Chen, t., j. zhang and k.k. lai, 2009. an integrated real options evaluating model for information technology projects under multiple risks

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An integrated real options evaluating model for information technology
projects under multiple risks
Article in International Journal of Project Management · November 2009
DOI: 10.1016/j.ijproman.2009.01.001
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2. An integrated real options evaluating model for information
technology projects under multiple risks
Tao Chen a
, Jinlong Zhang b
, Kin-Keung Lai c,*
a
School of Public Administration, Huazhong University of Science and Technology, China
b
Department of Management, Huazhong University of Science and Technology, China
c
Department of Management Science, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong
Received 14 May 2008; received in revised form 14 December 2008; accepted 6 January 2009
Abstract
Information technology (IT) investments are exposed to multiple sources of risks. Past information systems research on real options
evaluation could not deal with a mix of public and private risks sufficiently in the valuation procedures. Moreover, the relationship
between IT investment value and risk factors has rarely been fully explored and remains debatable. In this light, we present an approach
based on real options to evaluate IT investments subject to multiple risks. By modeling public risks and private risks in different ways,
this approach produces some new results that are different from prior researches. It is found that public risk has an upward effect on the
expected payoffs, while private risk influences the options value in a contrary way. The suggested method could help IT managers pro-
duce a well-structured valuation process in IT investment decision-making, and understand the interactions between IT risks and options
value in a clear way. We also illustrate how the proposed procedure is applied to an ERP project in a construction company.
Ó 2009 Elsevier Ltd and IPMA. All rights reserved.
Keywords: Information technology investment; Real options evaluation; Risk analysis
1. Introduction
In today’s business environment, information technol-
ogy (IT) is considered to be a key source of competitive
advantage. With its growing strategic importance, organi-
zational spending on IT applications is rising rapidly, and
has become a dominant part of the capital budgets in many
organizations. Managing IT investment is a challenging
task for most IT managers, because the costs and benefits
have been hard to quantify. Benefits, which are a function
of technology, could change dramatically even during
short-lived IT projects because the underlying technologies
are changing so rapidly. Recently, real options are being
gradually accepted as a modern approach to evaluate
investments characterized by high levels of uncertainty.
Researchers on information systems have also recom-
mended this technique (i.e. real options) to understand
and facilitate IT investment decisions [1]. Real options
analysis (ROA) has been proved suitable for modeling IT
investments involving an option; its strengths have been
illustrated by several researchers [2–4]. However, there still
exist some gaps between ROA and what is needed to effec-
tively evaluate real world IT investments [5,6].
Firstly, the current ROA model can not adequately deal
with multiple risks embedded in IT projects. Past research
on real options typically looks at a subset of risks affecting
IT investments. It mostly looks at financial risk (such as
interest rate uncertainty), market risk (such as price and
demand uncertainty), and cost risk (such as technical and
inputs uncertainty) [14]. Existing real options valuation
models can consider no more than two sources of risk at
a time because the computational complexity increases as
more sources of uncertainty are added [7]. However, IT
investments are often exposed to additional risks, such as
requirement risk, technology risk, and so on. Virtually no
0263-7863/$34.00 Ó 2009 Elsevier Ltd and IPMA. All rights reserved.
doi:10.1016/j.ijproman.2009.01.001
*
Corresponding author.
E-mail address: mskklai@cityu.edu.hk (K.-K. Lai).
Available online at www.sciencedirect.com
International Journal of Project Management 27 (2009) 776–786
www.elsevier.com/locate/ijproman

3. tool is available to address these kinds of uncertainties.
Furthermore, most ROA models describe risks as the var-
iability of the expected benefits of the project. In fact,
obtaining a reliable estimation of the variability is usually
a difficult work [8]. Managements are rarely able to directly
give an adequate estimate of the distribution of the
expected revenues and probable variability. Benaroch and
Kauffman [8] summarized five basic schemes that can be
used to estimate the parameter, but it seems that none of
these schemes could deal, simultaneously, with multiple
risk factors that affect IT investments.
Another main gap is that the inherent relationship
between IT risks and investment value remains a point of
debate. An information technology project is an inherently
uncertain investment. Almost everything – user require-
ments, technology, experience of the team, market – is
changing constantly. Uncertainty in one, or a combination
of these factors, could considerably affect the value of the
project, which is of significant importance for IT invest-
ment decisions. The relationship between option values
and IT projects’ risks has been discussed by Dos Santos
[3], Kumar [9] and Erdogmus [10]. Santos [3] first examined
the pattern of variation of option values for different
parameter values, and then drew the conclusion that the
option value of an investment increased with increase in
uncertainty of project costs or benefits. Contrary to this
conclusion, Kumar [9] illustrated that option values could
either increase or decrease with higher levels of project risk,
depending on the relative values of variances of project
costs and benefits, and the correlation between them.
Unfortunately, this work did not take into account the dis-
tinction between private risk and market risk. Erdogmus
[10] used historical project data to estimate cost and sche-
dule uncertainty, before evaluating commercial software
development projects. However, it is a rather strong
assumption that a right and suitable reference project
exists.
Since typical IT investments could be exposed to more
than two sources of risks, it is necessary to find other ways
to model and evaluate such investments. In this paper, we
look into the valuation of IT investments under uncertain
environments, with a focus on modeling multiple risks of
IT investments and assessing their impact on expected pay-
offs. We try to explore the following questions:
How to evaluate an IT investment, especially when it is
exposed to multiple risks?
