Course Policies. All work must be original (in
your own words).
5. BTLS, ACLS, ATLS, NALS, PALS and other similar
certifications do NOT qualify for this assignment. If you have
any questions about the organization or the certification, contact
your instructor for clarification.
Grading Criteria
Category
Points
%
Description
Description of professional organization
40
18%
Names organization and describes the organization’s mission,
vision, and values. Indicates membership eligibility criteria and
yearly financial implications of membership.
Workable link to organization’s website is included for faculty
review.
Certification requirements
30
13%
Describes criteria for initial certification including education,
testing, required practice hours (if applicable), fees etc.
Recertification requirements
30
13%

Describes criteria for recertification including education,
testing, practice hours (if applicable), fees etc.
Practice impact
50
22%
Describes how membership (participation in the organization’s
activities) will benefit your nursing practice, improve patient
outcomes, enhance quality, safety, etc.
Certification impact
50
22%
Describes how certification and recertification will benefit your
nursing practice, improve patient outcomes, enhance quality,
safety, etc.
Scholarly writing and formatting
25
12%
· Date, name of the student and instructor appears on the form.
· Punctuation and sentence structure are correct.
· Evidence of spell and grammar check.
Total
225 points
100%
A quality assignment will meet or exceed all of the above
requirements.
Grading Rubric
Assignment Criteria
A
Outstanding or Highest Level of Performance
B
Very Good or High Level of Performance
C
Competent or Satisfactory Level of Performance
F

Poor or Failing or Unsatisfactory Level of Performance
Description of professional organization
40 points
Thoroughly describes organization’s mission, vision, and
values, indicating membership eligibility criteria and yearly
financial implications of membership.
Working link to organization’s website is included for faculty
review.
37–40 points
Clearly describes the organization’s mission, vision, and values,
indicating membership eligibility criteria. Omits financial
implications of membership.
Working link to organization’s website is included for faculty
review.
34–36 points
Briefly describes the organization’s mission, vision, and values.
Omits membership eligibility criteria and yearly financial
implications of membership.
Working link to organization’s website is not included for
faculty review.
31–35 points
Does not describe the organization’s mission, vision, and
values. Omits other key information.
Working link to organization’s website is not included for
faculty review.
0–30 points
Certification requirements
30 points
Thoroughly describes criteria for initial certification including
education, testing, required practice hours (if applicable), fees
etc.
28–30 points
Clearly describes criteria for initial certification but omits one
of the following: education, testing, required practice hours (if
applicable), fees etc.
26–27 points

Briefly describes criteria for initial certification but omits two
or three of the following: education, testing, required practice
hours (if applicable), fees etc.
23–25 points
Does not describe criteria for initial certification.
0–22 points
Recertification requirements
30 points
Thoroughly describes criteria for recertification including
education, testing, practice hours (if applicable), fees etc.
28–30 points
Clearly describes criteria for recertification but omits one of the
following: education, testing, practice hours (if applicable),
fees etc.
26–27 points
Briefly describes criteria for recertification but omits two or
three of the following: education, testing, practice hours (if
applicable), fees etc.
23–25 points
Does not describe criteria for recertification.
0–22 points
Practice impact
50 points
Thoroughly describes how membership (participation in the
organization’s activities) will benefit your nursing practice,
improve patient outcomes, enhance quality, safety, etc.
46–50 points
Clearly describes how membership (participation in the
organization’s activities) will benefit your nursing practice but
does not mention improvement of patient outcomes OR
enhanced quality, safety, etc.
42–45 points
Briefly describes how membership (participation in the
organization’s activities) will benefit your nursing practice but
does not mention improvement of patient outcomes AND
enhanced quality, safety, etc.

38–41 points
Does not describe how membership (participation in
organization’s activities) will benefit patient care.
0–37 points
Certification impact
50 points
Thoroughly describes how certification and recertification will
benefit your nursing practice, improve patient outcomes,
enhance quality, safety, etc.
46–50 points
Clearly describes how certification and recertification will
benefit your nursing practice but does not mention improvement
of patient outcomes OR enhanced quality, safety, etc.
42–45 points
Briefly describes how certification and recertification will
benefit your nursing practice but does not mention improvement
of patient outcomes AND enhanced quality, safety, etc.
38–41 points
Does not describe how certification and recertification will
benefit patient care.
0–37 points
Scholarly writing and formatting
25 points
Date, name of the student and instructor appears on form.
No punctuation or sentence structure errors are noted.
No spelling or grammar errors noted.
23–25 points
Date or name of the student or instructor is missing
One-two punctuation and/or sentence structure errors noted.
One-two spelling or grammar errors noted.
21–22 points
Date and name of the student or instructor are missing.
Three-four punctuation and/or sentence structure errors noted.
Three-four spelling or grammar errors noted.
19–20 points
No student identification noted.

Multiple typos noted.
Multiple grammar and punctuation errors noted.
No evidence of proof-reading prior to submitting assignment.
0–18 points
Total possible points = 225
Your score=
A quality assignment will meet or exceed all of the above
requirements.
2
121
AbstrAct Global sourcing will continue to have a major
impact on IS
organizations. Fourteen “new” and traditional skills that IS
organizations will
need in tomorrow’s global sourcing environment are
highlighted.
Keywords global IS management, human resource
management,
IS capabilities, IS organization of the future, outsourcing
The IS organization of the future will be greatly impacted by
the IT global
sourcing phenomena that became highly visible after Kodak
outsourced
its data center operations in 1989 and which has since grown
rapidly to

encompass offshoring as well as domestic outsourcing. IS
departments have
already been impacted — some in carefully-conceived and
planned ways;
others in unplanned ways that have created a degree of chaos.
The basic rule today is that, “every IT activity that can be
outsourced, may
be outsourced,” because vendors are developing ever-more-
sophisticated
capabilities and the labor arbitrage and limited investment
imperatives of
both domestic and offshore outsourcing operate to demand that
outsourcing
be considered for most IT activities.
An Association for Computing Machinery Report (Aspray,
Mayadas, &
Vardi, 2006) delineates six varieties of work related to IS that
are often off-
shored: (1) programming, software testing, and software
maintenance; (2) IT
research and development; (3) high-end jobs such as software
architecture,
product design, project management, IT consulting, and
business strategy; (4)
physical product manufacturing — semiconductors, computer
components,
computers; (5) business process outsourcing/IT Enabled
Services — insur-
ance claim processing, medical billing, accounting,
bookkeeping, medical
transcription, digitization of engineering drawings, desktop
publishing, and
high-end IT enabled services such as financial analysis and
reading of X-

rays; and, (6) call centers and telemarketing. Some of these are
more relevant
to future IS organizational management than are others.
To visualize what the IS organization will look like as this
nearly 20-year-
old process continues further, one needs to focus on those
activities that
cannot be conveniently outsourced, those that are too important
to be out-
sourced and those that need to be given greater attention in this
evolving
sourcing environment.
Address correspondence to
William King,
Katz Graduate School of Business,
University of Pittsburgh, PA 15260,
USA. E-mail: [email protected]
Address correspondence to
William King,
Katz Graduate School of Business,
University of Pittsburgh, PA 15260,
USA. E-mail: [email protected]
The IS Organization of the Future:
Impacts of Global Sourcing
william r. King
University Professor,
University of Pittsburgh,
Katz Graduate School
of Business,

Pittsburgh, PA, USA.
Information Systems Management, 24:121–127, 2007
Copyright © Taylor & Francis Group, LLC
ISSN: 1058-0530 print/1934-8703 online
DOI: 10.1080/10580530701221015
King 122
GLobAL soUrcING As A bAsIs
For coNcePtUALZING tHe
Is “sHoP” oF tHe FUtUre
The logic behind offshoring is twofold. On the
one hand, it is a version of the outsourcing trend in
other areas such as manufacturing, which posits that
organizations should focus on their core competen-
cies while contracting other necessary activities to
specialists in those activities. It is also based on the
classic strategy of economic arbitrage — the exploi-
tation of price differences in order to profit from
them. Thus, the offshoring of IT activities arbitrages
wage differentials in the provision of outsourcing
services by domestic or offshore contractors or by
creating company operations in offshore locations.
The internal IS function is often viewed by senior
mangers to be costly, difficult to understand and
peripheral to the firm’s core business. As firms
shifted in the 1990s to business strategies that focus
on their core competencies rather than strategies for
mediating risk through diversification as had previ-
ously been emphasized, attention was focused on
the role that IT played in the enterprise. Since IS was

frequently judged to be non-core, it became a prime
candidate for outsourcing.
The most important reasons for IS outsourcing are
its perceived non-core nature (Grover & Teng, 1993),
the significant cost savings that may often be gained
if IS activities were performed outside the firm (Loh
& Venkatramen, 1992), the difficulty that firms have
in assessing the business value contributed by IT and
the lack of understanding of IT by top-level business
executives (Lacity & Hirschheim, 1993).
As Larry Ellison, CEO of Oracle has said, “Why
should every automaker, publisher or doctor’s office
have to be a tech company too, employing high-paid
staff who spend all of their time fiddling around
with computers?” (Kowula, 2004).
There has been considerable discussion in the lit-
erature of problems that may arise in outsourcing;
these also apply to offshore outsourcing. These prob-
lems include structural, cultural, legal, and financial
risks and costs. In the context of IT offshoring, addi-
tional problems may arise; these include the loss of
IS skills to a degree that the client firm no longer has
a choice of whether to outsource and the possibility
of major offshore disasters. These problems may be
exacerbated by the fact that IT activities are now so
closely interwoven with organizational activities in
general that it may be difficult to determine which
activities indeed lie outside the organization’s core
(King & Malhotra, 2000).
Regardless of these problems, it is inevitable that
outsourcing and offshoring will continue to increase

in volume and importance (Davis, Ein-dor, King,
& Torkzaden, 2007). Therefore, it is important that
the IS function designs, constructs and manages an
IS organization that has both the traditional func-
tions and skills that are too important or risky to
outsource and the non-traditional skills and capa-
bilities that will be important to success in this new
environment.
tHe NEW ANd trAdItIoNAL
Is roLes tHAt For A GLobAL
soUrcING eNVIroNMeNt
In the future, the global sourcing phenomenon
will dictate that IS organizations will need greater
skills in a number of areas that will importantly
determine the efficacy of the IS organization of the
future. In the areas of the contract and the relation-
ship with vendors, the critical skills are:
contract negotiations and management;
relationship management;
developing and implementing partnerships, stra-
tegic alliances, and joint ventures;
vendor and partner assessment and selection;
and,
risk assessment and management
In the areas of system implementation and new
systems development, the critical skills are:
collaborative system customization, implementa-
tion and integration;
technology assessment and monitoring;

