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Valuing Technology Richard A. Howey In order to make intelligent decisions about implementing and managing a tech- nology, managers must be able to estimate the value of the technology ex ante and to measure that value ex post. If it is going to be of practical use, any overall theory of technology must include methods for estimating and measuring the value of technology. While some of this value is tangible and relatively easy to estimate and measure, much of the value is intangible and very difficult to estimate and measure. Unfortunately, the state of the art in estimating and measuring the value of technology, particularly intangible value, is primitive at best. The bursting of the dot-com bubble is only the most recent example that illustrates the inadequacy of current practice. Using information technology as an example, this paper ex- plores techniques for estimating and measuring the intangible value of technol- ogy. Technology has been defined as "created competence as manifested in de- vices, procedures, and acquired human skills" (Van Wyk, 1999: 16). The term "competence" refers to an ability to do something. Thus, this definition im- plies that technology exists to be used for some purpose. It isn't just passive knowledge. Hopefully, use of the technology will benefit somebody such as a corporation or society as a whole. However, it usually takes some kind of investment to develop or implement a technology. It doesn't come free. Thus, if a corporation, government, non-governmental agency, etc. is going to imple- ment a technology, they need to be convinced that there is at least a reason- able chance that the benefit will be worth the investment. They need to be able to estimate the value they will receive from implementing the technol- ogy. Richard A. Howey is an information systems consultant in the Human Resources Management prac- tice at IBM Business Consulting Services where he applies data warehousing and other advanced analytical software technologies to satisfy the information needs of HR management professionals. He has over 27 years of experience in information systems as a software developer, project manager, and consultant. In addition to his professional activities, he recently completed a Master of Science degree in management of technology at the University of Minnesota. He may be reached at <rich_howey @yahoo.com>. Knowledge, Technology, & Policy, Fall 2004-Winter 2005, Vol. 17, No. 3-4, pp. 44-64.

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Valuing Technology

Richard A. Howey

In order to make intelligent decisions about implementing and managing a tech- nology, managers must be able to estimate the value of the technology ex ante and to measure that value ex post. If it is going to be of practical use, any overall theory of technology must include methods for estimating and measuring the value of technology. While some of this value is tangible and relatively easy to estimate and measure, much of the value is intangible and very difficult to estimate and measure. Unfortunately, the state of the art in estimating and measuring the value of technology, particularly intangible value, is primitive at best. The bursting of the dot-com bubble is only the most recent example that illustrates the inadequacy of current practice. Using information technology as an example, this paper ex- plores techniques for estimating and measuring the intangible value of technol- ogy.

Technology has been def ined as "created competence as manifested in de- vices, procedures, and acquired human skills" (Van Wyk, 1999: 16). The term "compe tence" refers to an ability to do something. Thus, this defini t ion im- plies that technology exists to be used for some purpose. It isn ' t just passive knowledge. Hopefully, use of the technology will benefit somebody such as a corpora t ion or socie ty as a whole . However , it usual ly takes some kind o f investment to develop or implement a technology. It doesn ' t come free. Thus, if a corporation, government, non-governmental agency, etc. is going to imple- ment a technology, they need to be convinced that there is at least a reason- able chance that the benef i t will be worth the investment . They need to be able to est imate the value they will receive f rom implement ing the technol- ogy.

Richard A. Howey is an information systems consultant in the Human Resources Management prac- tice at IBM Business Consulting Services where he applies data warehousing and other advanced analytical software technologies to satisfy the information needs of HR management professionals. He has over 27 years of experience in information systems as a software developer, project manager, and consultant. In addition to his professional activities, he recently completed a Master of Science degree in management of technology at the University of Minnesota. He may be reached at <rich_howey @yahoo.com>.

Knowledge, Technology, & Policy, Fall 2004-Winter 2005, Vol. 17, No. 3-4, pp. 44-64.

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Unfortunately, this isn't easy. One of the most difficult aspects of technol- ogy is predicting its value. Yet, predicting the value of a new technology, ex ante, is critical if business and society as a whole are going to invest in it and benefit from its use. It must also be possible to measure its value, ex post, to see whether value was actually created. It is widely accepted that if you can't measure it, you can't manage it. Any overall theory of technology must in- clude approaches to predicting and measuring its value if it is going to help us manage technology effectively.

In recent years, the value of information technology (IT) has been of con- siderable interest. IT provides an evocative example of just how hard it is to predict and measure the value of technology. In fact, Nobel Laureate econo- mist Robert Solow stated, "We see computers everywhere, except in the pro- ductivity statistics" (Brynjolfsson and Hitt, 1998: 4). This led to coining of the term "IT Productivity Paradox." It just doesn ' t appear that IT investments have the value they are expected to have. This paper explores the IT produc- tivity paradox and summarizes lessons that the paradox teaches us about valu- ating technology.

Next, we analyze methods that are typically used to predict the value of technology, ex ante. The specific methods discussed are discounted cash flow (DCF) and real option analysis. A hypothetical example is presented illustrat- ing the use of these techniques to value an IT investment.

We then turn to the measurement of technology value, ex post. Since many technology-based assets are intangible, we examine intangible asset account- ing techniques. Specifically, we discuss the work of Baruch Lev and of Paul Strassmann in measuring the value of intangible assets.

The IT Productivity ParadoxmWhat Can It Teach Us about the Value of Technology?

When Robert Solow made his famous statement in the 1980s, he was prob- ably looking at statistics that showed U.S. labor productivity had declined to an annual growth rate of only 0.8 percent while computer technology pur- chases were rising at an annual rate of 11 percent. Indeed, this does seem to show that computers and information systems do not pull their own weight (Strassmann, 1997: 83).