How to systematically identify various risk factors?
What are the ultimate influences of the identified risks
on the expected value of an IT investment? Would the
risks undermine or enhance the expected payoffs?
We develop a disciplined project evaluation approach,
which incorporates risk assessment and real options analy-
sis into a unified framework. This method is expected to
evaluate IT investments exposed to multiple risks, and to
explore the interaction between IT value and various risks
in depth, thus facilitate better IT investment decisions. The
rest of this paper is organized as follows. Section 2
describes the categories of different risk factors associated
with IT investment, and gives an overview of real options
analysis and its application in IT investment decisions. In
Section 3, we present an integrated valuation approach,
which includes both risk assessment and real options anal-
ysis, to formulate a model for valuation of IT investments
under multiple risks. Section 4 applies the methodology to
a case of an ERP system development in a construction
company in China. Finally, the strengths and limitations
of this comprehensive approach are discussed.
2. Risk factors associated with IT investments and real
options evaluation
Information technology investments offer several poten-
tial benefits to enterprises, such as reducing transaction
costs, improving production efficiency, and facilitating bet-
ter customer relationship. Unfortunately, however, pro-
ductivity gains from IT investments may be neutral, or
even negative, due to the nature of high levels of risk that
characterize most IT projects [11]. Unsuccessful manage-
ment of IT risks can lead to a variety of problems, such
as cost and schedule overruns, unmet user requirements,
and failure to deliver business value of IT investment.
Risks of IT investments are abundant in terms of variety
too [12,13]. There have already been several lists of risk fac-
tors published in IS literature. There exist two streams of IS
research which consider IT investment risks in different per-
spectives. The first stream is mainly concerned about risks in
software development. For example, Boehm [14] identified a
‘‘Top-10” list of major software development risks that
threaten the success of projects. Barki et al. [15] identified
35 risk variables in software projects and categorized them
into five factors. Building upon this, Wallace [16] conducted
a survey with 507 software project managers and this
resulted in six categories or dimensions of risk: team, organi-
zational environment, requirements, planning and control,
user, and project complexity. These risks can be generally
treated as private risks, which are specific to projects.
The second stream of research views IT investment risks
from a broader perspective. It is not limited to software
development process, but is extended to external factors.
Risks produced by market conditions and competitive
environments are also included by researchers [8,17–20].
These literatures hold the view that even when private risks
have been controlled to low levels, an IT investment project
could still fail to generate the expected payoffs due to an
uncertain environment. For example, the customer may
not accept the end product or services that the finished
IT application yields. The efficacy of the adopted technol-
ogy may change, or a competitor may make a preemptive
move. Such risks are generally produced by factors external
to the project, and are applicable to all investment projects
that have similar features. Thus they are referred to as
public risks. On the basis of prior IS literatures, Table 1
T. Chen et al. / International Journal of Project Management 27 (2009) 776–786 777

4. gives a list of risk areas that threaten the success of IT
investments.
Real options analysis is proved to be a suitable tool to
valuate investment under uncertainties [21–24]. Because
of the high uncertainty character inherent in IT investment,
IS researchers propose to introduce ROA to IT investment
decision-making. IS research on real options is mainly con-
cerned with identification of various options in IT invest-
ments, and then their framing as pricing problems, their
valuation, and interpretation of the results. For example,
Dos Santos [3] applied real options theory to a two stage
IT investment, treating the first stage as an option to speedy
implementation of the second. Benaroch and Kauffman [8]
illustrated the use of real options techniques in the context
of a decision to delay the application of a banking ATM
network. Taudes et al. [25] suggested that the value of IT
investment could be defined as the sum of economic value
and option value. Clemons and Gu [26] viewed a partial
IT investment as an strategy option to preserve flexibility
and to accelerate subsequent choices, and saw completing
the future contingent investments as exercising the strategic
options created by initial investments. Miller et al. [27] and
Dai et al. [28] both used analytical model based on real
options to value IT infrastructures investment.
Different from regular investments, the value of IT
investment depends largely on the embedded real options
[29,30]. For example, investment on infrastructure such
as Intranet may be unattractive in terms of NPV. However,
it provides a good foundation for possible e-commerce
activities in future [31]. These payoffs may not be direct
or immediate, but are the true value of most IT investment
projects. The traditional net present value techniques only
focus on the current predictable cash flows, and, therefore,
may mislead managers in the process of decision-making in
case of IT investments. Real options analysis could identify
the potential options value, and make a more comprehen-
sive evaluation of IT investment proposals. Real options
analysis also provides some new insights with respect to
the role and impact of uncertainty on investment value that
runs counter to conventional thinking. According to real
options theory, an investment is of higher option value in
a more uncertain or volatile markets because of the flexibil-
ity of the investment decision. This point has been formally
shown by Merton [32], and discussed by Dos Santos [3],
Edwards and Ma [33].
With the growing acceptance of ROA as a valuation
technique, several IS researchers have started studying
the interactions between real options’ value and risk factors
in IT investments. For example, Chatterjee and Ramesh
[34] applied ROA to management of risks in adoption of
technological innovations, and presented a risk driven pro-
cess framework for risk management of software projects.
Benaroch [5] developed a four step option-based approach
to managing IT investment risk, which facilitated a more
comprehensive identification of option configurations.
Erdogmus [10] presented a disciplined approach to assess
the economic value of commercial software development
projects that are simultaneously subject to schedule, devel-
opment cost, and market risks.