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These problems include
structural, cultural, legal, and
financial risks and costs.
123 Impacts of Global Sourcing
business process redesign;
integrated business and IS planning;
mission-critical systems development and testing;
and,
systems testing.
Among the general skills that IS will need to
emphasize are:
security;
IS personnel development; and,
awareness of national cultures.

the contract and relationships
with Vendors
Areas related to the sourcing contract and rela-
tionships with vendors are discussed first below:
contract negotiation and management, relationship
management, developing, negotiating and imple-
menting partnerships, strategic alliances and joint
ventures, vendor and partner assessment and selec-
tion and risk assessment and management.
Contract Negotiation and Management
One of the things that the early birds outsourc-
ing clients quickly realized was that when one out-
sources an activity, it needs to be replaced by another
activity that will focus on developing, negotiating,
implementing, and controlling the contract.
Although the basic offshore contract will be
drawn up by attorneys, they are usually not well-
informed about IT and, in my experience, leaving
the task to them is often problematic. For instance,
IS people need to provide expertise on service-level
benchmarks that should be put into the contract. IS
people will also know which areas of activity are
most dynamic and therefore most likely to require
changes as the term of the arrangement progresses.
This will permit the contract to be written in a man-
ner that will accommodate reasonable change rather
than, as has been the situation in some instances,
allowing changes to become so costly that the client
believes that the vendor, who is in something of a
monopoly position when changes are requested, is
taking advantage of this position.

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⦁
Relationship Management
Effective relationship management has been fre-
quently shown to be related to outsourcing success.
Many firms who thought that they could offshore
through a contract and then do little to monitor
and manage the client-vendor relationship have
been surprised with negative results from this style
of outsourcing management. In these instances,
communications and coordination processes and
their associated costs often were not given much
attention.
For success in global sourcing, close attention
must be paid to everything about the client-vendor
relationship, from the criteria for selecting a vendor,
to the frequent monitoring of progress, to the level
of control exerted over the vendor and to the level of
trust that is developed in the client-vendor relation-
ship. None of these things can be ignored or taken
lightly since all have been shown to be critical suc-

cess factors for effective outsourcing.
Developing, Negotiating and
Implementing Partnerships; Strategic
Alliances, and Joint Ventures
Although many offshoring client-vendor relation-
ships are referred to as partnerships, few of them
are much more than contractual business relation-
ships. However, this is likely to change in the future
as two or more organizations recognize that they
have complementary skills and that for one organi-
zation to develop the entire range of skills that are
necessary for success in IS is excessively expensive.
Whether these cooperative ventures are formatted
as real partnerships, or strategic alliances or joint
ventures, the identification of the need for coop-
erative effort, the negotiation of the deal, and the
implementation of an arrangement which provides
For success in global sourcing,
close attention must be paid
to everything about the
client-vendor relationship.
King 124
benefits to all parties is a skill that IS departments
increasingly need to develop.

Vendor and Partner Assessment
and Selection
Since the days of the early computer era when
outside vendors were used primarily for hardware,
vendor assessment and selection has played a role
in the IS organization. However, in this new era,
it becomes of greater importance. The success or
failure of the entire IS function can rest on the per-
formance of a few vendors to whom critical tasks
have been outsourced. So, picking the right vendors
becomes an IS critical success factor. Having a meth-
odology and people who are skilled in applying it is
key to success.
The same is true of potential partners and alliance
participants. The IS organization must be aware of the
array of potential partners and not merely respond
to opportunities that are presented in a haphazard
fashion. This involves identifying potential partners
and their capabilities, assessing each for their fit with
one’s organization and for likely major new projects
for which complementary skills may be required.
Risk Assessment and Management
Risk assessment and management will become
a greater focus in vendor selection and in continu-
ing relationship management. The risks that are
involved in performing critical functions in Third-
World countries have not been fully recognized by
many firms who have begun offshoring. Everything
from political risk, to risks of natural disasters, to
the risks associated with marginal communications
infrastructures needs to be identified and moni-
tored. After all, India — the primary location for

offshore vendors — almost became involved in
a nuclear confrontation only a few years ago and
while international communications from India
have improved dramatically, local communications
and transportation infrastructures are often mar-
ginal. These lead to greater risk, especially when
unplanned activities must be performed. Often, this
will be addressed using backup sites, often in the
Phillipines, but this involves a new layer of com-
plexity in the overall management process. Kliem
(2004) provides a good framework for assessing
risks in global sourcing.
system Implementation and
New system development
The areas that relate to system implementation
and new systems development are discussed below
in terms of collaborative system customization,
implementation and integration, technology assess-
ment and monitoring business process redesign,
integrated business and IS planning, mission-critical
systems development and systems testing.
Collaborative System Customization,
Implementation and Integration
The focus for systems implementation and integra-
tion will need to shift from an internal orientation to
one which addresses working in joint consultant-cli-
ent teams. For instance, the typical ERP implementa-
tion project, in which joint teams work, sometimes
for extended periods, to customize and implement
a vendor-supplied system to meet a firm’s unique
needs, is a good prototype for a process that will

become increasingly common for various types of
vendor-supplied systems. Thus, inter-firm implemen-
tation processes will need to be more fully devel-
oped (eg., Ko, Kirsch, & King, 2005).
So, system customization, implementation and
integration is another area in which competence
must be maintained and enhanced by an IT depart-
ment that is going out of the programming and sys-
tems development business, (which will increasingly
be the norm). Increasingly, software will be devel-
oped by vendors, purchased by clients and then cus-
tomized and integrated with other internal systems.
These implementation and integration processes
The risks that are involved in
performing critical functions in
Third-World countries have not
been fully recognized.
manager realizes that he/she must do something to
replace those old sources of information concerning
technology.
So, the client must independently assess evolv-
ing technology in order to maintain an awareness
of potential service-level improvements that may
become feasible through technological advances.

The client must continuously be aware of the
technological offerings and service levels offered by
other vendors as well. Even if a client is involved in
a long-term contract, this is necessary. It also illus-
trates why negotiations and the terms of the contract
are so important. No client should allow themselves
to be truly “locked into” a long-term contract in
which the vendor can attempt to provide, on a con-
tinuing basis, service levels that are less than oth-
ers routinely offer. Contracts must provide for the
continuous benchmarking of service-levels against
other providers.
Business Process Redesign
Business processes cannot be effectively rede-
signed at a distance; direct contact between analysts
and employees who are involved in operating the
processes is required. Therefore, while many offshor-
ing contracts relate to operating business processes,
the analysis and modeling skills that are required for
process redesign must reside in the organization’s
internal IS function.
Integrated Business and IS Planning
Strategic IS planning is the link between the busi-
ness strategy and the mission, strategy, goals and
architectures for IS in the organization. As such,
this planning process requires in-depth understand-
ing of the firm. It should never be outsourced or
offshored.
may be aided by external consultants, but they often
cannot be effectively done by outsiders; an inter-
nal capability that reflects a deep understanding of

the business, its operations, goals and priorities, is
required. This extends to the software testing arena
since externally-developed software, must be thor-
oughly tested on an independent basis.
Even when external consultants are used in these
roles, the goal of the client must be to have their
own personnel learn the skills that are necessary to
perform these tasks with ever-less levels of outside
help
Technology Assessment and Monitoring
In an outsourcing/offshoring environment, a tech-
nology assessment capability must be maintained,
or developed, by the outsourcing client since the
vendor’s objectives with regard to technology are
not always consistent with those of the client. In
many situations, vendors wish to consolidate the
work of many clients on their own legacy technol-
ogy to achieve economies of scale and high returns.
This may not always well serve specific clients, even
if it meets their initial cost goals, since some clients
might benefit greatly from greater accuracy, reduced
cycle time or a greater security level than is initially
offered by the vendor.
The day-to-day monitoring of technological
advances may, in fact, be performed outside the cli-
ent organization. But the CIO and other IS execu-
tives must be certain that they are aware of these
developments, if only because it will enable them to
anticipate technological changes that a vendor may
be about to consider, or that they may be motivated
to consider.

The need to independently keep abreast of tech-
nology becomes apparent to every CIO shortly after
he or she outsourced operational computing systems.
The outer office is no longer filled with vendor sales-
people because the outsourcing client is no longer a
potential customer for entire categories of hardware
and software. Only on recognizing that the outer
office is no longer full do many IS executives realize
how much important technological information they
formerly obtained from salespeople. Those “pests
waiting for an appointment” (as one IS executive put
it) suddenly are recognized for their value and the IS
Contracts must provide
for the continuous benchmarking
of service-levels against
other providers.
King 126
IS strategic planning has been integrated into stra-
tegic business planning in many firms. This activity
will need to be maintained as no firm can ignore
the potential role of IT in its future business strategy.
When outsourcing takes place, top managers tend
to presume that IT’s role in the business is lessened
and they may give less attention to it. IT people must
understand business strategy and IT’s role in it (even
when large segments of traditional IT have been out-
sourced) and keep these issues in the mix of those
treated in strategic business planning.