Other statistics, at a corporate level, verify the apparent lack of return from IT. Paul Strassmann, who has served as chief information officer of General Foods, Kraft, and Xerox, has tried to correlate corporate IT spending per em- ployee with return on equity. The result was that the relationship appeared random. He also tried other measures such as return on assets and return on net investment. The results were the same (Strassmann, 1997: 34-35).

Certainly, some investments in IT seem to have large paybacks. For ex- ample, Wal-Mart spends more on IT than the average in the discount retail industry and has reaped large rewards. (Foley and Mahmood, 1996: 8) How- ever, overall, while higher levels of IT spending may lead to higher financial returns, they do not necessari ly lead to higher financial returns. Investments in IT are not sufficient to guarantee profitable returns.

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Erik Brynjolfsson of MIT and other researchers have taken a different ap- proach to valuing IT investments. Their approach is to correlate IT expendi- tures to the market value of the corporation. The difference in results when this approach is taken is striking:

Using eight years of data for 820 non-financial firms in the United States, we find that an increase of one dollar in the quantity of computer capital installed by a firm is associated with an increase of about ten dollars in the financial markets' valuation of the firm. Other forms of capital do not exhibit these high valuations (Brynjolfsson and Yang, 1999: 1).

It has also been observed that announcements in the business press of IT investments may have a significant effect on a firm's market value. The mag- nitude of this effect is directly related to the degree to which IT capabilities are considered to be critical to competitive advantage in the industry (Richardson and Zmud, 2002: 3).

Clearly IT is valued as an intangible asset by the investment community. Indeed, intangible assets now make up the majority of the market value of most companies. We can see this by observing the mean market-to-book ratio of publicly traded companies. During the late 1970s and early 1980s, this ratio for the Standard & Poor (S&P) 500 companies was around 1. This meant that most of the market value of a company was based on its physical or tangible assets as reflected in its financial statements. However, throughout the 1980s and 1990s, this ratio has increased. In March 2001 this ratio was approximately 6. That means that for every $6 of market value, only $1 (17 percent) appears on the company's balance sheet as a tangible asset. The other $5 (83 percent) represents intangible assets. Even when replacement values rather than book values of tangible assets are considered, the ratio still exceeds 3 (Lev, 2001: 8).

If intangible assets are indeed so important, then why don't investments in the IT intangible asset show up in the productivity statistics? Productivity is the ratio of outputs produced divided by the inputs consumed to produce those outputs. At least one possibility is the difficulty of measuring inputs and outputs so that productivity can be computed:

Properly measured, output should include not just the number of widgets coming out of a factory, or the lines of code produced by a programming team, but rather the value created for the consumers. Fifty years ago, tons of steel or bushels of corn were a reasonable proxy for the value of output. In today's economy, value depends increas- ingly on product quality, timeliness, customization, convenience, variety and other "intangibles."

Similarly, a proper measure of inputs includes not only labor hours, but also the quantity and quality of capital equipment used, materials and other resources con- sumed, worker training and education, even the amount of "organizational capital" required, such as supplier relationships cultivated and investments in new business processes. The irony is that while we have more raw data today on all sorts of inputs and outputs then ever before, productivity in the information economy has proven harder to measure than it ever was in the industrial economy. (Brynjolfsson and Hitt, 1998: 1)

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By this theory, the improvements are there but they are intangible and so are hard to measure. Economists admit that their measures, such as gross do- mestic product (GDP), cannot account for all effects and thus may not be truly adequate measures. (Gwartney, Stroup, and Sobel, 2000: 181-182) For ex- ample, a modern luxury car that includes today's safety and convenience fea- tures wasn't available at any price 30 years ago. So is it really valid to compare productivity across 30 years in the automobile industry by dividing the total price of cars produced by the total cost of producing them? The measurement theory is a possible explanation of the paradox, at least in part.

Another complicating factor is the time lag between IT investments and their payoff. Most studies of IT examine the payoff question at a certain point in time. Unfortunately, IT implementations take time to realize their full po- tential. The phase immediately after implementation of a system is often one where a significant amount of learning and adaptation to the new systems occurs. This phase may not see much in the way of performance improve- ments. The true benefits may not emerge for anywhere from several days to weeks or months, or even years, depending on the size and complexity of the system. Any computation of productivity or improvement has to account for this time lag (Devaraj and Kohli, 2002: 13-14).

One interpretation is that it is not just the IT investment itself that adds value, but the organizational changes and other factors in the firm that are enabled by the IT investment. The long-term benefits are not just the returns from IT but from a system of technology and organizational change that only starts with the IT investment. Some studies have shown that the long-term benefits of such investments range from two to eight times as much as the short-term benefits (Brynjolfsson and Hitt, 1998: 7).

The post-1995 improvement in U.S. productivity growth has also fueled some speculation that the productivity paradox is history. However some re- searchers, such as Robert Gordon of Northwestern University, have shown that the use of computers may not be responsible for this productivity growth. The productivity growth may be more the result of the surge in the manufacturing of computers than in benefits realized from their use. This is a clearly unsustainable condition. If the use of computers does not have enough benefits to justify the purchase, then the manufacturing boom will collapse. Other factors besides the use of computer technology may have been responsible for the 1995-2001 surge in U.S. productivity growth (Gor- don, 2002).