Real options approaches are generally based on the
assumption of replicating portfolio, which means that a
traded replicating portfolio of financial assets exists for a
corporate investment in real assets. When markets are
complete, this assumption will be reasonable and a traded
replicating portfolio could provide the information of pub-
lic risk. However, most realistic problems are more compli-
cated because many assets are not freely traded and a twin
security may be not available in incomplete markets [35].
Amram and Kulatilaka [7] acknowledge difficulty with
the tracking portfolio concept, and recognize the existence
of private risk that could cause ‘‘tracking error” in real
options pricing. Unfortunately, no detail guidance is
proved on how to deal with private risk and tracking error.
Smith and Bob Nau [36] pioneered the concept of public
and private uncertainties, and they compared options pric-
ing analysis and decision analysis for valuating risky pro-
ject. They showed that option pricing techniques can be
used to value risks that can be hedged by trading existing
securities, conversely, decision analysis techniques can be
used to value risks that cannot be hedged by trading.
3. The proposed valuation approach
This research aims to develop a more general approach
for IT investment valuation under multiple sources of risk
Table 1
Key risk areas associated with IT investments.
Risk category Description
Private risks Organizational risk—the stability of the management;
organizational support for an investment
User risk—lack of user involvement during system
development; unfavorable attitudes of users towards a
new system
Requirement risk—frequently changing requirements;
incorrect, unclear, inadequate, or ambiguous
requirements
Structural risk—the strategic orientation of the
application; a number of departments are to be
involved; the business process needs to be changed
frequently
Team risk—insufficient knowledge or inadequate
experience among team members, frequent team
member turnover
Complexity risk—whether the new technology is used;
the complexity of the processes being automated;
whether a large number of links to existing systems are
required
Public risks Competition risk—strong competitor reactions that
may prevent the firm from obtaining the expected
outcome
Market environment risk—acceptance by customers,
vendors and business partners, of the application;
unanticipated changes in the industry or market; the
application becomes obsolete due to introduction of
new technology
Source: Based on [5,16].
778 T. Chen et al. / International Journal of Project Management 27 (2009) 776–786

5. factors, and to explore further the relationship between IT
risks and real options’ value in IT investment decisions.
The basic assumption underlying this approach is that
the market is partially complete, which means that IT
investments typically involve a mix of public and private
risks. Therefore an accurate valuation depends on address-
ing both kinds of risk. The proposed procedure is divided
into four steps:
Step 1: Define the investment goal and identify its risks
The first step of our approach is to define the investment
goal and requirement, and to identify the critical sources of
risk factors. The goal of IT investment usually can be
defined as to enable the organization to achieve a set of
business capabilities [37], which is a unique attribute of a
business organization that creates value for its customers.
These capabilities, in turn, provide options for a firm to
capture the future possible benefits brought by improved
products or services [38]. Capabilities can be measured by
the value generated for the organization through a series
of cash flows. However, the expected payoffs may fluctuate
due to the influence of uncertainties, which include project
and market related risks. Project-related risk is determined
by how the firm chooses to design, develop and implement
the IT applications. Market-related risk comprises uncer-
tainties that affect market demand for the firm’s products
or services, such as customer acceptance, competitor reac-
tions, and other external factors. The capability-based real
option analysis provides a unique perspective to identify all
kinds of risks during the transformation from IT invest-
ment to the future benefits [38].
Step 2: Assessment of public risk
Financial markets data are valuable sources of informa-
tion to assess this kind of risks [12,39]. If the financial mar-
kets contain certain assets whose values are subject to the
same source of uncertainty, then it is said that the underly-
ing risk is market-priced. Fluctuations in these assets
would provide a reasonable, objective estimate of the target
source of risk involved. It is generally accepted that vari-
ability is just another term for risk; thus it is reasonable
to represent public risk using the volatility of the expected
payoffs, which could be calculated by a reference to finan-
cial assets. It should be noted that, in the assumption of
our approach only the part of public risks, which is with
a complete market and relevant information could be
reflected by a tracking replicating portfolio, could be for-
mulated in this way.
Adopting a reference portfolio is an abstraction from
reality, which could provide some objective market infor-
mation about the target investment [7,40]. However, sup-
posing that such reference assets are hard to find, a
subject prediction is an alternate approach [41,42]. Firstly
we can recognize sources of public risks (competition risk,
uncertain demand, etc.), estimate their impact on cash
flows, and then give a prediction of the volatility of the
expected project payoffs. However, Management should
carefully explore every component that affects the output
variables, and collect sufficient data to justify their assess-
ment, in order to make the estimate of the volatility more
reliable.
Step 3: Assessment of private risk
Private risks, or risks that are project-specific, are com-
mon in technology-based investment, especially in IT
investment. Private risks are constituted by specific internal
factors such as team experience, project complexity, plan-
ning and controlling, and unforeseen technical problems.
Erdogmus [10] employed the concept of earned value man-
agement to estimate private risks, based on historical data
from past projects. However, as pointed out by Erdogmus,
because many technology projects are unique, it is usually
rather difficult to obtain a suitable reference project of
comparable scale, scope, technological complexity, team
skills and other such characteristics.
In order to obtain a comprehensive and structured esti-
mation of private risks, we have adapted a risk checkout
list developed by Wallace [16]. This metric has covered
most of project-specific risk factors mentioned in IS
literature.
