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1. Introduction
Organizations are making large investments in information technology
(IT). However, organizational benefits resulting from these investments
are not always easy to measure (Brynjolfsson 1993, Brynjolfsson 1994, Brynjolfsson
and Hitt, 1995, Clemons 1990, Clemons and Weber 1990, Hitt and Brynjolfsson
1996, Powell 1992, Yap 1986) There is some evidence to indicate that IT
investments improve organizational productivity (Brynjolfsson and Hitt,
1995). However, the relationship between IT investments and organizational
performance remains poorly understood and some leading researchers in economics
are concerned about the payoff from investments in information technology
(Uchitelle, 1996).
An important issue in understanding the business value of information
technology is expressing the benefits of IT investment in a manner that
senior executives (particularly, financial executives) can relate to. Quantification
of the business value of IT in terms of financial numbers such as net present
value (NPV) helps. However, traditional financial calculations such as
NPV may not capture all factors that need to be considered (Clemons 1990,
Dixit and Pindyck 1994, Dixit and Pindyck 1995, Nichols 1994, Pindyck 1991,
Tam 1992, Trigeorgis 1995, Trigeorgis 1996). This paper examines an important,
relatively intangible, benefit that has been associated with IT investments,
namely, improved responsiveness (Brynjolfsson 1994, Kumar forthcoming,
Lucas and Olson 1994). Responsiveness can be defined as the ability to
quickly react to changes, and is one of several different types of flexibility
(Kumar forthcoming, Sethi and Sethi 1990). The focus of this paper is to
illustrate that the value of IT-enabled responsiveness may be quantifiable
and can be expressed in terms that senior financial executives can relate
to. A framework for understanding the value of responsiveness derived from
IT investments is presented. This framework is based on real options theory
(Dixit and Pindyck 1994, Dixit and Pindyck 1995, Trigeorgis 1995, Trigeorgis
1996), a relatively new theory in the area of capital budgeting. This theory
has the potential for considering and possibly quantifying factors that
are often not adequately addressed by traditional financial techniques
such as NPV. Thus, it represents a valuable addition to the toolkit that
can be used for understanding IT investments and addresses a need that
has been identified in earlier MIS research (Brynjolfsson 1993).
This paper is organized as follows: An introductory discussion of real
options is provided in Section 2. A review of related literature on evaluation
of investments in IT is provided in Section 3. An example of IT-enabled
responsiveness is provided in Section 4. A specific model for analyzing
the value of IT-enabled responsiveness is presented in Section 5. Managerial
implications of this research are discussed in Section 6. Conclusions and
directions for future research are discussed in Section 7.
2 . Real Options
An option can be defined as a right but not an obligation. Financial
options are options on financial assets. For example, one might acquire
an option to buy 1000 shares of Netscape stock at $100.00 per share on
1/1/99 by paying a sum of money (say, $20.00 per share) today. This transaction
is an example of buying an European financial call option. The $20 per
share paid to acquire the option is called the option premium and the price
of $100 per share of Netscape is called the strike price. The option holder
could buy 1000 shares of Netscape stock at $100 per share (this is referred
to as exercising the option) if the transaction is likely to yield a profit
(for example, if the selling price for Netscape shares was $150 on 1/1/99).
On the other hand, the option holder is not required to exercise the option
if conditions were unfavorable (for example, if Netscape shares were priced
at $75.00) on 1/1/99.
Real options are options on real assets. An example of a real option
would be an option to buy a 100 personal computers (PCs) with a certain
specification at $1500 anytime within the next year. It is likely that
the actual prices of the PCs could vary depending on technology and market
factors. This is an example of an American type call option on a real asset
(PCs) which can be exercised at any time prior to an expiry date (unlike
European type options, which can only be exercised on a particular date).
Recent research in economics, finance, and MIS has highlighted the fact
that traditional financial measures such as net present value do not correctly
value investment opportunities that contain real options (Dixit and Pindyck
1994, Dixit and Pindyck 1995, Dos Santos 1991, Kemna 1993, Kester 1984,
Kumar 1996, Majd and Pindyck 1987, McDonald and Siegel 1986). NPV calculations
assume that once a pattern of cash flows is arrived at, management does
not have options to change the course of the project as new information
is received (Kensinger 1987, Kester 1984). However, management may have
options to alter investment (for example, abandon a project, or switch
to manufacturing a modified product) as the investment proceeds.