Mission-Critical Systems Development
The development of mission-critical software/sys-
tems must usually be retained in-house since this
is where the essence of one’s informational core
competence resides. Most organizations have trade
secrets and/or critical key processes embedded in
their software and systems that they would not wish
to be made available to outsiders.
Systems Testing
The testing of software is typically performed by
the developer, but in the case of offshored develop-
ment, clients often wish to perform their own post-
delivery testing.
General skills
Among the “general skills” that IS must maintain
or attain are security, IS personnel development and
awareness of national cultures.
Security
Sharing critical processes and software with ven-
dors may increase risk to some degree. Of course,
most vendors apply elaborate security systems and
procedures. Indeed, in some cases, consultants have
found that vendor security is better than client secu-
rity. Nonetheless, the ultimate responsibility for the
security of data, especially customer data, is with
the client, so the necessary skills must be available
in-house to oversee security.

IS Personnel Development
IS employee development programs involving the
IS jobs that are kept in-house as well as the IS inter-
face jobs in the marketing, production, finance and
other departments should also be retained in-house.
Such programs may involve on-the-job training and/
or job rotations through IS and business functional
job assignments. In that way, career progressional
plans can be developed involving the set of IS func-
tions that are retained.
Awareness of National Cultures
Another critical need will be developing an under-
standing of relevant foreign cultures. For instance,
the Indian culture is quite unique. Although the
caste system has been officially outlawed, its vestiges
remain strong. In many vendor nations, businesses
and government agencies operate on the routine
basis of bribery, hiring of relatives, unaccountabil-
ity of employees who are relatives, and other-than-
merit-based promotions. Of course, large Indian
outsourcing vendors are much less traditional and
more Westernized, but one need only look at the
marriage system to recognize how much traditional
cultural practices permeate all aspects and levels of
Indian society.
Anyone who routinely deals with foreign vendors
must recognize these, and many other, aspects of
the national culture of the vendor in order to under-
stand the proposals of, and responses given by, the
employees of foreign vendors. Rao (2004) provides
details of “best practices” in this, and other areas of

global sourcing.
Nonetheless, the ultimate
responsibility for the security
of data, especially customer
data, is with the client.
coNcLUsIoNs
To summarize, global sourcing must be treated as
a major and central IS paradigm for the IS organiza-
tion of the future. It can no longer be thought of as
an interesting appendage to basic IS.
Second, the specific skills necessary for perform-
ing the activities that will remain in the IS portfolio
in the new world of global sourcing– contract nego-
tiation and management, relationship management,
developing and implementing partnerships, strate-
gic alliances, and joint ventures; vendor and partner
assessment and selection, risk assessment and man-
agement, technology assessment and monitoring,
collaborative systems customization, implementation
and integration, business process redesign, integrated
business and IS planning, mission-critical systems
development, system testing, security, IS person-
nel development and greater awareness of relevant
national cultures — must be given central focus in
the IS organization of the future. Many of these are

typically not major foci of today’s IS departments.
This means that IS people will need to under-
stand negotiation techniques, contract law, change
management and develop the “softer” skills involved
in partnering and developing trust between client
and vendor. Strategic issues such as understanding
the sort of benefits that may be expected from vari-
ous kinds of possible “strategic alliances” with ven-
dors will become essential.
All of the key strategic management concepts
— core competencies, critical success factors, inter-
nal markets etc., which have been little known to
IS professionals, must also become familiar to them
through revised curricula and training.
reFereNces
Aspray, W., Mayadas, F., & Vardi, M. Y. (Eds.) (2006).
Globalization and
offshoring of software: A report of the ACM job migration task
force. Association for Computing Machinery.
Davis, G., Ein-dor, P., King, W. R., & Torkzadeh, R. (2007). IT
offshor-
ing: History, prospects and challenges. Journal of the
Association for
Information Systems, to appear.
Grover, V., & Teng, J. T. C. (1993). The decision to outsource
Information
Systems functions. Journal of Systems Management, 44 (11),
34.
King, W.R., & Malhotra, Y. (2000, September). Developing a

framework
for analyzing IS sourcing, Information and Management, 37 (6),
Sep-
tember, 323–334.
Kliem, R. (2004). Managing the risks of offshore IT
development project,
Information Systems Management, 21 (3), 22–27.
Ko, D. G., Kirsch, L., & King, W. R. (2005, March).
Antecedents of
knowledge transfer from consultants to clients in enterprise
system
implementations. Management Information Systems Quarterly,
29
(1), 59–85.
Kowula, A. (2004, February 24). Outsourcing is not the enemy.
Wall
Street Journal, pB-2.
Lacity, M.C., & Hirschheim, R. (1993). Information systems
outsourcing:
Myths, metaphors and realities. Chichester, U.K.: Wiley.
Loh, L., & Venkatraman, N. (1992). Determinants of
Information Tech-
nology outsourcing: A cross-sectional analysis, Journal of
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ment Information Systems, 9 (1), 7.
Rao, M.T. (2004). Key issues for global IT sourcing: Country
and indi-
vidual factors. Information Systems Management, 21 (3), 16–
21.

bIoGrAPHy
William R. King holds the title University Professor
in the Katz Graduate School of Business at the Univer-
sity of Pittsburgh. He has served as president of TIMS
(now INFORMS), founding president of the Association
for Information Systems (AIS), and editor-in-chief of MIS
Quarterly.
Global sourcing must be treated
as a major and central
IS paradigm for the
IS organization of the future.
Hahn et al./Evolution of Risk in IS Offshoring
SPECIAL ISSUE
THE EVOLUTION OF RISK IN INFORMATION SYSTEMS
OFFSHORING: THE IMPACT OF HOME COUNTRY RISK,
FIRM LEARNING, AND COMPETITIVE DYNAMICS1
By: Eugene D. Hahn
Information and Decision Sciences
Salisbury University
Salisbury, MD 21801

U.S.A.
[email protected]
Jonathan P. Doh
Department of Management
Villanova School of Business
Villanova University
Villanova, PA 19085
U.S.A.
[email protected]
Kraiwinee Bunyaratavej
Business Administration and Accounting
Wesley College
Dover, DE 19901
U.S.A.
[email protected]
Abstract
Information systems offshoring has emerged as a significant
force in the global political economy, an important source of
firm-specific competitive advantage, and a focal point
for debates over the benefits and costs of globalization. As
1This paper was recommended for acceptance by Associate
Guest Editor
Erran Carmel.
worldwide competition exerts increasing pressure on the IS
function of firms to become geographically unbundled, and IS
services are dispersed among increasingly distant and
unfamiliar locations, the issue of risk emerges as a significant
factor in decisions about where to locate offshore facilities.
Drawing from prior research in IS outsourcing/offshoring
and theoretical perspectives from international strategy and
multinational management, we examine the determinants of
risk firms bear in their offshoring decisions. In particular,
the current paper explores firm-level and environment-level

“push” factors that drive firms to accept increasingly greater
degrees of host country risk. We predict that firm-level risk
outcomes for locating IS offshore facilities will be influenced
by prior firm-specific experience, the relative gap between
home and host country risk levels, and the overall movement
by IS offshore services providers toward increasingly riskier
locations. We test these hypotheses on a proprietary data set
of more than 850 information technology and software off-
shoring projects in 55 host countries worldwide during the
period 2000 through 2005. We find that firm-specific experi-
ence and the core “risk gap” between home and host country
are predictive of companies pursuing progressively riskier
locations, but that their effects dissipate as environment-wide
experience is incorporated into our model. Our analysis
suggests that broader dynamics in the competitive environ-
ment are powerful contributors to the overall observation that
IS offshoring is moving to increasingly high-risk locations.
This trend has implications for the management, security, and
global integration of information systems. Our study contri-
butes to the literature on risk and IS offshoring in providing
the first worldwide empirical examination of the determinants
of actual firm IS offshoring behavior with respect to off-
shoring location risk.
MIS Quarterly Vol. 33 No. 3, pp. 597-616/September 2009 597
Keywords: IS offshoring, risk, FDI, offshore outsourcing,
firm experience, empirical, longitudinal
Introduction
Over just the past decade, an ever-widening sphere of service

activities are being offshored, from accounting functions such
as tax preparation (e.g., Shamis et al. 2005) to architectural
design and medical imaging diagnostic interpretation
(UNCTAD 2004). These more recent offshoring trends owe
their existence to the first mass mover of services offshoring:
IS offshoring. The worldwide number of IS services jobs that
could be offshored elsewhere in the globe was estimated by
McKinsey & Co. to be a considerable 2.8 million in 2003
(Aspray et al. 2006). The origins of the growth in IS
offshoring can be traced to Y2K conversion-based needs in
Western countries in the years leading up to 2000 (Qu and
Brocklehurst 2003). With demand for programming greatly
outstripping supply, U.S. and European firms turned to off-
shoring to accomplish goals in required timeframes. Pleased
with the results from this first wave of offshoring, firms began
seeing increasingly more general opportunities for IS
offshoring. Other contributing factors to the IS offshoring
trend include spiraling IS costs in the 1990s as well as diffi-
culties in clearly documenting the contribution of IS invest-
ment to bottom-line firm financial considerations (Fish and
Seydel 2006). Perhaps most significantly, technological
advances in information technology have contributed sub-
stantially to the growth of IS offshoring. Indeed, it would be
difficult to conceive of the contemporary scope of offshoring
without the Internet and associated global high-speed transfer
of information. Highly-offshored IS activities to date have
included data-center operations, application maintenance,
network management, and user support (Barthélemy 2001).
Looking ahead, IT professionals predict that the next several
years are likely to be characterized by robust growth in off-
shoring of applications development and maintenance, data
center operations, telecommunications/LAN activities, and
systems development and maintenance (Fish and Seydel
2006). However, high client-contact activities like project
management are being retained in-house, rendering them
more immune to offshoring, at least for the present time (Fish