Basically, we follow Balasubramanian et al. [38] and
Erdogmus [10], using a failure rate1
of information system
project to measure the private risk. But in order to achieve
a more reliable assessment, we adopt a methodology pre-
sented by Pinto [45], to come up with the overall project
risks, which are measured along the probability and conse-
quence dimensions. Table 2 demonstrates the scores for
evaluating the project risk. The detailed criterion can be
seen in [45].
The scores for each individual dimension of probability
(Pf) and consequence (Cf) are added and the sum is divided
by the number of factors used to assess them:
Pf ¼
X
Pi=i; Cf ¼
X
Ci=i: ð1Þ
The overall failure rate for the project is calculated by using
the formula:
F ¼ Pf þ Cf ðPf ÞðCf Þ ð2Þ
Step 4: Real options valuation
In the last step, we can assess the real option value of the
investment based on the results obtained above. Benaroch
[5] classified option valuation models along two dimen-
sions: whether the investment payoffs and costs are certain
or stochastic, and whether the option involved is simple or
1
IS failure rate is able to to describe the extent to which the IS project
will be a failure. It was ever adopted by Linberg [43], Balasubramanian
et al. [38] and Erdogmus [10], For the definition of IS failure rate and
related discussion, please refer to [44].
T. Chen et al. / International Journal of Project Management 27 (2009) 776–786 779

6. compound. In the case of valuating a simple option, if only
the payoff is uncertain, Black-Scholes and binomial models
can be used; and if the payoff and cost are both uncertain,
an asset-for-asset exchange model should be employed. For
the purpose of simplicity, we assume that only the expected
payoff is uncertain, and utilize the binomial model.
The binomial model assumes that V, the value of an
option’s underlying asset, is governed by a multiplicative
binary random-walk process. Starting from an initial
expected value, V moves either up to uV with probability
q or down to dV with probability 1q, in a fixed interval
Dt, where u 1, d 1, and d 1 + rf u, with rf being
the risk-free interest rate. If the same process is repeated
for multiple periods, then V can be modeled using a bino-
mial tree with merging upward and downward branches
(see Fig. 1).
Let I be the option’s exercise price, then the terminal
value of a call option on V that matures in Dt is
Cu ¼ max½0; uV I or Cd ¼ max½0; dV I ð3Þ
with probabilities q and 1 q, respectively (see Fig. 2).
By using an artificial probability measure, which is set as
p ¼ ð1 þ rf dÞ=ðu dÞ ð4Þ
the value of the call option can be formulated as [46]:
C ¼
pCu þ ð1 pÞCd
1 þ rf
: ð5Þ
When the volatility is r, then u and d can be determined as
u ¼ expðr
ffiffiffi
s
p
Þ; d ¼ 1=u ð6Þ
where s is the chosen interval size expressed in the same
unit as r and exp denotes the exponential function.
Then, a decision tree that embeds all possible investment
configurations could be established. Using a dynamic pro-
gramming technique, the decision tree is rolled back to
determine the real options value underlying the target
investment.
As for the decision nodes of exercising an option, the
expected payoffs can be calculated as
C ¼ max½0; ð1 F ÞC I ð7Þ
where F denoted the project failure rate and 1F was the
probability that the project was developed successfully.
Finally, the value of an investment can be computed as
Expanded NPV ¼ Real Option Value þ NPV ð8Þ
4. Valuating the NCIE ERP project
National Construction and Installation Engineering
company (NCIE)2
is a typical construction company in
China, which has dozens of branches scattered all over
the country. Started in 2005, NCIE planned to develop
an Enterprise Resource Planning (ERP) system to support
integration of various business processes within the
company.
This system was expected to support most daily activi-
ties including purchase, inventory, marketing, equipment
and cost management. According to initial arrangement,
ERP development is divided into two stages (see Fig. 3).
The first stage involves development of five functional
modules: material procurement, facilities management,
cost control, and construction project management. The
development of stage 1 will be ready in one year and cost
about 0.8 million RMB. At the end of stage 1, if the market
outlook at that time is promising, NCIE will proceed to the
next stage. In stage 2, the task is to develop more func-
tional modules, including marketing, technology manage-
ment, quality control, financial management, audit and
security management, as well as to embed some compre-
hensive query and analysis components. The task in stage
2, if undertaken, is expected to take one more year to com-
plete and the cost would be 1.2 million RMB. All the appli-
cations are based on Browser/Server structure, and are
developed through .NET. If the whole ERP system were
successfully implemented, NCIE managers estimate that
it would generate approximately 5 million RMB in addi-
tional revenues due to improved cost analysis capability
and increased response ability to explore bidding opportu-
nities in the market.
Table 2
Scores for probability Pf and consequence Cf.
Pf, Cf Low Minor Moderate Significant Major
Score 0.1 0.2 0.5 0.7 0.9
V
uV
dV
u2
V u3
V
udV
d2
V
u2
dV
ud2
V
d3
V
(q)
(1-q)
Fig. 1. Binary random-walk process for the value of an underlying asset.
V
uV
dV
(1-q)
(q)
C
Cu
= max[0, uV-I]
(1-q)
(q)
Cd
= max[0, dV-I]
Fig. 2. Value of a call option on V that matures in Dt.
2
NCIE is a pseudonym for a real company. The data described in this
article came out of several interviews conducted by one of the authors.