In the case of the American and European call options described above,
exercising the option results in either a financial asset (Netscape stock)
or a real asset (PCs). These types of options where exercising an option
results in an asset are called simple options. Sometimes, exercising one
option results in another option. For example, consider an organization
that has an option to invest in a telecommunications network sometime over
the next year. Exercising this option might result in the option to use
the telecommunications network for electronic commerce (a second option).
Such investment scenarios where exercising one option results in another
option are referred to as compound options.
Many investment scenarios can be considered as sets of options. The
chief financial officer of a major multinational pharmaceutical company
has been quoted as saying "... to me all kinds of business decisions are
options" (Nichols 1994). Several situations where investment opportunities
contain real options have been studied. These include investments in flexible
manufacturing systems (Kamrad and Ernst 1995, Kulatilaka 1988), exploration
for oil (Kemna 1993), research and development expenditures (Roberts and
Weitzman 1981), and investments in new information technologies (Dos Santos
1991, Kumar 1996). For example, investment in flexible manufacturing systems
may result in options to expand production, contract production, or alter
the product mix at a later date. Investments in research and development
may result in options to undertake commercial production of new products
at a later date. In the MIS literature, Dos Santos (Dos Santos 1991) illustrates
that traditional financial evaluation methods are limited for evaluating
investments in new information technologies and proposes the use of a specific
option pricing model for evaluating scenarios where IT investments generate
additional options. For example, investment in a telecommunications network
may result in options to invest in distributed databases at a later date.
Kumar (Kumar 1996) examines some interesting properties of this option
model in the context of risky information technology projects. This paper
uses an option model developed in the context of sequential investments
to model investment in IT projects. Details of this model are described
in Section 5.
3. Evaluation of investments in IT
Researchers have recognized that measuring the business value of information
systems goes beyond evaluating productivity measures [Brynjolfsson 1994,
Clemons 1990,Tam 1992). Several intangible factors need to be considered.
A survey of 295 companies conducted by Information Week emphasizes the
need to look beyond cost savings in evaluating information technology related
benefits (Brynjolfsson 1994). Improved customer service was identified
by respondents as the most important benefit of investing in information
systems. Cost reduction was next in importance, followed by timeliness
of customer interactions, improved product and service quality, support
for reengineering efforts, and better flexibility. These benefits are often
qualitative and difficult to measure. Also, many of these benefits could
be interrelated. For example, support for reengineering could be related
to better flexibility, and improved customer service could be related to
increased flexibility. From the perspective of IT managers, justifying
investments in new IT projects often involves qualitative analysis of benefits
since existing capital budgeting techniques such as net present value are
of limited use (Tam 1992).
This paper examines an important and interesting benefit resulting from
information technology investments, namely flexibility. Webster’s dictionary
defines the term flexible as " capable of responding or conforming to a
changing or new situation". Researchers from several disciplines such as
economics and finance (Dixit and Pindyck 1994, Nichols 1994, Smit and Ankum
1993, Trigeorgis 1995), decision theory (Smith and Nau 1995), operations
management (Gerwin 1993, Sethi and Sethi 1990), strategic management (Evans
1991, Wernerfelt and Karnani 1987) and MIS (Boynton 1993,Clemons and Weber
1994,Kumar forthcoming, Lucas and Olson 1994) have studied flexibility.
In the MIS literature, Lucas and Olson (Lucas and Olson 1994) discusses
the fact that IT investments can result in organizational flexibility and
provides examples of such situations. Clemons and Weber (Clemons and Weber
1994) illustrate that information technology enables the implementation
of flexible, finely tuned "segment tailored" strategies that are much more
effective than simple strategies (such as cost leadership, differentiation
or niche). Information technology makes it easy to easily change strategies
for multiple, finely-differentiated market segments. Boynton (Boynton 1993)
defines the concept of "dynamically stable" organizations (organizations
that build a stable set of process capabilities that are dynamic enough
to deal with a variety of product and customer demands) based on information
technology. Kumar (Kumar forthcoming) discusses the fact that IT investments
often need to be accompanied by other kinds of investments and process
changes in order to enhance organizational flexibility and provides a framework
for understanding IT-enabled flexibility.
4. IT-Enabled Responsiveness: An Example
This research examines responsiveness, which is one type of flexibility.
Responsiveness is the ability to vary the maximum rate at which resources
can be committed to a set of tasks in order to respond to change. A more
responsive organization is better equipped to deal with change by rapidly
committing resources to deal with that change. For example, an option to
alter production may be more valuable to an organization that can easily
alter production by committing additional resources (a more responsive
organization) compared to an organization that cannot easily alter production
by committing additional resources (a less responsive organization).