and Seydel 2006; Serapio 2005).
In undertaking IS offshoring, firms must make many deci-
sions; however, the determination of the appropriate country
in which to offshore has especially critical ramifications.
From an IS industry perspective, consultants at the Gartner
Group coined the phrase “country before company” to
describe the precedence of country location selection over
vendor selection in strategic IS offshoring decisions (Zatolyuk
and Allgood 2004). One of the more salient aspects of IS
offshoring and the global relocation of internal firm functions
is risk. Risk has, therefore, been a natural (and perhaps even
customary) starting point for research on the benefits and
costs of IS offshoring (Aspray et al. 2006; Erber and Sayed-
Ahmed 2005; Farrell 2004; King 2005; Tafti 2005). Indeed,
if one adopts Bahli and Rivard’s (2003) managerial definition
of risk as being uncertainty about negative outcomes, risk is
a common theme in the offshoring and outsourcing literatures.
For example, Dhar and Balakrishnan (2006) provide a recent
review of the literature on IS offshoring risk factors and
identify risk associated with transition costs, switching costs,
contract negotiations, litigation, reliability and service quality,
cost escalation and hidden costs, loss of core competencies,
vendor opportunism, and security breach to be among the
risks discussed in the literature. Kliem (2004) takes a dif-
ferent perspective and classifies risks more along firm func-
tional area lines to discern financial, technical, managerial,
behavioral, and legal risks of offshoring.
Despite the focus that IS offshoring risk has received from
conceptual and practitioner perspectives, and the development
of some typologies of offshoring risks, there is very little
empirical work that has examined actual firm-level invest-
ment decisions in IS offshoring in the face of country-level
risk. This leads us to the following key research question:

What factors drive firms to accept increasingly greater levels
of destination (host country) risk in the location of IS services
offshoring projects? Addressing this gap in the literature, we
develop theories of intra-firm and across-firm (competitive
environment-level) determinants of the risk levels firms
tolerate in the country-specific siting of IS offshoring projects
(see Figure 1). We test our theories using a global database
of IS offshoring projects covering the years 2000 through
2005. We find support for our theoretical predictions
regarding intra-firm and across-firm determinants of country-
specific location risk levels of IS offshoring projects.
Literature Review and Hypotheses
Development
In this section, we develop our theoretical perspectives on risk
in IS offshoring. We begin by reviewing relevant literature in
IS outsourcing and IS offshoring. In doing so, we highlight
major themes, describe existing theories, and identify a sub-
stantial gap in the empirical research to date. We then turn to
the literature in international risk, international strategy, and
multinational management to further inform our analysis. We
focus on research that has explored firm responses to country-
598 MIS Quarterly Vol. 33 No. 3/September 2009
Firm-Level:
• Firm Experience
Country-Level:
• Home Country GDP
• Home Country Risk Level

Project-Level:
• Sector
Competitive Environment:
• Risk Assumed by Overall
IS Offshoring Participants
IS Offshoring Destination
(Host Country) Risk Level
Firm-Level:
• Firm Experience
Country-Level:
• Home Country GDP
• Home Country Risk Level
Project-Level:
• Sector
Competitive Environment:
• Risk Assumed by Overall
IS Offshoring Participants
IS Offshoring Destination
(Host Country) Risk Level
Figure 1. Conceptual Model of Key Variables Related to Risk
and IS Offshoring Destinations
level risk, and the question of whether firms tend to pursue a
self-reproducing approach to their offshore location decisions
(e.g., continue to reinvest in the same locations) or engage in

a learning process in which they gradually assume higher
levels of risk in their investments as they and their com-
petitors learn more about these risk-prone environments.
Specifically, we explore whether the country risk of IS service
offshoring projects is (1) determined primarily by self-
reproducing behaviors that reflect past experience whereby
firm learning does not change firm proclivity toward risk,
(2) conditioned on firms gaining knowledge and experience
that influences the evolution of their subsequent investment
strategies toward riskier conditions, (3) characterized by the
“oligopolistic reaction” hypothesis in which firms imitate and
mimic leading competitors, gaining knowledge from the pro-
gression of the overall competitive environment. Figure 2
graphically depicts these varying perspectives and their
potential implications for earlier and later entrants into a
geographic location under conditions of risk. The first two
rows present opposing views of the motivations behind firm
entry decisions under these circumstances. The first row
reflects the notion that, all other things equal, firms tend to
repeat past actions in their entry location decisions. The
second row reflects a firm-specific learning perspective and
the expectation that firm location decisions will evolve based
on their past experience. The third row reflects the notion
that firms will also evolve and adjust their location decisions
based on the overall movement of industry competitors. In
total, we expect that firms learn from their own and compe-
titor behavior and incorporate that information into their
decision making. This perspective underscores the dynamic
view of market entry under conditions of risk and the impor-
tance of firm and competitor action and reaction to under-
standing location decisions in such environments. In our
analysis, we incorporate these differing perspectives in devel-
oping specific hypotheses related to firm learning and firm
reaction to the overall movements of their competitive en-
vironment. We also evaluate whether the difference between

the risk profile of the home and host country influences the
propensity of investors to take on increasing risk
IS Outsourcing, Offshoring, and Risk
Selected perspectives in the IS outsourcing literature form a
useful starting point for our analysis. This is because
domestic outsourcing can be viewed as an important special
case of global offshoring wherein many sources of risks to
firms such as country-level governmental/political risk,
currency fluctuations, differences in legal systems, and the
extent of institutional barriers to business and/or differences
in corruption are held constant or simply nonexistent.
Susarla et al. (2003) identified two major thrusts of the IS
outsourcing literature, one examining antecedents and the
other focusing on outcomes. In regard to the former, Loh and
MIS Quarterly Vol. 33 No. 3/September 2009 599
A
Firm
Country X
Lower
Risk
A
Firm
Country X

Lower
Risk
A
Firm
Country X
Lower
Risk
A
Firm
Country X
Lower
Risk
A
Firm
Country X
Lower
Risk
A
Firm
Country X
Lower
Risk

Firm
Country Y
Higher
Risk
A
Firm
Country Y
Higher
Risk
A
A
Firms
Country Y
Higher
Risk
B
C
D
A
Firms
Country Y

Higher
Risk
B
C
D
B
Firms
Country Y
Higher
Risk
C
D
B
Firms
Country Y
Higher
Risk
C
D

Early Moves Later Moves
Self-Reproducing Behavior Self-Reproducing Behavior
Move Toward Risk with Experience Move Toward Risk with
Experience
Environment Dynamic Environment Dynamic
Figure 2. Competing Explanations of Firm Risk Pursuit
Strategies
Venkatraman (1992) appear to have been the first to have
empirically established that the degree of outsourcing was
related to firm-level cost structures as well as IS performance,
while Ang and Straub (1998), using a transaction cost per-
spective, found that production cost advantages and lowered
transaction costs led to increases in the degree of outsourcing.
Factors contributing to IS offshoring success include positive
experiences with provider performance and absence of dis-
confirmation of performance expectations (Grover et al. 1996;
Lee and Kim 1999; Susarla et al. 2003). More recent works
in this tradition include Levina and Ross (2003), who empiri-
cally identified complementarities in vendor-client compe-
tencies with respect to market conditions as an outsourcing
success factor, and Lin et al. (2005), who indicate that effec-
tive knowledge transfer should improve benefits for out-
sourcers and vendors. At the individual level, Koh et al.
(2004) documented the importance of careful management of
person-to-person relationships between customers and sup-
pliers in IS outsourcing. Presumably this becomes more
important (and more challenging) in IS offshoring because of
cultural differences, language considerations, and large geo-
graphic distances. Ang and Cummings (1997) utilized the
lens of organizational theory to examine IS outsourcing in the

banking industry. Building on institutional theory, they
adopted a “variance” theoretic perspective. They tested the
homogenous influence of both competitor banks and federal
regulators on IS outsourcing, finding that banks respond to
institutional pressures, but that response varies depending on
the source of the pressure.
Members of transnational IS teams may have profoundly
different expectations as to what constitutes effective indi-
vidual work habits or appropriate power relations among
individuals (Krishna et al. 2004; Walsham 2002), further
complicating successful execution of business task require-
ments in IS offshoring. Hence, expectation alignment appears
important in enhancing offshoring success. More manageri-
ally oriented accounts of best practices for facilitating off-
shoring success have been provided by Farrell (2004) and
Rottman and Lacity (2006). We observe that implicit in these
discussions of “success factors” for IS outsourcing is the con-
verse outcome of failure. Indeed, given that risk is closely
aligned with the likelihood of negative outcomes (Bahli and
Rivard 2003), the aforementioned success factors could also
be considered IS outsourcing risk mitigation factors since by
fostering success they reduce risk. However, this link
between IS offshore success factors and risk mitigation rarely
comes into sustained focus in the literature (for possible ex-
ceptions, see Carmel and Agarwal 2002; Grover and Teng
1993) and more in-depth examinations of the relationships
between success factors and risk would be a particularly

useful extension of the IS offshoring literature as it begins to
mature.
One of the most commonly used frameworks in the IS out-
sourcing literature is transaction cost theory (Williamson
1985). As pointed out by Qu and Brocklehurst (2003), trans-
action cost theory is typically associated with manufacturing
as opposed to services. However, it has recently been
extended to services by Murray and Kotabe (1999) and Wang
(2002). Transaction cost theory posits that costs can be
divided into production costs and transaction costs. Produc-
tion costs involve the cost of creating a good (or providing a
service) and hence they include labor costs, raw material costs
and capital costs. Transaction costs, by contrast, are the costs
of overseeing, coordinating, enforcing, and managing an
enterprise or undertaking. In the offshoring context, firms
will offshore if the production cost savings due to offshoring
IS functions exceed the additional transaction costs associated
with offshoring. Qu and Brocklehurst find that these addi-
tional transaction costs in IS offshore outsourcing may be
quite substantial and variable, and hence a key source of risk.
Multinational enterprises face a range of economic, financial,
institutional, and political risks as they enter and operate in
various environments around the world. These risks can
include country risks such as macroeconomic and other finan-
cial shocks, as well as political risk (Kobrin 1979). In addi-
tion, some researchers distinguish between overall economic
and political risks and a country’s governance infrastruc-
ture—its political, institutional, and legal environment—as an
important determinant of foreign direct investment (FDI)
(Globerman and Shapiro 2002). Other researchers argue the
corruption constitutes an important and independent source of
risk, especially in the emerging markets context (Uhlenbruck
et al. 2006). Transaction costs associated with particular
investments stem from the political and institutional environ-