780 T. Chen et al. / International Journal of Project Management 27 (2009) 776–786

7. Step 1: Define the investment goal and identify risks
The managers in NCIE hope that the implementation of
ERP could improve the company’s performance in several
ways:
To collect instantly relevant business data produced in
branches, and reduce the delay in information
dissemination;
To streamline some important daily business processes
across different departments in the head office;
To keep the accuracy of cost accounting, and improve
cost analysis capability, which will provide basis for better
control on the cost of construction projects.
However, there existed many uncertain factors that may
prevent the ERP system from delivering the above benefits.
Some of them came from the changing market environ-
ment, and the others resulted from the company’s internal
factors, and the inherent complexity of the projects.
Step 2: Assessment of public risk
It is estimated that successful implementation of the
ERP system would bring about 5 million RMB in addi-
tional revenues for NCIE. However, this figure was quite
uncertain due to the changing market environment. The
construction industry was significantly influenced by the
nation’s macroeconomic regulations and Chinese business
cycles. In addition, there were, in total, more than 65,000
construction companies in China; competition is rather
fierce. Therefore, it’s necessary to take into account the
influence of market uncertainties on the project value.
Financial markets provide valuable information sources
to assess the market uncertainties. A common solution is to
find a publicly owned firm operating in the same market,
which is assumed to be subject to the same market risks
[8]. We use the stock performance of Shanghai Construc-
tion, a public company with similar businesses, to estimate
the market risks. Fig. 4 plots the monthly performance of
Shanghai Construction over the last three years. The uncer-
tainty of the market payoff can be represented by the vol-
atility of the stock value over the analyzed period
(Erdogmus, 2002). The standard deviation of the monthly
percentage changes in the stock’s price was 9%. When
annualized, this yields a volatility of
ffiffiffiffiffi
12
p
9% ¼ 31%.
Step 3: Assessment of private risk
Private risks are diverse in nature and it is usually hard
to map all possible risk factors. Therefore, a comprehensive
risk list would help to make a relatively reliable and valid
assessment. We adopted the instrument developed by Wal-
lace (2004) and interviewed five key stakeholders of the
project, including the project manager, the user representa-
tive and three development team members. The detailed
risk situation is described as follows:
(1) Requirement risk: According to the interview with
the key developers, the requirements had been chan-
ged several times, which resulted in large modifica-
tions of several modules. It was partly due to
unclear system requirements, and partly due to the
lack of efficient change management.
(2) Complexity risk: The ERP system was developed using
.NET, a new technology that had never been used by
most team members. Therefore, internal training
courses were conducted to help developers adapt to
the new tool. Besides, there were several other software
applications that needed to be integrated into the sys-
tem, which increased the project complexity.
(3) User risk: The lack of user involvement was also a
risk factor in this case. Some users had insufficient
knowledge about the ERP system and were not
enthusiastic to cooperate. What’s more, conflicts
often appeared between users and developers.
(4) Organizational risk: There was a low level of organi-
zational risk because the company’s management was
rather stable. What’s more, this investment was
strongly supported by the top management.
(5) Team risk: When the project proceeded, the develop-
ment team experienced large turnovers of personnel.
At the beginning, there were 30 people conducting
requirement analysis and design. A month later, only
13 developers, who had richer experience, were cho-
sen to undertake the development work. Besides, sev-
eral members left during the process of project
development.
(6) Structural risk: The developers suggested that one of
the most serious risks was the large scope of the tar-
get project. There were so many modules in the sys-
6
3 9 12 18
15 21 24
I stage1=0.8 Million I stage2 =1.2 Million
V= 5 Million
Fig. 3. The investment configuration of ERP system in NCIE.
T. Chen et al. / International Journal of Project Management 27 (2009) 776–786 781

8. tem and the business processes involved quite a num-
ber of departments in the company. Most of our
interviewees felt that such a large project was prone
to getting out of control.
In sum, this ERP project was viewed as associated with
high risks. We use a failure probability to represent the pri-
vate risk of the target project. The five people that we inter-
viewed were required to assess the risk level along six
dimensions: requirements risk (R1), complexity risk (R2),
user risk (R3), organizational risk (R4), team risk (R5)
and structural risk (R6).The probability of project risk
score and the consequence of project risk score are listed
in Tables 3 and 4, respectively. Therefore,
Pf ¼
X
Pi=i ¼ ð0:28 þ 0:2 þ 0:18 þ 0:1 þ 0:42 þ 0:3Þ=6 ¼ 0:26;
Cf ¼
X
Ci=n ¼ ð0:52 þ 0:3 þ 0:22 þ 0:18 þ 0:6 þ 0:32Þ=6 ¼ 0:36
The overall failure rate of the target project is
F ¼ 0:36 þ 0:26 0:36 0:26 ¼ 0:53 ð9Þ
Step 4: Real options valuation
The ERP investment in NCIE could be structured as a
binomial tree, as shown in Fig. 5. The maximum duration of
the project was 24 months, which was divided into 8 intervals
of 3 months each. Discounted by a risk-free rate of 7%, the
present value of expected initial payoff was 5/(1 + 7%)2
=
4.37 million RMB. Beginning with this initial value, the
expected payoff V was assumed to follow a binomially distri-
buted multiplicative diffusion process. At the end of every
period, V either increased by a factor of u or decreased by a
factor of d. The upward and downward factors were chosen
to be consistent with the volatility of the expected investment
payoff. In the current case, the volatility of r = 31% and the
interval size of s = 0.25 years (three months) yielded an
upward factor u = 1.17 and a downward factor d = 0.86.