IT investments can play an important role in increasing the maximum
rate at which resources can be committed (or increasing the minimum cycle
time for a set of tasks) as illustrated by the following example based
on (Tapscott 1996, pp.143-147).
Design is an extremely important business process for aircraft manufacturer
Boeing. However, given the complexity of aircraft design, there are several
sources of uncertainty (such as suggestions from other designers, manufacturing
problems, and changes in customer requirements). It is important for the
design process cater to these sources of uncertainty in order to produce
high quality designs that enhance the company’s competitiveness. Boeing’s
original design process was relatively unresponsive since it was expensive
and time consuming to make design changes. This lack of responsiveness
was manifested in a high cycle time for design (a complete design which
took several design changes into account).
Boeing realized the importance of reducing the cycle time for design
and started using CATIA (computer-aided three-dimensional interactive application)
and ELFINI (finite element analysis system). This information system allowed
design teams to work concurrently unlike the earlier scenarios where versions
of designs slowly traveled from one designer to the other in a linear fashion
with backtracking where required.
The high precision drawings generated by CATIA helped the designers
to visualize if parts would fit or if adding new subsystems altered the
design in undesirable ways, thus reducing manufacturing problems. The advanced
simulation and graphics features facilitated relatively easy creation and
on-screen testing of sophisticated, innovative designs. Investment in CATIA
also involved process changes such as concurrent engineering, multi-functional
work teams, and on-line design and testing.
In this example, information technology (CATIA) made Boeing more " capable
of responding or conforming to a changing or new situation (requiring design
changes)" thus enhancing responsiveness. The use of information technology
increased the maximum rate at which resources could be committed to deal
with design change (more people could work concurrently on the design,
and lesser time was required for refocusing resources to deal with design
changes). The following section illustrates how investment opportunities
such as this example can be valued using concepts from the real options
literature.
5. The Value of IT-Enabled Responsiveness
The objective of this section is to illustrate some insights provided
by modeling investment opportunities as real options. Several factors impact
the value of a real option. These include the type of the option (whether
the option is a call, American, European, simple, compound option, or some
other type of option), cost of exercising the option, the benefits obtained
from exercising the options, the nature of uncertainty of the costs and
benefits, time available for exercising the option, and whether the underlying
asset of the real options produces any intermediate cash flows. Option
valuation involves using an appropriate model that captures some or all
of these parameters.
The approach used in this research is different from earlier options-based
MIS research (Dos Santos 1991) which used a model for valuing simple options
to evaluate investments in new information technologies (first-stage investments).
The research described in (Dos Santos 1991) models investments in new information
technologies as generating a second-stage option to make additional investments.
This second stage option should be exercised by a particular date. This
research, on the other hand, models investment opportunities as compound
options and not as simple options. Also, the investment opportunity does
not expire at a certain time. The model used in this paper was proposed
by Majd and Pindyck (Majd and Pindyck 1987) for valuing sequential investment
opportunities. The reason for choice of this model is that it explicitly
models investment projects as taking time to complete and requiring sequential
investment, which is typical of many IT-related investment projects. Sequential
investment and time to complete are characteristics of many real world
investment projects. As will be illustrated later, explicit modeling of
projects as requiring time to complete provides interesting insights into
the value of improved responsiveness resulting from IT investments. Also,
this model recognizes that the value of responsiveness to an organization
may be related to the intensity of competition. It includes parameters
to analyze the impact of competition.
An opportunity to invest in a project could be considered a compound
call option. Each dollar invested buys an option to make additional investment
and investment occurs continuously in time. In other words, management
has the option to stop investing at any time during the duration of the
project and resume investing when required. This flexibility to alter the
course of the project makes the option value of the project different from
its net present value. Investment can occur at a maximum rate k
and hence the project takes time to complete. At any point in time, let
C be the additional investment required in order to complete the
project. Then, the minimum time to complete the project is C/k.
The value of benefits from the project B is a stochastic variable
whose present value is denoted by B*. Since the project takes time
to complete, the present value, B* can be significantly different
from B. Similarly the present value of additional investment required
to complete the project C* can be significantly different from C.
Table 1 illustrates the meaning of these parameters in the context of the
aircraft design example in Section 4.