Some experts believe that the IT productivity paradox is history since we know that capital markets value IT and have at least some explanations for the lack of productivity statistics. Others believe that it is still a problem because IT has failed to lift productivity growth throughout the economy. Yet others believe that while the paradox has been resolved and IT does indeed have a high rate of return, we have a new paradox. If IT does produce significant returns, then why aren't managers investing more in IT? (Devaraj and Kohli, 2002: 17-18).

Whether or not the paradox has been resolved, we can learn some impor- tant lessons through the debate:

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�9 It isn't always obvious how important investments contribute to the bottom line of the firm or the economy.

�9 Investments that create intangible assets are important. The financial markets value them.

�9 It isn't just the IT investment itself that is important. Business and organizational processes must also change to take advantage of the new technology if real value is to be generated.

�9 The truly significant value of an IT investment is based on the likelihood of its supporting organizational and process changes that will add value to the company.

Ex Ante Prediction of Technology Value

Based on the discussion of the IT productivity paradox, we conclude that a major part of the value of technology is the intangible value that can be cre- ated from the use of that technology. Obviously, some technology will have a more tactical value. An example is a technology that reduces headcount re- quired to execute some business process. Yet, other technologies will have a more strategic value. That strategic value will be situational and hard to pre- dict. Yet, we need to at least try to do so if we are going to make intelligent decisions regarding investments in technology.

The following subparagraphs discuss two methods of predicting ex ante value, discounted cash flow (DCF) and real options. We then present a hypo- thetical example using both approaches and draw conclusions on the applica- tion of these methods based on the example.

Discounted Cash Flow

Discounted cash flow (DCF) is the basic concept taught in MBA finance courses. Readers who are familiar with DCF concepts such as net present value (NPV), weighted average cost of capital (WACC), and internal rate of return (IRR) may skip this section. Unfortunately, many technologists are not familiar with DCE As an example, this author was totally unaware of the concept for the first 20 years of his technical career. Thus, we include a short overview of DCF.

The basic idea of DCF is that there is a time value to money. Ask yourself which you would rather have: $1 given to you today or that same $1 given to you one year from now. Given this choice, most people would choose the $1 today. Why? It is because the $1 given to you today has more value to you than the $1 does a year from now. It has more value because you can do something with it now, such as invest it. Let's say you invest it in a bond that pays 10 percent interest. In one year, you will have $1.10. This is called the future value and is computed by:

FV = PV(1 +r)" Where:

FV = Future value PV = Present value r = Interest or discount rate n = Number of years in the future

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For the $1 given to us now, we can substitute PV --- 1, r = 0.10, and n = 1 into the formula and compute FV = 1.10. So the $1 given to you now has a future value one year from now of $1.10 assuming a 10 percent interest rate. $1 given to you a year from now is still only worth $1 a year from now.

We can turn this calculation around. Given a future value, an interest rate, and a number of years, we can compute a present value. All it takes is some simple algebra:

PV = FV/(I+r) n

If we plug our $1 a year from now (FV = 1) along with the same values of r (0.10) and n (1) we used previously into this formula, we find that that the present value is $0.90909. In other words, the present value of $1 given to us a year from now is a little under $0.91 assuming a 10 percent discount rate.

When we make a major investment, such as building an IT system, we will usually need to spend some amount of money over a number of years. For example, we will need to implement the system initially and then maintain it on an on-going basis. We also expect to receive some benefit from the new IT system over a number of years. These benefits can come from sources such as reduced costs, avoided costs, or increased revenues. Thus, for each year, we need to estimate the benefit we will receive from the IT system in that year and subtract the cost that we estimate we will have to pay in that year. The initial cost of implementing the system is usually taken as a cost in year 0. If we know the discount rate we need to apply, we can then compute the present value of the net cash flow for each year. If we add up all the present values for each year, then we have computed the net present value (NPV) of the invest- ment.

The following example that could represent the expected cash flow for implementing an IT system illustrates an NPV calculation.

Year Cost Benefit Net Cash Flow Discount Present Rate Value

$1,000,000.00 $0.00 $(1,000,000.00) 10% $(1,000,000.00)

$100,000.00 $200,000.00 $100,000.00 10% $90,909.09

$100,000.00 $300,000.00 $200,000.00 10% $165,289.26

$100,000.00 $400,000.00 $300,000.00 10% $225,394.44

$100,000.00 $500,000.00 $400,000.00 10% $273,205.38

$100,000.00 $600,000.00 $500,000.00 10% $310,460.66

Total NPV = $65,258.83

One question we need to address whenever we do an NPV calculation is the value we should use for the discount rate. Businesses need to finance themselves. They usually do this by some combination of loans or debt such as issuing corporate bonds and by owner equity such as selling shares of

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common and preferred stock. The people who invest in stocks and bonds expect to make some kind of return on their investment. Usually, stocks are riskier investments then bonds and investors expect to make a higher return on a stock than they do on a bond. If we know how much of our company is financed by various financial instruments and how much return investors in each of these types of financial instruments expect to make, then we can com- pute a weighted average of these expected returns. If our company is going to satisfy its investors, it needs to make at least this weighted average rate on all its business activities. This weighted average is called the weighted average cost of capital (WACC). It is essentially the interest rate that the company needs to pay its investors for the privilege of using their money to run the business. The discount rate used in NPV calculation is often the WACC. In our previous example, we assumed that our company's WACC was 10 percent.

In some cases, the project or IT system implementation that is being con- sidered has a much higher level of risk than the typical investment made by the company. In that case, it is standard financial practice to add a "risk pre- mium" to the WACC and use that as the discount rate in NPV calculations. But, to keep things simple, for now, we will assume that the risk premium is zero and will use WACC as the discount rate.