The decision whether to undertake the second stage
would be made in 12 months, depending on whether the
market outlook was favorable at that time. A decision tree
could be established to determine the real options value
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
Jul-03 Oct-03 Jan-04 Apr-04 Jul-04 Oct-04 Jan-05 Apr-05 Jul-05 Oct-05 Jan-06 Apr-06
Shanghai Construction Inc
Fig. 4. Shanghai Construction’s stock performance (source: cn.finance.yahoo.com).
Table 3
Assessment of the probability of private risks.
Probability Pi Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Evaluator A 0.7 0.5 0.3 0.3 0.7 0.4
Evaluator B 0.5 0.3 0.3 0.2 0.7 0.3
Evaluator C 0.5 0.3 0.1 0.1 0.7 0.3
Evaluator D 0.4 0.1 0.1 0.1 0.4 0.3
Evaluator E 0.5 0.3 0.3 0.2 0.5 0.3
Average 0.52 0.3 0.22 0.18 0.6 0.32
Table 4
Assessment of the consequences of private risks.
Consequence Ci Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6
Evaluator A 0.5 0.3 0.3 0.1 0.5 0.5
Evaluator B 0.5 0.2 0.3 0.1 0.5 0.3
Evaluator C 0.3 0.3 0.1 0.1 0.5 0.3
Evaluator D 0.3 0.1 0.1 0.1 0.3 0.1
Evaluator E 0.3 0.1 0.1 0.1 0.3 0.3
Average 0.38 0.2 0.18 0.1 0.42 0.3
782 T. Chen et al. / International Journal of Project Management 27 (2009) 776–786

9. underlying this investment (see Fig. 6). The value of the
nodes could be rolled back, using the risk-neural valuation
formula, which was of the following form:
C ¼
pCu þ ð1 pÞCd
1 þ rf
ð10Þ
where p = (1 + rfd)/(ud) = 0.69.
As for the decision nodes in the twelfth month, it in-
volved a decision to exercise an option. Because the second
stage would be foregone if the market outlook was nega-
tive, the expected payoffs were nonlinear, and were calcu-
lated as max[0,(1 F)V I2], where 1 F was the
probability that the project was developed successfully.
Note that the node at the last row of the twelfth month
24
0 6
3 9 12 18
15 21
4.37
5.1
3.74
5.95
4.37
3.20
6.95
5.1
3.74
2.74
8.12
5.95
4.37
3.2
2.35
9.48
6.95
5.1
3.74
2.74
2.01
11.07
8.12
5.95
4.37
3.2
2.35
1.72
12.95
9.48
6.95
5.1
3.74
2.74
2.01
1.48
15.09
11.07
8.12
5.95
4.37
3.2
2.35
1.72
1.26
Time
Months
Fig. 5. Multiplicative binary random-walk process of project value.
1.15
1.43
0.79
1.77
1.02
0.47
2.64
1.62
0.65
0.20
Max(2.64, 0)
Max(1.62, 0)
Max(0.86, 0)
Max(0.31,
0)
Max(-0.09, 0)
9.48
6.95
5.1
3.74
2.74
2.01
11.07
8.12
5.95
4.37
3.2
2.35
1.72
12.95
9.48
6.95
5.1
3.74
2.74
2.01
1.48
15.09
11.07
8.12
5.95
4.37
3.2
2.35
1.72
1.26
24
0 6
3 9 12 18
15 21
Time
Months
Fig. 6. ERP investment decision tree considering public and private risks.
T. Chen et al. / International Journal of Project Management 27 (2009) 776–786 783

10. had a negative payoff, and then the value was simply zero
because the following stage was not undertaken.
The root value of the decision tree represented the
option value of the second stage investment. However, it
did not take into account the cost of the first stage develop-
ment. The expanded net present value of this ERP invest-
ment equaled [10]:
Expanded NPV ¼ ðOption Value of Second StageÞ
ðTotal cost of First StageÞ
¼ 0:35 million RMB
5. Sensitivity analysis and discussions
Information technology projects are subject to multiple
sources of risks, which can affect the value of an investment
to varying extents. It is, therefore, necessary to make clear
the effects of interactions between the real options value
and risk factors in IT investment. In this paper, we present
a valuation methodology that is able to handle multiple
risks that are common in most IT investments.
In our methodology, the market risk is assessed using
financial market data, and represented by the volatility of
the expected payoffs. As for the private risk, it is estimated
using a risk assessment instrument and measured by a fail-
ure probability.
Fig. 7 shows the sensitivity of option value to market
risk, or the volatility of the expected payoffs. It can be seen
that an upward change in volatility increases the expected
option value. This conclusion is consistent with the opinion
held by most real option researchers [8,7]. A higher market
risk means bigger fluctuations in market conditions. The
real option valuation technique can capture valuable
opportunities in case of favorable changes, but cut off neg-
ative branches in case of pessimistic market conditions, to
avoid possible losses. Therefore, the greater the market
risks, the higher the option value of IT investment.
Fig. 8 illustrates the impact of private risks on IT invest-
ment value, which is rather different from the market risk.
When the project development failure probability decreases
from 0.53 to 0.3, the investment value grows significantly,
to 1.34 million RMB (an 85% increase). It is implied that
private risk has a more significant impact on the investment
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Volatility of revenues (σ)
Real
Options
Value
Fig. 7. Market risk’s impact on investment value.
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Failure rate of the target project (F)
Real
Options
Value
Fig. 8. Private risks’ impact on investment value.