In the original model of Majd and Pindyck, C is assumed to be
known with certainty. However, this research recognizes that C cannot
be determined exactly and is subject to variation for many projects. Hence,
this paper interprets B* as a net benefit after any variations in
costs have been adjusted from the benefits. The value of the opportunity
to invest in such a project can be denoted as V (B*,C*, k,  ,
r). 1/k represents the minimum cycle time for investment and is a function
of resource availability as well as the processes involved in investment.  represents
the opportunity cost of waiting to invest. A high value of  denotes
a scenario where delaying is
| Parameter |
Description |
| C* |
Estimate of the present value of additional
cash outflows for completion of design (at any point in time). |
| B* |
Expected present value of cash flows from sale
of aircraft minus any variations in cost of design from C* |
| C/k |
Expected minimum cycle time for design of aircraft |
 |
standard deviation of the percentage change
in B* per year |
|
|
Opportunity cost of delaying investment (expressed
as a % return on investment) |
Table 1. Major Model Parameters
expensive. This could be a scenario where competition is intense and
early investment preempts competition.  is
the percentage change in project cash flows representing the net benefit
over unit time and r denotes the risk-free rate of return. V (B,C, k, ,
r) can be obtained by numerical solution of partial differential equations.
Additional details regarding this model, methods of numerical solution,
and comparative statics describing the effect of different parameters on
the real option value can be found (Dixit and Pindyck 1994, Majd and Pindyck
1987). Simple illustrative examples are provided below, based on (Majd
and Pindyck 1987)
Consider an investment project with an expected present value of investment
of $5.65 million. Let r =0.2, =0.6,
and = 0.2, and k=
$1 million/year
Table 2 illustrates the value of V (B,C, k,  ,
r) (real option value) for different values of B* and C*.
| Present value of net benefits of
the completed project (B*) |
Present Value of Remaining Costs (C*)
C*=5.65 C*= 4.76 C*= 3.84 C*=2.91
________________________________________________________
Option Values |
|
5.70
|
0.74
|
1.34
|
2.54
|
3.88
|
|
6.62
|
1.22
|
2.23
|
3.57
|
4.98
|
|
7.69
|
2.02
|
3.34
|
4.77
|
6.25
|
|
8.94
|
3.20
|
4.65
|
6.17
|
7.73
|
Table 2. Value of V(B,C, k, ,
r) for different values of B* and C*.
The value of the investment option could be different from the NPV of
the project (NPV). For example, when B*=6.62 and C*=5.65, NPV = 0.97 and
V (6.62,5.65, 1,.06, 0.2, .02)= 1.22. It is also clear that reducing
C* or increasing B* increases the value of the investment opportunity.
In this example, the value of the project can decrease or increase over
time. Any increases in expected costs reduce the value of the project.
Hence, at any point in time we can ask the following question:
"Given that it will cost X dollars more to complete the project and
B* could increase or decrease, should I continue investing in this project
now". Majd and Pindyck (1987) illustrate that it makes sense to invest
in the project only if the NPV of expected benefits from the project (B*)
exceeds a certain critical value ( *).
In the above example,  *=
7.69 when C*=5.65. If the actual NPV of net benefits of the project (B*)
is less than *, then it
is better to wait for the value of B* to change rather than to invest.
The effect of competition on investments can be modeled by the parameter  which
represents the opportunity cost of delaying investment. When competition
is high and it is important to complete investment quickly in order to
preempt competition, is
likely to be high.
|
|
 *
|
|
0.02
|
17.82
|
|
0.06
|
7.69
|
|
0.12
|
7.03
|
Table 3. Values of *
for different values of (r
=0.2, = 0.2, k= 1, C*=5.65)
Table 3 illustrates that increasing reduces
the cutoff net present value of net benefits (*).
Reducing * decreases
the need to stop or delay investment, thus leading to quicker completion
of the project.
At any point in time C/k (the remaining investment/ maximum rate
of investment) represents the minimum time required to complete the project.
Hence an increase in k (the maximum rate at which investment can
be made) is analogous to a reduction in minimum time required to complete
the project. IT investments often add value to an organization by increasing
the maximum rate at which investment can be made (or decreasing the minimum
cycle time). Examples are provided in the next section. Figure 1 illustrates
the pattern of variation of the value of an investment project (real option
value) V (B,C,k) as a function of minimum cycle time T (T=C/k).