ments in which both the government and private investor
operate. Hence, these environments may be viewed as a set of
parameters, changes in which will elicit shifts in the compara-
tive costs of governance (Williamson 1999). Indeed, recent
research has shown country-level risks increase transaction
costs and cause firms to avoid such environments or alter their
entry mode or governance structure (Delios and Henisz 2003;
Henisz and Delios 2001; Henisz and Macher 2004). While
political risk researchers have noted that instability does not
equal political risk (Kobrin 1978) and that not all risks affect
firms in the same way (Oetzel 2005; Robock 1971),2 overall
country risk appears to be an important consideration for
offshoring investment.
Carmel and Nicholson (2005) examined the strategies that
small firms use to overcome the large transaction costs asso-
ciated with offshoring. Using a sample of nine small firms
that had recently offshored, they adopted a semi-structured
qualitative interview methodology, finding that gaining ex-
perience through trial and error and persevering through
multiple offshoring project failures was a common strategy.
In doing so, these small firms were able to gain economies of
experience, which allowed them to subsequently lower trans-
action costs and conduct successful offshoring. Hence, the
amount of firm experience with IS offshoring appears to be an
important factor allowing firms to operate in riskier locations
while still maintaining a reasonable likelihood of success. In
their work with 13 major U.S. firms, Carmel and Agarwal
(2002) found that companies go through four stages of an off-
shoring experience curve in which increasing amounts of
work are transferred offshore. Again, this implies the impor-
tance of firm experience as an influential factor in IS
offshoring.
Imitation and Experience in

International Investment
Research related to the international strategies of multina-
tional firms has placed increased attention on the location of
firms entering foreign markets and the factors that influence
the sequence, proximity, and competitive dynamics between
and among firms in these choices (Chang and Park 2005;
Delios and Henisz 2003; Shaver and Flyer 2000). Literature
on imitative and mimetic behavior in managerial strategy has
focused on how firms learn from their own experiences and/or
those of leading competitors (Caves and Mehra 1986; Gatig-
non and Anderson 1988; Gomes-Casseres 1989, 1990; Hen-
nart 1991; Kogut and Singh 1988; Zejan 1990). The recent
growth in international offshoring of IS services provides an
interesting laboratory to test these competing hypotheses in an
environment characterized by risk and change. Further, while
this industry shares some features with other knowledge-
intensive sectors, it also possesses some unique characteristics
that may be relevant in evaluating firm and industry tolerance
for risk. Finally, the fact that we are likely in a relatively
early stage of international offshoring may be reflected in the
types of evolutionary behavior that are observed.
Firm-Level Experience and International Investment
Research in the internationalization process of firms has long
focused on questions of past experience in international loca-
2
We appreciate the insights of an anonymous reviewer in
pointing this out,
a consideration which we carry forward to our empirical
analysis.

tion and entry mode choice. Johanson and Vahlne (1977)
were perhaps the first to examine the sequential internationali-
zation process that distinguishes specific stages of gradually
increasing foreign involvement that firms follow as they inter-
nationalize. Their model emphasizes incremental inter-
nationalization through acquisition, integration, and use of
knowledge concerning foreign markets. The firm enters new
markets with increasing psychic distance, defined as aspects
of language, culture, business practices, and industrial devel-
opment that tend to reduce the efficiency of information flows
between the market and the firm with the effect that trans-
action costs are increased. Tallman (1992, pp. 462-463)
explicitly discusses the importance of past decision-specific
experience in multinational corporations’ (MNC) organiza-
tional structure decisions, by noting that
The MNE (multinational enterprise) may reduce its
uncertainty in a given situation by attempting to imi-
tate either its own previously successful strategies
and structures or those of its competitors in the new
market.
At the same time, however, greater general international/host
country experience may also enable the firm to deal effec-
tively with the costs and uncertainties of doing business in
riskier markets, and accepting less familiar, more challenging
locations and competitive environments (Padmanabhan and
Cho 1996). The role of learning in MNC internationalization
decisions suggests that an effective organization continuously
develops new knowledge and incorporates that learning into
strategic management decisions (Senge 1990). The ability of

an MNC to learn from experience in foreign markets and then
transfer that knowledge to other markets is consistent with a
range of research streams, especially studies of the organi-
zational management of multibusiness, multinational firms
and their subsidiaries (Prahalad and Doz 1987; Stopford and
Wells 1972).
International expansion by IT firms, especially in a new form
of investment (services offshoring) places demands on firms
that are dissimilar to home country experiences and require
capabilities that are specific to those investment forms. Some
researchers propose that the prior experience provides the
bases for accumulations of the necessary skills to facilitate
entry and operation in a given country (Barkema et al. 1996).
Greater experience in a given or similar country, or with a
specific type of investments, gives investors an opportunity to
overcome so-called liabilities of foreignness—the social, eco-
nomic, political and cultural challenges that present particular
problems for foreign firms (Zaheer 1995). Experience devel-
oped through a sequence of investments that is specific to a
host country can provide the requisite background and
increase success in ever more challenging markets (Chang
1995; Chang and Rosenzweig 2001; Henisz and Delios 2001;
Kogut 1988).
Regarding the question of whether firms are likely to continue
investing in their first or early investment locations or use that
learning to take on increasing risk (and opportunities), David-
son (1980) found that while companies are likely to invest in
countries in which they already have a presence, firms with
experience tend to give investment priority to countries that
have a relatively high level of uncertainty. Delios and Henisz
(2003) found that companies that had international experience
were less sensitive to uncertain policy environments on
investment. From a more practitioner perspective, the most

recent Duke/Archstone (2005) consulting survey found that
companies lacking in offshoring experience perceive higher
risks in offshoring than do companies with such experience.
Again, these observations are consistent with a transaction
cost perspective.
IS offshoring has inherently greater levels of risk than do
similar activities in the domestic setting (Aspray et al. 2006;
Dhar and Balakrishnan 2006; Erber and Sayed-Ahmed 2005;
Farrell 2004; King 2005; Kliem 2004; Tafti 2005). As such,
offshoring projects (whether in risky countries or in safe
countries) are undertaken with substantially elevated levels of
risk in comparison to home country investments. Hence, prior
experience with such projects constitutes a reflection of
experience with this form of risk, and this experience in turn
may serve to partially mitigate the risk of subsequent off-
shoring activities. This assumption is consistent with the
findings of the Duke/Archstone research, which notes that
“companies with no offshoring experience perceive higher
risks than companies with experience” (p. 9). Given the rela-
tive nascent stage and the relatively underdeveloped knowl-
edge base regarding IS offshoring investment, we expect IS
offshoring firms with greater experience to be open to subse-
quent investments in riskier environments. In particular, we
expect that firm-specific experience would help lower trans-
action costs such as switching costs and contract negotiation
costs, as well as lessen the likelihood of asset/intellectual
property expropriation by governments, mitigate vendor
opportunism, and reduce the likelihood of security breach,
risks identified in the IS offshoring literature (Dhar and
Balakrishnan 2006).
H1: As firm-specific experience with risky IS off-
shoring investments increases, firms will
assume increasing levels of host country risk in
subsequent offshoring projects.

Home Country Risk Level
Another mechanism by which firms might develop higher risk
tolerance for foreign investment is through their experiences
in their home market. For example, firms based in countries
that feature risk profiles closer to a potential country destina-
tion for investment may be more open to investments in that
country than would be their counterparts in countries with a
risk profile that is quite unlike that of the destination country.
The logic for this expectation is quite similar to that of foreign
country experience described above (e.g., experience and
learning under similar conditions can help offset the chal-
lenges and transaction costs associated with investment in
unfamiliar and risky environments) and to research on the
influence of cultural distance on international investment
location choice and form.
Several researchers have examined how psychic distance
affects international investment decisions and their studies
have found that locating in countries that are psychically close
reduces the levels of uncertainty and the perceptual barriers
that result (Johanson and Vahlne 1992). Kogut and Singh
(1988) were the first to evaluate psychic distance and inter-
nationalization, using a cultural distance index that provided
a quantitative measure that has since been used to predict the
location and mode choice of foreign investment. Kogut and
Singh found that the greater the cultural distance between the
home and host country, the more likely a firm would choose
a joint venture entry mode to reduce risk and uncertainty and

to gain knowledge about the market. Gatignon and Anderson
(1988) echoed this finding, arguing that sociocultural distance
causes uncertainty for firms, and concluding that cultural
distance was the most important variable affecting location
and control of foreign ventures.
The existence of similarities in culture between a host country
and home country provides many benefits to a firm. In a
more similar culture, firms will likely be able to reduce trans-
action costs that might occur from training and acquiring
information. Moreover, although the point of production of
services could be physically located far from consumers, it is
incumbent on the service providers that they should make
consumers feel that the services originate close to home. We
draw an analogy between host/home cultural distance and
distance in the degree of country-level risk. Indeed, Kogut
and Singh characterized psychic distance as the degree to
which firms are uncertain about a foreign market, a term that
explicitly conjures up risk and lack of predictability. Hence,
we believe the risk gap between home and host country will
be an important contributor to the location choice of offshore
IS investment.
H2: The greater the risk of the country in which a
firm is headquartered, the greater the level of
host country risk that will be associated with
the foreign location of offshoring investment
projects undertaken by that firm.
Competitive Environment: Firm Imitation,
Mimicry, and the Oligopolistic Reaction
Knickerbocker (1973) introduced the concept of oligopolistic
reaction to explain patterns in foreign direct investments
(FDI). He explained that firms (followers) are likely to
match the foreign investment moves of rivals (leaders) by