784 T. Chen et al. / International Journal of Project Management 27 (2009) 776–786

11. value than market risk in this case. Note that when the pro-
ject failure rate increases to 0.6, the option value of NCIE’s
ERP investment drops to zero. It means that the ERP sys-
tem does not deserve an investment in case of such a high
private risk. This result is consistent with people’s common
intuition, in that a great technological complexity or devel-
opment difficulty could make an investment project less
attractive; however, the difference between impact of public
and private risks on IT investment value has not been
emphasized sufficiently in prior IS literatures. The evalua-
tion method we present has modeled private and market
risks in different ways, which could help to illustrate clearly
the mechanism of risks’ impact on the investment value.
6. Conclusion and future research
Typical information technology investments are exposed
to multiple sources of risks. Real option analysis is a prom-
ising tool to formulate investment decisions in uncertain
environments, but it could not deal with the mix of public
and private risks sufficiently in the valuation procedures.
Besides, the relationship between option value and IT pro-
ject risk has never been explored adequately; it remains
debatable. These gaps of real options analysis have limited
its strengths for evaluating IT investments characterized by
high uncertainties.
The present paper develops a real options based
approach to evaluate IT investments that are subject to
multiple sources of risks. The methodology we suggest
has incorporated risk identification and assessment, on
the ground that IT risks have a significant influence on
the investment value. By modeling public risks and private
risks in different ways, this evaluation approach could
address both kinds of risks and demonstrate the relation-
ship between IT risks and options value more clearly.
The public risk, or market risk, has a positive effect on
the options value of the project. The private risk, or pro-
ject-related risk, however, tends to undermine the expected
value of an investment.
We are confident that this method would help IT manag-
ers produce a well-structured valuation process in IT invest-
ment decision-making. The insights obtained from our
methodology could also provide guidelines for practitioners
to develop risk management strategies in IT investments. A
case study concerning an ERP investment in a construction
engineering company has illustrated the value of applying
the proposed real options analysis methodology.
However, because the investment in reality may be more
complex, further research is needed to improve this valua-
tion approach. Firstly, the proposed ROA approach shows
a one-time snapshot analysis of risks, as they relate to an
IT investment. In fact, project risks might change over
time. For example, the NCIE ERP project was planned
to be implemented in two phases. Most developers were
not familiar with the .NET technology before starting the
project, but their understanding of such a technology
should improve as they move from one phase of the project
to another. Therefore, the complexity risk might be less of
a problem as developers familiarize themselves with the
technology and the business they are dealing with. Devel-
oping an evaluation approach that supports dynamic deci-
sions would be a promising way to make this model more
powerful.
Secondly, this approach allows multiple stakeholders to
be involved in estimation of real option value, but it simply
averages all the evaluators’ assessments. Although multi-
criteria decision models (MCDM) have been used for sev-
eral decades to model subjective/objective, tangible/intan-
gible factors, it can also be used for risk assessments [47].
It would help to alleviate the biases of individual evalua-
tors’ personal preferences and achieve a more reliable val-
uation of the target investment.
Another visible problem is that a decision tree grows
very fast, as we consider more complicated forms of
options embedded in an investment, or as the project cost
is also uncertain. How can we map all plausible investment
outcomes, or develop a complete tree to calculate the
expected investment value? Finally, this research adopts
risk neutrality in the valuation approach, and the risk tol-
erance of individual people is ignored. How can the valua-
tion model avoid serious bias from particular evaluator’s
personal preference? Challenges like these are call for addi-
tional, sustained research efforts.
Acknowledgements
This project is supported by the grant from the NSFC of
China (Project No. 70801031, 70571025, 70731001), the
NSFC of China and RGC of Hong Kong Joint Research
Scheme (Project No. N_CityU110/07), the Social Science
grant from Ministry of Education (Project No.
07JC630044), and by the China Postdoctor grant (Project
No. 20080440137). We would like to thank the editor
and anonymous reviewers of JPMA for their excellent im-
provement to this article. Any mistakes are, of course, our
responsibility.
References
[1] Panayi S, Trigeorgis L. Multi-Stage real options: the cases of
information technology infrastructure and international bank expan-
sion. Quart Rev Econ Finance 1998;38(Special issue):675–92.
[2] Margrabe W. The value of an option to exchange one asset for
another. J Finance 1978;33(1):177–86.
[3] Dos Santos BL. Justifying investment in new information technolo-
gies. J Manage Inform Syst 1991;7(4):71–89.
[4] Schwartz ES, Zozaya-Gorostiza C. Investment under uncertainty in
information technology: acquisition and development projects. Man-
age Sci 2003;49(1):57–70.
[5] Benaroch M. Managing information technology investment risk: a
real options perspective. J Manage Inform Syst 2002;19(2):43–84.
[6] Tallon PP et al. Using real options analysis for evaluating uncertain
investments in information technology: insights from the ICIS 2001
debate. Commun AIS 2002;9(9):136–67.
[7] Amram M, Kulatilaka N. Real options: managing strategic invest-
ment in an uncertain world. Boston: Harvard Business School Press;
1999.
T. Chen et al. / International Journal of Project Management 27 (2009) 776–786 785

12. [8] Benaroch M, Kauffman RJ. A case for using real options analysis to
evaluate information technology investments. Inform Syst Res
1999;10(1):70–86.
[9] Kumar RL. A note on project risk and option values of investments
in information technologies. J Manage Inform Syst
1996;13(1):187–93.
[10] Erdogmus H. Valuation of learning options in software development
under private and market risk. Eng Econ 2002;47(3):308–53.