It is clear that the effect of cycle time reduction depends on the value
of the project (B*) and is higher for higher values of B*. The
ability to reduce minimum cycle time (by increasing the maximum rate at
which investment can be made), or responsiveness also has value. The marginal
value of unit reduction in cycle time is given by the slopes of the curves
for different values of cycle times and project value. For example, the
marginal value of unit reduction in cycle time when B*=7, and T=2.5 is
0.66. Figure 1 also illustrates that the marginal value of cycle time reduction
(for the same value of B*) decreases as cycle time becomes less. In other
words, cycle time reduction is more valuable when cycle times are high
than when they are already low. This discussion of the effect of cycle
time reduction illustrates that use of the options pricing model provides
more detailed insight into the value of responsiveness (cycle time reduction)
than mere qualitative statements about improved responsiveness as a result
of IT use.
Figure 1. Effect of cycle time reduction on real option value
Thus, compound real options theory provides a richer framework for analyzing
investments than do traditional methods such as NPV. The implications of
this framework for understanding the value of investments in information
technology are discussed in the following section.
6. Managerial Implications
This paper has illustrated that a relatively intangible benefit such
as improved responsiveness can be better understood in quantitative terms
by using option models. Option values of investment opportunities in information
systems can be significantly different from the net present value and is
impacted by other factors besides costs and benefits. In particular, use
of options models provides additional insight into the effects of uncertainty,
responsiveness, and competition on the value of an investment opportunity.
Major organizations are using real options theory to understand complex
investment scenarios (Dixit and Pindyck 1995, Kemna 1993, Nichols 1994).
Modeling capital budgeting decisions in terms of financial options theory
is an approximation of reality (Smith and Nau 1995), since all the assumptions
in the original derivations used in the context of financial options may
not be valid. However, use of option pricing models may be better than
using relatively static NPV (Dixit and Pindyck 1995, Trigeorgis 1994, Trigeorgis
and Mason 1987) which assumes that management does not have the option
to alter the course of investment as the investment proceeds. Hence, framing
the IT investment evaluation problem in terms of real options is likely
to appeal to key finance executives who involved in capital budgeting decisions.
There is anecdotal evidence in the context of Research and Development
expenditures to illustrate that project proposals formulated using real
options concepts help to illustrate the value of intangible benefits (Naj
1990, Nichols 1994). This is an important benefit from a managerial perspective.
7. Conclusions and Future Research
This research has augmented the theory-base for understanding the business
value of information systems. A specific model of real option valuation
has been used to illustrate the value of improved responsiveness resulting
from IT investments. This integration of finance and MIS research is likely
to be of interest to a variety of researchers and practitioners.
The real options framework provides a basis for quantifying relatively
intangible benefits such as improved responsiveness or flexibility. Choice
of model parameters may be difficult in some cases and requires further
research. However, it is possible to analyze option models quantitatively
under different scenarios representing a variety of approximate parameter
values. Options-based analysis, based on approximate parameter values,
has proved useful practical insight into the value of flexibility in investments
in research and development (Nichols 1994), and oil exploration (Kemna
1993). This analysis could help to provide better insight into the value
of IT investments than do qualitative statements.
Several avenues for future research are possible. These include further
study of other real options models in the context of IT investments, combination
of real options frameworks with decision theory, case studies of the application
of real options concepts to real IT valuation problems, and empirical research
on the valuation of real options resulting from IT investments.
Acknowledgements: This research was funded in-part by a grant
from BarclaysAmerican and UNC Charlotte. I am also grateful to the anonymous
referees for their insightful comments.
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About the Author
Ram L. Kumar is Assistant Professor in the Information&Operations
Management Department at the Belk College of Business Administration, the
University of North Carolina, Charlotte. He received his B.Tech and Post
Graduate Diploma in Management (MBA) degrees from the Indian Institute
of Technology, Madras, and Indian Institute of Management, Bangalore, respectively.
He has worked for five years in information systems development and management.
He received his Ph.D. from the University of Maryland in 1993, where he
was the recipient of the Frank T. Paine Award for Academic Merit. His research
interests include management of investments in technology, security and
control in information systems, and the interface between MIS and Operations
Management. His research has appeared in Computers & Operations
Research, Database, International Journal of Production Economics, Journal
of MIS, Journal of Systems Management, edited books, and several
national and international conference proceedings including ICIS. His research
has been funded by organizations such as the US Department of Commerce,
the Maryland Industrial Partnerships Scheme, and BarclaysAmerican. He is
a member of ACM, AIS, DSI, and INFORMS.
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