investing in the same countries. Although Knickerbocker
suggested that these patterns were more likely to emerge in
concentrated industries, many researchers have confirmed this
imitative pattern in a range of industry settings and environ-
ments, showing that organizational actions such as inter-
national entry moves by industry actors often exhibit macro-
level clustering (Gimeno et al. 2005; Henisz and Macher
2004; Nachum and Zaheer 2005; Schelling 1978).
Among the competitive dynamics that urge firms to follow the
overall moves of competitors, some researchers suggest that
follower firms react out of concern that early entrants could
gain “first-mover” type advantages from the additional infor-
mation in a market (Knickerbocker 1973; Lieberman and
Montgomery 1988). Other researchers have suggested this
calculus is less objective and is more likely characterized as
herd behavior (Abrahamson and Rosenkopf 1993). Head et
al. (2002) showed that Knickerbocker’s prediction relies on
risk aversion and Carmel and Agarwal (2002) also docu-
mented an imitative bandwagon effect in IS offshoring (see
also Lacity and Hirschheim 1993) as well as the need to be
sustainable in competitive global markets.
Henisz and Delios (2001) found that the previous number of
entries by firms in the same industry had an impact on the
probability of locating a plant in a given country. In their
analysis of the motivations for international investment
among knowledge-intensive industries, Nachum and Zaheer
(2005) hypothesized that competitive pressure, as measured
by imitative and mimetic investment patterns, would be a
stronger driver of FDI in the information-intensive world (in
both its linear and quadratic forms) than in noninformation-
intensive industries, although this was not supported. They
conjectured that this finding may have resulted from the open
systems that characterize many information-intensive indus-
tries, creating a more cooperative dynamic.

In addition to the competitive dynamics described above,
another set of related forces that may draw firms to colocate
in a country or region are agglomeration economies: the posi-
tive externalities that benefit firms that locate in close
geographic proximity. For resource-seeking investors who
are reliant upon information and human capital, externalities
associated with colocation in a specific geography may be a
powerful draw. Shaver and Flyer (2000) and Chung and Song
(2004) found that Japanese firms located their manufacturing
facilities in states where many other Japanese firms were
located and suggested that externalities might explain this
pattern of agglomeration. Similarly, in their study of firms
entering China, Chang and Park (2005) conclude that Korean
firms are more inclined to invest in areas where other Korean
firms have located. As Shaver and Flyer point out, there are
at least two related sources of positive externalities associated
with colocation: information spillovers that help firms “keep
tabs” on industry developments and competitive dynamics,
and human resource externalities that create larger demand for
qualified workers to enter the labor force and upgrade their
skills to qualify them for positions in industry. Both of these
factors and the associated reduction in transaction costs can
be used to explain the emergence of industry clusters in
various regions of the world, including potentially clusters of
IS offshore facilities in places like Bangalore, India.
The Duke/Archstone offshoring study (2005) found that com-
petitive pressure was cited as an offshoring driver by 71 per-
cent of executives surveyed. In sum, we expect that the com-

petitive environment, as represented by the move by all
participants toward country locations with greater risk, will
create a climate in which firms feel pressure to pursue those
same riskier locations. We believe this is especially likely in
newly internationalizing and emerging industries. In addition
to competitive dynamics, firm clustering in a given industry
likely mitigates some of the risks posed to assets by creating
a large and growing constituency that can leverage its influ-
ence through creation of trade associations, such as the
National Association of Software and Service Companies in
India, a trade association that represents foreign (and
domestic) firms, many of which are involved in IS offshoring.
Further, as the presence of an industry becomes more signi-
ficant, multiple companies from a given home country can
call upon the governmental representatives of that country to
pressure host governments to protect the increasing presence
of their IS offshore firms.
H3: The greater the host country risk assumed by
the IS services offshoring environment overall,
the greater the level of host country risk firms
will accept in subsequent offshoring project
locations.
Data, Research Methods,
and Findings
Data
We use three main sources of data for our empirical analysis.
The first is drawn from a database of over 36,000 worldwide,
foreign, direct-investment projects called the LOCOmonitor
database as developed by OCO Consulting. The FDI project
information in the LOCOmonitor database is derived from
nearly 9,000 media sources using daily data collection via
search strings. From this database, we extracted IT and soft-

ware FDI projects for the years 2000 (the beginning of the
data) to 2005 (the last full year of data). For our analysis, it
was important to consider only services-based projects and
thus to exclude FDI projects involving manufacturing and
other non-services-based activities. Hence, we utilized pro-
jects involving the four major types or sectors of services
offshoring as identified by UNCTAD (2004). These were
customer support centers (e.g., help desks, customer technical
support, information services, and customer relationship
management), IS services centers (e.g., software development,
software design, and applications testing), shared services
centers (e.g., data processing, transaction processing, and
claims and payroll processing), and regional headquarters
(e.g., regional IS management coordination centers for acti-
vities such as enterprise application software, information
management software, Web software infrastructure and data
management software). There were 881 such projects world-
wide in the period 2000–2005.3 Descriptive statistics for the
variables used in the models to be described appear in
Table 1. Figure 3 shows the distribution4 of the top 10 home
countries and the top 15 host countries in our sample
(including the United States). It is interesting to compare our
distribution of host countries with that reported by Carmel and
Agarwal (2002, p. 75). They had identified that more than
3
The host countries for offshoring for which we had complete
data were as
follows: Argentina, Australia, Austria, Belgium, Brazil,
Canada, China,
Colombia, Costa Rica, Czech Republic, Denmark, Dominican
Republic,
Egypt, Estonia, Finland, France, Germany, Honduras, Hong
Kong, Hungary,
India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Latvia,

Lebanon,
Luxembourg, Malaysia, Mexico, Morocco, Netherlands, New
Zealand,
Oman, Pakistan, Philippines, Poland, Portugal, Romania,
Russia, Singapore,
South Africa, South Korea, Spain, Sweden, Switzerland,
Thailand, Turkey,
United Arab Emirates, United Kingdom, Uruguay, United
States, and
Venezuela. The home countries were Australia, Belgium,
Canada, China,
Colombia, Denmark, Egypt, Finland, France, Germany, Hong
Kong, India,
Ireland, Israel, Italy, Japan, Netherlands, New Zealand,
Norway, Philippines,
Russia, Singapore, South Africa, South Korea, Spain, Sweden,
Switzerland,
United Arab Emirates, United Kingdom, and United States.
4
The pie charts in Figure 3 can be read clockwise alphabetically
beginning
with Australia at the 12 o-clock position.
Australia
Canada
France

Germany
India
Ireland
Israel
Japan
UK
USA
Other
Australia
Canada
France
Germany
India
Ireland
Israel
Japan
UK
USA

Other
Australia
Canada
China
France
Germany
Hong Kong
India
Ireland
Malaysia
Netherlands
Singapore
Spain
UAE
UK
USA
Other
Australia

Canada
China
France
Germany
Hong Kong
India
Ireland
Malaysia
Netherlands
Singapore
Spain
UAE
UK
USA
Other
Table 1. Summary Statistics and Pearson Correlations
Mean
(S.D.)

Host
Risk
Cumul
Proj
Home
GDP
Home
Risk Agg
Home
Pol St
Home
Gov Eff
Home
Corrup Year RRA
CustSvc
Center
Shared
SvcCtr

HostRisk
80.573
(6.712)
--
CumulProj
0.000
(3.352)
-0.148 --
HomeGDP
0.000
(8.006)
0.001 0.114 --
HomeRisk
Aggregate
0.000
(4.260)
0.141 -0.161 0.188 --
HomePol
Stability
0.000
(0.552)
0.164 -0.166 0.181 0.898 --

HomeGovt
Effective
0.000
(0.366)
0.047 -0.029 0.602 0.543 0.555 --
HomeCor-
ruption
0.000
(0.452)
0.051 -0.020 0.565 0.565 0.559 0.953 --
Year
-0.364
(1.741)
-0.269 0.284 0.042 -0.470 -0.597 -0.174 -0.139 --
RRA
0.000
(1.292)
0.271 -0.275 -0.028 0.469 0.594 0.163 0.129 -0.976 --
CustSvc
Center
0.000

(0.350)
-0.172 0.076 0.028 -0.043 -0.022 0.058 0.062 0.005 -0.006 --
SharedSvc
Center
0.000
(0.281)
-0.210 0.137 0.013 -0.073 -0.094 -0.014 -0.010 0.064 -0.061 -
0.126 --
TechSupport
Center
0.000
(0.331)
-0.197 0.270 0.048 -0.112 -0.118 -0.062 -0.051 0.309 -0.296 -
0.154 -0.116
Absolute correlations > 0.068 are significant at the 0.05 level
assuming i.i.d. sampling.
Projects by Home Country Projects by Host Country: Top 15 +
United States
Figure 3. Distribution of Projects by Home Country and Host
Country

95 percent of U.S. offshore IT sourcing activity is taking
place in the following nations: Australia, Brazil, Canada,
China, “EU nations (most),”5 India, Israel, Japan, Mexico,
Philippines, Russia, and Singapore. Hence, there is con-
siderable concordance between the distribution of offshoring
locations in our sample and theirs, especially considering our
top 15 host countries plus the United States does not account
for 95 percent of the total global sourcing activity in the data.
Our second source of data is drawn from the International
Country Risk Guide (ICRG) country risk ratings maintained
by the PRS Group. We obtained yearly composite risk rating
data on 62 countries for the period 2000–2005. The com-
posite risk ranking for country m is formed by PRS through a
weighted average of country m’s risk ratings on political risk
(weight = 50 percent), financial risk (weight = 25 percent),
and economic risk (weight = 25 percent). Components of a
country’s political risk rating include the country’s govern-
mental stability, investment profile, corruption, and demo-
cratic accountability. Financial risk rating components
include the country’s exchange rate stability and foreign debt
as a percentage of GDP, while economic risk rating com-
ponents include the country’s annual inflation rate, real GDP
growth rate, and budget balance as a percentage of GDP. We
comment here that the risk ratings are defined on an index
basis that ranges from 0 to 100, with 0 being the most risky
and 100 being the least risky. It will be worthwhile to remem-
ber the definition of this scale in the subsequent discussion of
the results as a high value on the scale indicates low risk and
vice versa. The ICRG is a definitive source of country risk
ratings, and is used extensively in scholarly and practitioner
research across multiple disciplines, including strategy, eco-
nomics, international business, and finance (e.g., Buch et al.