[11] Salerno LM. What happened to the computer revolution? Harvard
Bus Rev 1985(Nov–Dec):129–38.
[12] Benaroch M et al. Option-based risk management: a field study of
sequential IT investment decisions. J Manage Inform Syst
2007;24(2):103–40.
[13] Bahli B, Rivard S. Validating measures of information technology
outsourcing risk factors. Omega 2005;33(2):175–87.
[14] Boehm BW. Software risk management: principles and practices.
IEEE Software 1991;8(1):32–41.
[15] Barki H, Rivard S, Talbot J. Toward an assessment of software
development risk. J Manage Inform Syst 1993;10(2):203–25.
[16] Wallace L, Keil M, Rai A. Understanding software project risk: a
cluster analysis. Inform Manage 2004;42(1):115–25.
[17] Clemons EK. Evaluating strategic investment in information systems.
Commun ACM 1991;34(1):22–36.
[18] Kemerer CF, Sosa GL. System development challenges in strategic
use of information systems. Inform Software Tech 1991;33(3):212–23.
[19] Erdogmus H. Value of commercial software development under
technology risk. The Financier 2000;7(1–4):101–14.
[20] Kumar RL. Managing risks in IT projects: an options perspective.
Inform Manage 2002;40(1):63–74.
[21] Sullivan KJ et al. In: Trigeorgis L, editor. Real options and business
strategy. London: Risk Books; 1999. p. 215–62.
[22] Kumaraswamy A. An empirical investigation of a real options
perspective on RD. In: Proceedings of the second annual conference
on real options applications; 1998.
[23] Jeffery M, Leliveld I. Best practices in IT portfolio management.
Sloan Manage Rev 2004;45(3):41–9.
[24] Benaroch M, Lichtenstein Y, Robinson K. Real Options in IT risk
management: an empirical validation of risk-option relationships.
MIS Quarterly 2006;30(4):827–64.
[25] Taudes A, Feurstein M, Mild A. Options analysis of software
platform decisions: a case study. MIS Quarterly 2000;24(2):227–43.
[26] Clemons EK, Gu B. Justifying information technology investments:
balancing the need for speed of action with certainty before action. J
Manage Inform Syst 2003;20(2):11–48.
[27] Miller L, Choi SH, Park CS. Using an options approach to evaluate
Korean information technology infrastructure. Eng Econ
2004;49(3):199–219.
[28] Dai Q, Kauffman RJ, March S. Valuing information technology
infrastructures: a growth options approach. Inform Technol Manage
2007;8(1):1–17.
[29] Fichman RG, Keil M, Tiwana A. Beyond valuation: ‘options
thinking’ in IT project management. Calif Manage Rev
2005;47(2):74–96.
[30] Fichman RG. Real options and IT platform adoption: implications
for theory and practice. Inform Syst Res 2004;15(2):132–54.
[31] Kumar RL. A framework for assessing the business value of
information technology infrastructure. J Manage Inform Syst
2004;21(2):11–32.
[32] Merton RC. Theory of rational option pricing. Bell J Econ Manage
Sci 1973;4(1):141–83.
[33] Edwards FR, Ma CW. Futures and options. New York: McGraw-
Hill; 1992.
[34] Chatterjee D, Ramesh VC. Real Options for risk management in
information technology projects. In: Proceedings of the 32nd Hawaii
international conference on system sciences; 1999.
[35] Borison A. Real options analysis: where are the emperor’s clothes? J
Appl Corp Finance 2005;17(2):17–31.
[36] Smith JE, Nau RF. Valuing risky projects: option pricing theory and
decision analysis. Manage Sci 1995;41(5):795–816.
[37] Sambamurthy V, Bharadwaj A, Grover V. Shaping agility
through digital options: reconceptualizing the role of informa-
tion technology in contemporary firms. MIS Quarterly
2003;27(2):237–63.
[38] Balasubramanian P, Kulatilaka N, Storck J. Managing information
technology investments using a real-options approach. Strategic
Inform Syst 2000;9(1):24–39.
[39] Appari A, Benaroch M. A viable approach for measuring the risk-
return relationship of IT investments. In: Proceedings of the Americas
conference on information systems, August 2005, Omaha, NE.
[40] Dixit A, Robert Pindyck. Investment under uncertainty. Princeton,
NJ: Princeton University Press; 1994.
[41] Laughton D, Guerrero R, Lessard D. Real asset valuation: a back-to-
basics approach. J Appl Corp Finance 2008;20(2):46–65.
[42] De Reyck B, Degraeve Z, Vandenborre R. Project options valuation
with net present value and decision tree analysis. Eur J Oper Res
2008;184(1):341–55.
[43] Linberg KR. Software developer perceptions about software project
failure: a case study. J Syst Software 1999;49(2–3):177–92.
[44] Shan L et al. Assessing risks through information systems failure
probability based on the life cycle theory. In: Proceedings of the sixth
international conference on management, August 2007, Wuhan,
China.
[45] Pinto JK. Project Management: Achieving Competitive Advantage.
Pearson. Education, Inc.; 2007.
[46] Cox J, Ross S, Rubinstein M. Option pricing: a simplified approach. J
Finan Econ 1979;7(3):229–63.
[47] Chapman RJ. The effectiveness of working group risk identification
and assessment techniques. Int J Project Manage 1998;16(6):333–43.
786 T. Chen et al. / International Journal of Project Management 27 (2009) 776–786
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