In our previous example, the NPV was a positive number. This is a good thing. It means that implementing this IT system creates value for our inves- tors. However, NPV is highly dependent on the discount rate. Let 's say our company 's WACC is 13 percent instead of 10 percent. The resulting NPV calculation, using the WACC as the discount rate, now looks like this:

Year

0

1

2

3

4

5

Cost Benefit Net Cash Flow

$1,000,000.00 $0.00 ($1,000,000.00)

Discount Present Value Rate

13% ($1,000,000.00)

$100,000.00 $200,000.00 $100,000.00 13% $88,495.58

$100,000.00 $300,000.00 $200,000.00 13% $156,629.34

$100,000.00 $400,000.00 $300,000.00 13% $207,915.05

$100,000.00 $400,000.00 13% $5OO,0OO.OO

$600,000.00 $500,000.00 $I00,000.00

$245,327.49

13% $271,379.97

Total NPV = ($30,252.58)

Now, our total NPV is negative. This means that implementing this IT sys- tem destroys value for our investors. Thus, this company should not do it. Note that it is exactly the same IT system and that the costs, benefits, and net cash flow are exactly the same as in the previous example, but the higher WACC of this company makes the NPV negative. The expenditure just doesn't have a high enough return to make it worthwhile. The initial investment in building the system should be spent on other things that do have a high enough return to produce a positive NPV.

Since we can see that the discount rate that is used in an NPV calculation can cause the NPV to be either positive or negative, we might wonder where

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the break-even point is. That is, what discount rate would give us an NPV of 0? That discount rate is called the internal rate of return (IRR) and gives us an idea of the actual interest rate that this investment is earning us. We can solve for IRR by adjusting the discount rate and computing NPV until we find the value that gives NPV = 0. This is illustrated in the following example:

Year

0

1

2

3

4

5

Cost Benefit Net Cash Flow Discount Present Value Rate

$1,000,000.00 $0.00 ($1,000,000.00) ($1,000,000.00) 12.01%

12.01% $100,000.00 $200,000.00 $100,000.00 $89,281.12

$100,000.00 $300,000.00 $200,000.00 1 2 . 0 1 % $159,422.37

$100,000.00 $400,000.00 $300,000.00 1 2 . 0 1 % $213,501.12

$100,000~00 $500,000.00 $400,000.00 1 2 . 0 1 % $254,154.93

$100,000.00 $600,000.00 $500,000.00 1 2 . 0 1 % $283,640.46

Total NPV -- $0.00

Here we can see that the IRR of implementing this IT system is 12.01 per- cent. Generally, if the IRR of a project is greater than a company's WACC, then the project will create value for the company's investors and the com- pany should authorize the project. However, if the IRR is less than the WACC, then the investment will destroy value and the company should not authorize the project.

When using IRR on risky projects, a company will often add a risk pre- mium to the WACC to define a "hurdle rate." IRR must exceed this hurdle rate in order for the project to be approved.

Difficulties Applying DCF to IT

DCF is the most common approach used to value capital investments. But that doesn ' t mean it is easy to use, especially for valuing risky technology investments.

One difficulty in applying the DCF model to IT is the difficulty of estimat- ing the future cash flows. As we stated earlier, many of the benefits of an IT system may be intangible, and thus hard to estimate, and may not be apparent until some time after the system is implemented. Because they may be intan- gible, the future benefits are often more difficult to estimate than the future costs. A common result is that many potential IT investments show a negative NPV.

Consider the following case. Let 's assume we are considering implement- ing a new decision support system. This system will replace an older reporting system. There will be some cost to implement, operate, and maintain the new system. We will eliminate the cost of operating and maintaining the old report- ing system. If the on-going costs of the new system are lower than the old system, we can show a positive net cash flow after the new system is imple- mented. If we have good historical financial data on the old system and our

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people have a good understanding of the new technology, we can develop reasonable estimates of these costs and benefits. So we calculate the costs and benefits out for a number of years. When we do this, we often find that the NPV of the projected cash flows is negative.

But this hasn't told the whole story yet. We have only estimated the opera- tional costs and benefits of the new system. We haven't begun to think about the users who will be using the new system to make business decisions. These are the more intangible benefits that should be seen after the system is imple- mented. The main reason to implement the decision support system is to allow these users to make "better decisions" in running the business. If we could estimate these benefits and add them to the cash flow, then our NPV may very well turn out to be positive. But, how does one place a financial value on something as nebulous as "better decisions?" This is really quite difficult and often requires us to engage subject matter experts on the technology and the business in a scenario planning exercise. This exercise may define several possible business scenarios for the future and attempt to develop potential financial benefits that the new system will provide under the various sce- narios. It may also develop probabilities of the various scenarios materializ- ing.

While difficult, this does not mean that producing a DCF is impossible. Processes such as decision tree analysis and Monte-Carlo simulation can be used to calculate NPV and/or IRR given probabilistic models of what the fu- ture cash flow could look like (Kulatilaka, Balasubramanian, and Storck, 1996: 11-12).

Another difficulty is determining the discount rate that should be used. We noted earlier that the discount rate should be the WACC plus a risk premium if the project is highly risky. However, the risk of a project can change with market conditions. A company's WACC is also not fixed and varies over time. Thus, using a f ixed d i scount rate may not be real is t ic (Kulat i laka , Balasubramanian, and Storck, 1996: 11-12). Both the WACC and the risk premium may be subject to changing conditions, and thus difficult to deter- mine.