2006; La Porta et al. 1997; Oxley and Yueng 2001;
Uhlenbruck et al. 2006).
Since research has suggested that different types of risk may
affect investment differently (Oetzel 2005; Robock 1971),
that political instability does not necessarily equate to risk
(Kobrin 1978), and that features of host country institutional
governance—such as corruption and governmental effective-
ness (Globerman and Shapiro 2002; Uhlenbruck et al. Eden
2006)—can also have important effects on investment, we
wanted to ensure that results obtained via our overall risk
measure would be consistent with more specific measures of
risk. We were also interested in whether more specific mea-
sures of risk in the home country would influence the patterns
of project risk level at the host country level. Hence, our third
data source was drawn from the Worldwide Governance
Indicators (WGI) compiled by the World Bank. The WGI
contains aggregate indicators of six dimensions of govern-
ance. The indicators are constructed using an unobserved
components methodology that relies on 31 sources, including
surveys of enterprises and citizens, and expert polls, gathered
from 25 different organizations around the world. These pro-
vide data derived from hundreds of questions on governance.
Each question is mapped to one of the six dimensions of
governance before the aggregation is carried out. The six
governance indicators are measured in units ranging from
about -2.5 to 2.5, with higher values corresponding to better
governance outcomes. Details on the concepts measured by
each indicator, its components, and the interpretation of the
point estimates and standard errors can be found in the many
papers that have been used to evaluate governance (see
Kaufmann et al. 2006). The six indicators included in the
WGI are voice and accountability, political stability and
absence of violence, government effectiveness, regulatory
quality, rule of law, and control of corruption. We selected

three of these—political stability, government effectiveness,
and control of corruption—based on literature that has distin-
guished between these measures of stability and institutional
governance and also research that suggests they would be less
highly correlated to overall risk, and could independently
capture specific risk types or transaction costs firms face.
Our overall data set was created by merging the data sets
described above. Composite risk rating data was unavailable
for a few small countries (Antigua, Iceland, Mauritius,
Slovakia, and Ukraine). Hence, it was necessary to drop the
13 projects associated with these countries. There was also
one geographic entity, the territory of Puerto Rico, that is
sometimes classified as a separate entity and other times
treated as part of the United States. Because of the multiple
classification issue and the fact that there were no composite
risk ratings for Puerto Rico, we dropped the three projects in
Puerto Rico. In accounting for host country GDP (see control
variables below), sporadic incomplete data as well as data
unavailability for Taiwan caused 10 more observations to be
unaccounted for. The final sample thus consisted of P = 855
projects.
Dependent Variable and Explanatory Variables
For a given project, the dependent variable consisted of the
host country’s aggregate risk rating.6 Our first explanatory
independent variable of interest was the experience level of a
5
Given the 2002 publication date, this would mean the pre-
enlargement
European Union.
6

We also estimated an additional three sets of models in which
the three more
specific variables from the WGI were respectively used as the
dependent
variable. In sum, no substantive differences were found with
these alter-
native measures. These results are available from the authors
upon request.
=
=
1-
11-
1 k
t
tk yRiskHostCountr
k
RRA
particular firm as operationalized by the number of offshore
FDI projects undertaken prior to the current one. For example,
for a firm’s first FDI project, the independent variable takes
on the value zero; for a firm’s second project, the independent

variable takes on the value one, and so on. Numerous firms
had only one FDI project during the time period. From our
sample of N = 624 unique firms, 98 of them had two or more
FDI projects during 2000–2005. Our second explanatory
variable of interest is the risk ranking of the home country.
This continuous variable was derived from the same source
(PRS) as was our dependent variable (except when we respe-
cified our model, substituting the more specific risk mea-
sures). Our third explanatory variable was designed to cap-
ture the hypothesized driver of firm imitative activities—
namely, the overall appetite for risk in the IS services environ-
ment. To capture this characteristic of the broader trans-firm
direction regarding risk, we calculated the running risk
average (RRA) of host country entries over time in the sample.
Specifically, we first sorted the projects by their calendar date
of occurrence from oldest to most recent. In considering the
kth project, let t index the projects from the first (oldest)
project to the current project k. Then, we calculated
so that the value of RRA for project k is equal to the average
of the host-country risk ratings of the previously conducted
projects up to and including that of project k – 1. This
measure is shown in Figure 4 with the calendar date of the
project on the x-axis. We see, for example, that the average
of the host country risk ratings of all IS offshoring projects
occurring before January 2001 was about 83.9.7 Here, RRA
is undefined for the first project and this project was dropped.
Control Variables
We also include year as a control variable. Based on our
discussion above, it is possible that there is an overarching
temporal trend such that over time firms are increasingly
likely to locate in riskier countries. However, time in isola-
tion is more a control variable in the statistical sense as it is
firm decisions and market activities, not time per se, that

drives entries into particular countries or other arenas such as
new markets or new products. Again, this perspective is
consistent with the Duke/Archstone Offshoring Study (2005)
in which “competitive pressure” (i.e., market activities) was
cited as an offshoring driver by 71 percent of executives
surveyed. Hence, we expect our RRA variable to be related to
year but perhaps a more accurate predictor of IS offshoring
risk outcomes.
Although we would also be interested in various firm-level
characteristics such as sales volume, market capitalization, or
number of employees (e.g., Loh and Venkatraman 1992),
identifying information was unavailable for the firms in our
database; hence, we had no way of including such variables
and consider these as areas for future research. However, we
did examine controlling for other relevant firm-level factors
such as firm-specific experience with country risk.8 Addi-
tionally, at the project level, we controlled for the afore-
mentioned sectors, and at the country level, we controlled for
home country GDP per capita, home country political sta-
bility, government effectiveness, and corruption.
Research Methods
Conceptually, our models center on the drivers of the risk
firms are prepared to tolerate when locating offshore projects.
As such, we focus on identifying those “push” factors that
contribute to these outcomes (see UNCTAD 2006); this
orientation is distinct from research on “pull” factors such as
country-level wages and education (see Bunyaratavej et al.
2007; UNCTAD 2006) and a distinct aspect of this particular
research. Additionally, our approach enables us to assess the
determinants of the level or degree of risk assumed; some-
thing that can at most be only more indirectly assessed with
count-based pull methodologies.

Our data set consists of P = 855 projects associated with N =
624 firms. Hence, for firms that have more than one project,
we have longitudinal data on a firm’s IS offshoring project
history. Clearly, however, this is unbalanced longitudinal
data in that a firm may have numerous IS offshoring projects
(up to 28 in our sample) but each firm’s quantity of offshoring
projects varies from firm to firm. Random effects models are
panel data models (see Greene 1997) that are especially flex-
7
To ensure that our findings were robust across different levels
of country
risk, in addition to the alternate models described above, we
divided our
sample at the median level of country risk and generated
variables for the
cumulative number of projects undertaken in risky countries and
those
undertaken in safe countries and reran the model with the
cumulative risk
variable derived from the “risky” versus “safe” sample,
respectively. Our
findings were substantively identical to the overall model.
8
We created a country-specific experience variable that took on
the value 1
if the firm had previously located a project in the particular
country, and zero
otherwise. This control variable did not have a significant
relationship with
the dependent variable, nor did its utilization impact
conclusions regarding

our hypotheses. Hence, these results are omitted.
80
81
82
83
84
85
86
Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05
Time
(h
ig
h
e
r
ri
sk

)
R
R
A
(l
o
w
e
r
ri
s
k
)
80
81
82
83
84
85
86
Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05

Time
(h
ig
h
e
r
ri
sk
)
R
R
A
(l
o
w
e
r
ri
s
k
)
Figure 4. IT Services FDI Project Running (Overall) Risk
Average: 2000–2005
ible with regard to unbalanced data (Laird and Ware 1982;

Mikulich et al. 1999). Here we use the random intercepts
model (see Pinheiro and Bates 2000). This model can be
written in matrix notation as follows:
yi = Xi β + bi + gi, (1)
gi ~ Normal(0, σ ), (2)
bi ~ Normal(0, τ). (3)
We see from (1) and (2) that the model follows the linear
regression form with coefficients of β and the residuals being
normally distributed with mean zero and standard deviation
σ. However, we also include random intercepts where each
firm in our sample (firm i where i = 1, …, N) has its own
intercept, bi. These intercepts share a common normal distri-
bution with mean of zero and standard deviation of τ as is
seen in (3). Note that yi is a vector of length Pi where Pi is the
total number of projects for firm i. Hence we may write the
elements of yi as yij where j = 1, …, Pi. This similarly applies
to Xi and gi. An additional aspect of the random effects model
is that we can examine whether any unobserved firm-specific
effects that are constant across a firm’s projects (e.g., manage-
ment structure, CEO style, or market position) may have
altered the findings obtained. Phrased differently, a Hausman
test can reveal whether there is an omitted variables problem,
or conversely whether we have estimated β consistently in the
face of omitted variables. The results for Model 1 through
Model 5 appear in Table 2.
Model 1
In Model 1, we examined Hypothesis 1 by including the firm
experience variable while controlling for home country dif-
ferences in GDP and home country risk level. The particular
specification that follows from Equations (1) through (3) was
as follows:

HostRiskij = β0 + β1CumulProjij + β2HomeGDPij +
β3HomeRiskij + bi + gij,
bi ~ Normal(0, τ).
Here, each project constitutes an observation. HostRiskij, the
dependent variable, is the degree of aggregate risk in the host
country of the jth project of firm i. CumulProjij is the corre-
sponding cumulative number of projects previously completed
by the firm at the time of the jth project, HomeGDPij is the
corresponding GDP per capita of the home country of the
offshoring firm, and HomeRiskij is the risk rating of the host
country for the jth project of firm i. We centered independent
variables around the mean as is customary in hierarchical
modeling (Raudenbush and Bryk 2002). In Model 1, we find
that as firm IS offshoring project experience increases, firms
increasingly seek out more risky host countries (β = -0.304, p
= 0.0009) after controlling for differences in home country
risk levels and home country GDP. Here the negative slope
indicates that as the number of projects completed increases,
firms seek host countries with lower risk ratings that corre-
Table 2. Parameter Estimates and Regression Results
Model 1 Model 2 Model 3 Model 4 Model 5
Intercept 80.581 80.579 80.266 80.608 80.527
CumulProj
-0.304
(0.0009)

-0.298
(0.001)
-0.162
(0.0692)
-0.169
(0.0551)
0.049
(0.5618)
HomeGDP
-0.001
(0.9624)
0.027
(0.4705)
0.024
(0.4213)
0.021
(0.4847)
0.033
(0.2253)
HomeRisk
(Aggregate)
0.182
(0.0012)
HomePol

Stability
2.087
(< 0.0001)
-0.134
(0.8006)
-0.117
(0.8229)
-0.394
(0.4225)
HomeGovt
Effective
-1.534
(0.4781)
Home Cor-
ruption
0.129
(0.9389)
Year
-0.958
(< 0.0001)
RRA
1.294
(< 0.0001)
1.103

(< 0.0001)
CustSvc
Center
-4.779
(< 0.0001)
SharedSvc
Center
-5.844
(< 0.0001)
TechSupport
Center
-5.287
(< 0.0001)
RRA*Cust
SvcCenter
1.500
(0.0029)
RRA*Shared
SvcCenter
2.034
(0.0016)
RRA*Tech
SupportCtr

-0.634
(0.4083)
τ 2.100 2.038 1.787 1.732 1.373
σ 6.258 6.259 6.206 6.217 5.857
AIC 5669.0 5657.8 5632.7 5631.2 5513.5
Note: p values in parentheses.
spond to host countries with greater risk. Hence, we find sup-
port for H1. We also find that home country risk level ceteris
paribus is positively related to the dependent variable (β =
0.182, p = 0.0012), supporting H2. Here the positive slope
indicates that firms from lower risk countries (higher risk
ratings) seek out host countries with lower levels of risk
(higher risk ratings) after controlling for differences in firm IS
offshoring experience and home country GDP. Hence, Model
1 suggests that, internationally, IT firms seek relative risk
similarities in that firms in low-risk countries tend to seek
somewhat lesser-risk hosts.
Standard results for random effects models show that the
intra-firm R2 of firms’ project risk is ρ = τ 2/(τ 2 + σ 2). Here
ρ
is 0.10, suggesting it would be erroneous to use OLS
regression and treat a firm’s projects as independent. We can
confirm this through a likelihood ratio (LR) test for the
random effects component. This test rejected the null
hypothesis that τ = 0 (LR = 6.57, p = 0.0052) and as this test
similarly rejected the null for all remaining models, we do not
report on it further. A nonsignificant Hausman test (W = 0.69,
p = 0.8754) supported the random effects formulation and
indicated that the model did not suffer from inconsistency due

to omitted variables.9 We also note the significant result from
the LR test indicates that specification tests designed for the
OLS regression scenario are not valid for this model.
Model 2
In Model 2, we provide a more fine-grained analysis of the
impact of home country risk by replacing our aggregate risk
measure, HomeRisk, with three more specific measures of risk
described previously. As such our model was as follows:
β4HomePoliticalStabilityij + β5HomeGovtEffij +
β6HomeCorruptionij + bi + gij.
The random effects, bi, are again taken to be normally
distributed with mean zero and standard deviation τ (since this
specification does not change throughout, we omit it for
brevity). Model 2 indicates an important driver of firm risk
considerations is home country political stability (β = 2.087,
p < 0.0001) ceteris paribus. This finding supports H2 in a
more specific way by identifying a key driver for H2. We also
find that H1 is again supported even with the more fine-
grained characterization of risk (β = -0.298, p = 0.001). We
compare the model fit of Models 1 and 2 by using the Akaike
Information Criterion (AIC) of the respective models. AIC is
a model comparison measure that favors models that both fit
well and are parsimonious. We see that the AIC of Model 1
is 5669.0 while that of Model 2 is 5657.8. Model 2’s lower
AIC indicates it is preferred over Model 1. Here, using three

specific risk measures10 instead of the aggregate measure
leads to a notable fit improvement, with the utility of home
country political stability being particularly relevant. We
therefore utilized this latter variable instead of the aggregate
measure in subsequent models.11 The latter two risk com-
ponents have contradictory signs which may render the con-
clusions less satisfying. However, a partial explanation for
this may well be the extreme collinearity (r = 0.953) exhibited
by these two components in excess of the |r| > 0.9 collinearity
threshold of Hair et al. (1995).
Model 3
In Model 3, we include the temporal control variable using
the following specification:
β4HomePoliticalStabilityij + β7Yearij + bi + gij.
Model 3 includes the year (2000–2005) in which the jth
project of the ith firm was undertaken. We subtracted the value
of 2003 from the year variable in order to approximately
mean-center the variable. In Model 3, we find that, after
controlling for home country’s risk level, home country GDP,
and year, there continues to be a relationship between greater
firm IS offshoring project experience and firms seeking out
more risky host countries ceteris paribus. However, this
relationship is only marginally significant in Model 3 (β =
-0.162, p = 0.0692), so we have marginal support for H1. By
contrast, we find that home country risk level (as measured by
our more specific home political stability risk measure) is no
longer significantly related to host country risk level when
year is entered into the model (β = -0.134, n.s.), contrary to
H2. Finally, we find that there is a negative relationship
between year and project host country risk level and that this

relationship is significant (β = -0.958, p < 0.0001). This
negative relationship indicates that as the years pass from
2000 to 2005, firms increasingly seek out riskier host coun-
tries for their projects. Our control for home country GDP is
again nonsignificant. According to AIC, Model 3 explains
the data better than the previous models, suggesting the
relevance of temporal factors and/or environment evolution
effects in IS offshoring project decisions. The question
remains, however, as to whether this is merely temporal drift
or whether it is evolution in the competitive environment
resulting from firm competitive/imitative behavior such as
that predicted by the oligopolistic reaction theory. In Model
4, we examine these two competing explanations for shifts in
IS offshoring patterns and compare them to each other.
Model 4
In Model 4, we include the RRA measure using the following
specification:
β4HomePoliticalStabilityij + β8RRAij + bi + gij.
Model 4 incorporates the running risk average variable as
described previously. We centered RRA around its mean for
interpretational purposes. In Model 4, we find that ceteris
paribus there continues to be a marginally significant relation-
9
Hausman tests for all models were nonsignificant and are hence
not reported
further.
10
As suggested by an anonymous reviewer.

11
As noted above, we additionally conducted three sets of
analyses in which
each one of the three specific risk measures was individually
used as the
dependent variable in the place of the aggregate risk variable,
HostRiskij. The
conclusions reached were substantively identical to those
described here;
hence, these analyses are omitted.
ship between firm IS offshoring project experience and firms
seeking out more risky host countries (β = -0.169, p =
0.0551). Hence we again have marginal support for H1. We
again find that contrary to H2 our specific measure of home
country risk level is no longer significantly related to host
country risk level ceteris paribus (β = -0.117, n.s.). The rela-
tionship between risk and RRA is significant (β = 1.294, p <
0.0001). The positive relationship between RRA and the
dependent variable indicates that as the broader competitive
environment assimilates experience with higher risk countries,
firms increasingly seek out higher risk IS offshoring destina-
tions, further accelerating the trend in IS services offshoring.
We also note that the AIC for Model 4 indicates that it pro-
vides a better fit than Model 3. This provides evidence for the
inclusion of RRA as opposed to Year.
Model 5

In Model 5, we explore the possibility of differences attrib-
utable to projects’ association with one of four IS services
sectors discussed previously: customer services centers,
shared services centers, IT technical support centers, and
regional headquarters. The headquarters sector was used as
the reference group and the projects in the remaining sectors
were dummy-coded with a “1” on the respective dummy
variable if the project was of the respective type. We also
explore the possible interaction between sector-level effects
and the competitive environment risk construct, RRA. Com-
petitive environment trends toward greater risk may accelerate
firm decisions to locate offshoring projects in riskier countries
for certain sectors more than others. For example, some
sectors may be extremely competitive such that broader trends
toward risk become highly magnified, while other sectors may
be relatively less affected by such trends. Accordingly,
Model 5 was specified as follows:
HostRiskij = β0 + β1HomeGDPij + β2HomeGDPij +
β3HomePoliticalStabilityij + β6RRAij +
β7CustSvcCenterij + β8SharedSvcCenterij +
β9TechSupportCenterij +
β10RRAijCustSvcCenterij +
β11RRAijSharedSvcCenterij +
β12RRAijTechSupportCenterij + bi + gij.
With regard to the central theoretical development of the
paper, H3 remained significant after controlling for possibly
different sector-level reactions to broader risk trends (β =
1.103, p < 0.0001), while H1 (β = -0.049, n.s.) and H2 (β =
-0.394, n.s.) again did not. Moreover, we see that projects in
the sectors of customer services centers (β = -4.779, p <
0.0001), shared services centers (β = -5.844, p < 0.0001), and
IT technical support centers (β = -5.287, p < 0.0001) are all

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