It should also be noted that the standard approach to accounting for risk in DCF by adding a risk premium into the discount rate has an interesting effect. All else being equal, a higher discount rate produces a lower NPV. Thus, higher risk always lowers the value computed by a DCF analysis.

Real Options

In the decision support system example discussed earlier, we noted that the value of the intangible benefits of how the system will be used to make deci- sions that support the business is the most difficult thing to determine. We now note some additional interesting things about this system:

At least some of the investment in implementing the decision support system will be irreversible. That is, once we have paid for the development and installation of the system, we won't be able to get that money back.

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The decision support system gives the users the opportunity, but not necessarily the obligation, to use that system to make decisions at some future date that will sup- port or improve the business.

These characteristics of irreversibility and optionality are similar to what we find in certain kinds of financial instruments called options. One type of option is a call option on a stock. A call option gives the holder the right, but not the obligation, to buy the stock at a given price, called the strike price, at some future date. If the actual market price of the stock is above the strike price at that future date, then the option is "in the money" and it is a good deal. The holder of such an option will then exercise the option and make some money. However, if the actual stock price is below the strike price, the holder of the option will just let the option expire. That person is out the money he or she paid to purchase the option in the first place, but is under no obligation to exercise the option and thus lose even more money. Thus, the financial call option is quite similar to the decision support system:

�9 The cost that the holder of the call option pays to purchase the option is irrevers- ible. If the holder decides not to exercise the option, he or she won't be able to get that money back.

�9 The call option gives the holder the opportunity, but not the obligation, to use the option to purchase the stock at a price that, hopefully, is below the market price of the stock and make some money.

Because our investment in the decision support system creates options that have these properties in common with financial options, we can use option theory to valuate it. These options are created by real assets, such as an IT system, rather than financial assets such as a stock. Hence, they are known as "real options."

In recent years, real options have been used to valuate many types of IT systems. Just a small sample includes decision support systems (Kumar), ob- ject-oriented middleware (Dai, Kauffman, and March, 2000), and document imaging. (Kulatilaka, Balasubramanian, and Storck, 1996: 20-28) We can embed real options in IT implementation projects by structuring the project such that it has options to defer, abandon, stage, etc. the project (Benaroch, 2002: 6). Real options are also used in valuating other high-risk investments such as technology research and development and oil exploration. Some of the company's using real options for one purpose or another include Merck, Eli Lilly, Baxter International, Amgen, Genentech, Genzyme, Smith & Nephew, Endo Pharmaceuticals, Mobil, Chevron, Petrobras, Texaco, Conoco, Anadarko Petroleum, Dynergy, Amerada Hess, Duke Energy, and Aquila Energy (Boer, 2002: 118-119).

Unfortunately, the mathematics involved in valuating options is far more complex than the mathematics involved in a DCF analysis. The basic formula to valuate a call option is the Black-Scholes formula. This formula is based on the solution to a stochastic differential equation--a very esoteric area of math- ematics. It was developed by Fischer Black and Myron Scholes in 1973 and was extended by Robert Merton. Scholes and Merton received the Nobel Prize

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in Economics in 1997 for this work. Fischer Black died in 1995 or he would have undoubtedly shared the Nobel Prize with his colleagues ("1997 Nobel Prize").

Other formulas for valuating options are available. A formula that we will use later in this paper in an example is the Cox-Rubinstein equation (Cox, Ross, and Rubinstein, 1979: 6.):

C =

max(0, uS - K ) (1- r ) - d + m a x ( 0 , d S _ K ) U- (1- r) u - d u - d

l + r

Where: C = Value of the stock option S = Current stock price K = Option strike price r = Risk-free rate of return u = "Upside" multiplier d = "Downside" multiplier

(Note: In most documentation of the Cox-Rubinstein formula, it is assumed that 1 is already added to the risk free rate, that is, r = 1 + risk-free rate of return. However, to maintain consistency of notation with the previous dis- cussion of DCF, we have defined r as just the risk-free rate of return.)

This basic version of the Cox-Rubinstein formula assumes that there is one time period until the option matures and that the stock price will either move up by some factor, the upside multiplier, or down by some other factor, the downside multiplier, over that time period. However, the Cox-Rubinstein for- mula can be generalized to apply to more than one time period. In fact, as the number of time periods approaches infinity, the Cox-Rubinstein formula con- verges to the Black-Scholes formula (Cox, Ross, and Rubinstein 1979: 7-25).

A hallmark of the option pricing equations is that volatility or uncertainty is explicitly included. In the case of Black-Scholes, the standard deviation of the stock return represents volatility. In the case of Cox-Rubenstein, the upside and downside multipliers represent volatility. This is a major difference be- tween option pricing approaches and DCF. DCF assumes that we know the future cash flows with certainty. We have to add things such as Monte-Carlo simulation or decision trees on top of the DCF equations to account for uncer- tainty. With option theory, uncertainty is built into the basic equations them- selves.

Another point to note is that the risk free rate of return rather than the corporation's WACC is used in the option calculations. The risk free rate of return is generally taken to be the return generated by U.S. Treasury Bills. This is less volatile than a corporate WACC. It thus eliminates one of the issues we noted earlier about using WACC in DCF analysis.

Yet another point to note is that higher levels of volatility increase the value of an option. In DCF, increased levels of risk are often accounted for by in-

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creasing the discount rate. This decreases the net present value. Thus, options and DCF treat risk quite differently.

Another hallmark is that the value calculated through the option equations is often far greater than the value computed through DCE We will illustrate this later through an example. In fact, where DCF produces a negative NPV, real option analysis may compute a substantial positive value.

Real options can be used in conjunction with DCF analysis to estimate the total value of an investment, such as the implementation of an IT system. DCF is used to compute the NPV of the more predictable operational cash flows while real options are used to compute the value of the more intangible op- tions opened up by the IT implementation project. By adding the NPV plus the value of the real options together, we determine the total value of the investment (Dai, Kauffman, and March, 2000: 4).

Note that this discussion of real options is consistent with some of the ob- servations we made during the discussion of the IT productivity paradox:

Recent studies of information technology and productivity have shown that informa- tion technology has a minimal impact on productivity ... leading to the conclusion that firms have over-invested in information systems. However, if many IT investments are unexpired options not exercised during the period of the study, the level of IT invest- ments deployed towards increasing productivity may be over-estimated. This over- estimation.., can negatively bias the outcome of productivity studies even if the studies looked for a lagged effect of information technology on productivity (Kambal, Henderson, and Mohsenzadeh, 1992: 19).

Also from our earlier discussion, it seems that investors value a company's spending on IT more than the benefits that they can directly observe in the corporate financial statements. Could this be because the investors perceive, perhaps subconsciously, the real options that IT spending opens up for the company?

We must also recognize that real option analysis is not a magic silver bullet. Like any powerful tool, it can be used or abused:

Real options can turbocharge value creation. But their potential for value destruction is equally awesome. When real option thinking is applied without consideration of competitive dynamics and of the volatile nature of the capital markets that serve high- risk investors, the consequences to the unwary can be punishing (Boer, 2002: 129).

We must also beware of technical investments that close options rather than open new ones. Simply adopting the latest technological innovation for its own sake can actually leave the company with fewer real options than it had before. In such a case, value is destroyed. (Boer, 2002: 154-155).

While we have offered nothing like a formal proof, we do believe that there is at least enough circumstantial evidence to indicate that real options may be a realistic way to model and understand the intangible benefits of technology investments.

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An Example Using DCF and Real Options

We will now consider a hypothetical example of estimating the value of an IT system.

Let us assume that we have performed a DCF analysis for the implementa- tion costs and operational benefits of a new IT system and have computed an NPV of -$100,000. This negative NPV says that we should not implement the system. However, we have not yet accounted for the possible uses of this system. Adding these into the equation may very well make the NPV positive.

Let us further assume that we intend to use the new system in one year's time to accomplish some corporate function such as a targeted marketing cam- paign to raise revenue. There is some uncertainty as to just how much revenue this will realize. To keep things simple for this example, we will assume that only two outcomes are possible, an upside result and a downside result. We estimate that the potential upside benefit is $1,000,000 and that there is a 50 percent probability of this benefit being realized. On the other hand, we estimate that there is a 50 percent potential that a downside benefit of only $300,000 will be realized. In essence, this is a decision tree that has two branches. We also estimate that it will cost us $500,000 to implement the marketing campaign. We also have a WACC of 10 percent. From this information, we can use DCF to compute an expected NPV for this use of the system:

[(0.5)($1,000,000) + (0.5)($300,000) - $500,000] / (1 + 0.1) = $136,364

However, let us now assume that we could actually have performed the targeted marketing campaign with our older systems. We estimate that it would have cost more and been less effective, but we could have done it. Thus, it really isn't fair to allocate the entire $136,364 to the new system. We really should only allocate the difference between what we can do with the new system versus what we could have done with the old system. This would be the marginal value of applying the new system to this use.

To compute the marginal value, we need to estimate what we could accom- plish with the older systems. Let us assume that the up-side and the down-side benefits with the old system would have been less than they were with the new system. We estimate $950,000 and $250,000 respectively. We will also assume that the probability of the up-side benefit is only 40 percent. Finally, we will assume that the cost of implementing the targeted marketing cam- paign with the old system is $525,000. Our calculation is now:

[(0.4)($950,000) + (0.6)($250,000) - $525,000] / (1 + 0.1) = $4,545

The marginal benefit of using this new system to implement the targeted marketing campaign is:

Marginal Value (DCF) = $136,364 - $4,545 = $131,819

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Thus, the total marginal value for the system, including its implementation, maintenance, and use for the targeted marketing campaign is:

Total Marginal Value (DCF) = -$100,000 + $131,819 = $31,819

This total value is now positive indicating that this is an investment that the company should make.

We can also compute the value for this use of the system using real options methods. We will use an algorithm based on the Cox-Rubinstein equation (Dai, Kauffman, and March, 2000: 20-22). This algorithm computes the val- ues needed for the equation as follows:

S = PV of the benefit expected to be received from executing the option S = [(0.5)(1,000,000) + (0.5)(300,000)] / (1 + 0.1) = 590,909 u = potential upside benefit / S u = 1,000,000 / 590,909 = 1.692307692 d = potential downside benefit / S d = 300,000 / 590,909 = 0.507692308 K = cost to implement the option K = 500,000

If we take the risk free rate of return to be 7 percent, we can plug these numbers into the equation and compute:

C(new system) = $221,811

Note that this value is considerably larger than the $136,364 we computed for the value of using the new system for the targeted marketing campaign using DCF analysis.

Again, in order to be fair, we need to consider the fact that we could have implemented the targeted marketing campaign using our older systems. In implementing the new system, we effectively abandoned that option and in- stead created a new option using the new system. Thus, we really should compute the value of the option we abandoned and subtract it from this value to arrive at the net marginal value created by the new system. If we do this we find:

C(old system) = $150,677 Marginal Value (Real Options) = $221,811 - $150,677 = $71,134

Note that this net marginal value of $71,134 computed using real options is considerably smaller than the net marginal value of $131,819 computed us- ing DCF. Even though the value of the option created by the new system computed using option theory is considerably larger than the value computed using DCF, the net marginal value when the value of the option that we aban- doned is subtracted out is considerably smaller using option theory than using DCF.

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Also, if we add the value of this option to the original NPV of the new system we find the following:

Total Marginal Value (Real Options) = -$100,000 + $71,134 = -$28,866

The total marginal value computed using options is negative whereas the total marginal value computed using DCF was positive. Thus option theory says that this is not a good investment, unless more options can be found that make the total marginal value positive.

Implications for Technology Bubbles

The results of our example are counterintuitive. They also have profound implications. If we assume that real option theory is an accurate way of valu- ating the use of IT and other technology investments, then our example points to the following conclusions:

1. If a technology implementation enables truly new business processes that were not possible before the technology was implemented, that is the technology creates totally new real options, then DCF analysis may understate the value created by the technology.

2. If a technology implementation allows improvements to existing business pro- cesses such as lower cost or better results but doesn't enable truly new business processes, that is the technology replaces old options with better options but doesn't really create entirely new options, then DCF analysis may overstate the marginal value created by the technology.

Note that these conclusions may help to explain the IT productivity para- dox. If most IT investments are oriented towards improving existing business processes rather than creating new ones, then our conclusions state that they may in fact produce less financial benefit than a traditional DCF analy- sis would indicate that they should. It is only when an IT investment creates options to implement truly new business processes that it will have a superior valuation.

It can be argued that most IT investments improve existing business pro- cesses rather than create revolutionary new ones. For example, Peter F. Drucker stated in 1999, before the Internet bubble burst:

The truly revolutionary impact of the Information Revolution is just beginning to be felt. But it is not "information" that fuels this impact. It is not "artificial intelligence." It is not the effect of computers and data processing on decision-making, policymaking, or strategy. It is something that practically no one foresaw or, indeed, even talked about ten or fifteen years ago: e-commerce--that is, the explosive emergence of the Internet as a major, perhaps eventually the major, worldwide distribution channel for goods, for services, and, surprisingly, for managerial and professional jobs. This is profoundly changing economies, markets, and industry structures; products and services and their flow; consumer segmentation, consumer values, and consumer behavior; jobs and la- bor markets (Drucker, 1999: 47).

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However, did the Internet truly create something totally new, or did it just replace distribution channels and supply chains we already had with some- thing that was more efficient? If the later, then did valuations of Internet tech- nology look at the value of the options that people already possessed and that the dot-coms assumed that they would abandon to use the new technology? If not, then this could explain why the bubble burst.

While this paper has examined the issues from the viewpoint of IT, is IT really any different from other technologies in this regard? Other technolo- gies create real options for their use as well. For example, biotechnology is being used to create new treatments for diseases. However, we already have treatments for many of these diseases. The newer biotechnology-based treat- ments may be more effective, produce fewer side effects, etc. But, we still have options as to whether we adopt these new treatments or not. Could bio- technology be headed towards the same kind of bubble and subsequent col- lapse as we experienced with the Internet?

Carlota Perez has analyzed major technological revolutions since the 1770s. These include the industrial revolution (1771-1829), the age of steam and railways (1829-1873), the age of steel, electricity, and heavy engineering (1875-1918), the age of oil, automobiles, and mass production (1908-1974), and the age of information and telecommunicat ions (1971-20??). She has concluded that they follow a pattern:

1. An irruption phase where the new technology makes its first "big bang" appearance. 2. A frenzy phase of intense financial investment in the new technology. 3. A crash when the frenzy bubble bursts. The crash is followed by a recession and

ultimately by a turning point. 4. A synergy phase of economic prosperity after the turning point that may be consid-

ered a golden age. 5. A maturity phase in which markets are saturating and conditions become ripe for

the irruption of the next technology (Perez, 2002: 47-57).

The frenzy phase and its subsequent collapse are consistent with our basic observations and conclusions. If potential investors utilize DCF to compute the marginal value of the new technology, then they could be led to believe that this value is higher than it really is. Likewise, if they adopt real option theory but only consider the value of the new options created by the technol- ogy and do not discount them by the value of the options that must be aban- doned, then they could also be led to unrealistically high valuations.

Ex Post Measurement of Technology Value

If we are going to manage technology, we not only need to be able to be able to predict its value ex ante, we also need to be able to measure its value ex

pos t . Did its implementation really create the value we expected? It would be easy if this value showed up directly in the economic statistics

and corporate financial statements. However, as our discussion of the IT pro- ductivity paradox points out, that is not the case. Much of the value is intan- gible. Thus, we need to be able to measure intangible assets.

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The simplest and most obvious method of computing intangible value for a publicly traded company is to simply subtract its book value from its market value. However, this assumes that the capital markets always value a com- pany at the proper price. This is unlikely to be true (Gu and Lev, 2001: 3). Hence we need a better approach.

The following sub-paragraphs discuss two methods of measuring the intan- gible assets of a company. The first was developed by Professor Baruch Lev of New York University. The other was developed by Paul Strassmann who was mentioned earlier during the discussion of the IT productivity paradox. We then compare these two methods in the context of measuring intangible assets created by technology investments.

Lev's Intangible Assets Algorithm

Lev's method (Gu and Lev, 2001: 6-7) is based on the assumption that the economic performance of a company (i.e., net earnings) is based on a produc- tion function of the following form:

Economic Performance = ~Physical Assets) + 13(Financial Assets) + ~i(Intangible Assets)

The algorithm starts by defining "economic performance" in this function as annual normalized earnings. Normalized earnings are computed as a weighted average of 3-5 years of reported historical earnings and the same number of years of expected future earnings as forecast by financial analysts. Future earnings are used because many intangible assets do not contribute to current earnings but are expected to contribute to future earnings.

Lev then uses economic studies and analysis to estimate the average contri- butions of physical and financial assets to income, the ~ and [3 in the produc- tion function. For some analysis, he uses economy wide averages of 7 percent for physical assets and 4.5 percent for financial assets. He then subtracts the contribution of physical and financial assets from the normalized income. The residual is called the intangibles-driven earnings (IDE).

Lev then forecasts a future series of IDE using a three-stage model. Future years 1-5 use financial analysts' long-term growth forecasts; years 6-10, lin- early converge the forecasts down to the long-term growth of the economy-- 3 percent; and years 11 to infinity utilize a 3 percent growth rate--the expected long-term growth rate of the economy.

The NPV of the future IDE series, using a discount rate that reflects the above-average riskiness of these earnings, yields an estimate of intangible assets.

(Note: Professor Lev has a patent pending for this algorithm.) Essentially, this approach measures the intangible assets possessed by a

company by observing the normalized earnings and subtracting off the amount of that income that should be generated by physical and financial assets. The remaining earnings are assumed to be generated by intangible assets. The intangibles-driven earnings are then projected forward. The present value of

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the total projected intangibles-driven earnings stream is taken as the value of the intangibles possessed by the company.

Strassmann's Knowledge Capital tm Algorithm

Paul Strassmann's algorithm is somewhat simpler than Lev's. Strassmann computes Economic Value Added (EVA) as the amount of :income that is at- tributable to intangible assets using the following formula (Strassmann, 1999: 39):

EVA = Profit - Cost of Capital(Total Assets - Total Liabilities) Where:

Profit = Accounting profits after taxes and before preferred dividends

Or, equivalently:

EVA = Profit - Cost of Capital(Total Shareholder Equity)

(Note: This differs from the typical definition of EVA or residual income found in accounting books which only accounts for net operational income after taxes and the operating capital applied to produce that income.)

Once we have c o m p u t e d EVA, we can c o m p u t e k n o w l e d g e capital (Strassmann, 1998):

Knowledge Capital tm= EVA / Cost of Capital

(Note: Knowledge Capital is a trademark of Strassmann, Inc.) With a little algebra, we can rearrange Strassmann's equations to the same

format as the productivity equation:

Profit = Cost of Capital(Total Shareholder Equity) + Cost of Capital(Knowledge Capital)

Comparison of Algorithms

Strassmann's algorithm is conceptually similar to Lev's with two major dif- ferences. First, Strassmann assumes that c~ = 13 = ~i = Cost of Capital (i.e., WACC) in the productivity equation where Lev assumes that these variables can have different values. Second, Lev incorporates forecasts of future earn- ings into the algorithm where Strassmann only utilizes current, and possibly historical, earnings. The fact that Lev's algorithm uses forecasts of future earn- ings also means that it includes some level of subjectivity. Since Strassmann's algorithm only depends on historical financial figures, it is more objective.

It is interesting to also look at these algorithms in the light of real options theory. F. Peter Boer, a proponent of real options theory, has defined two types of capital that companies may possess: economic capital and strategic capital. Economic capital or assets are defined as the assets that contribute to

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current cash flow. Strategic capital or assets are non-economic assets that are put in place for strategic reasons such as to support future growth but do not contribute to current cash flow. Strategic assets are essentially real options and should be valuated using option methods. Economic assets support cur- rent operations and can be valuated using DCE Economic capital can be con- verted to strategic capital by purchasing real options. Likewise, strategic capital can be converted to economic capital by exercising the real options (Boer, 2002: 69-77).

The division between economic and strategic capital or assets is not the same as the division between tangible and intangible capital or assets. For example, a patent is an intangible asset. Yet, it can be either an economic asset, if it is contributing to current cash flow since products based on the patent have reached the market, or a strategic asset, if it is not yet contributing to cash flow since products based on the patent have not yet reached the market. Thus, intangible assets may be either economic or strategic assets.

If we apply the concepts of strategic and economic capital to Lev's and Strassmann's algorithms, we see that Strassmann's algorithm does not incor- porate the concept of strategic intangible capital since it is based strictly on current or past earnings. It can only measure economic intangible capital. Since Lev's algorithm does incorporate a forecast of future earnings, it at least has the potential of incorporating both economic and strategic intangible capital. Thus, we judge Lev's algorithm to be more comprehensive than Strassmann's. However, as we noted earlier, it is more subjective than Strassmann's.

Both algorithms are valiant and laudable attempts to solve a very difficult problem. Both algorithms can be used to estimate the value of the intangible assets possessed by a company. They can be used to see if a company is creating or destroying intangible assets by observing trends in the intangible asset valuation over time. They can be used to compare how well individual companies are managing their intangible assets in an industry. However, they only provide an aggregate picture of the intangible assets possessed by a firm. Neither algorithm can tie the intangible assets possessed by a company back to the individual investments that created the assets in the first place. It may be possible to use regression analysis to detect correlations in investments in technology and other intangibles and changes in the total value of intangible assets possessed by the company. But this is a long way from determining true cause and effect relationships of individual investments on intangible value.

Neither algorithm is adequate to measure the intangible value that is cre- ated by individual technology investments. However, it may be possible to augment these algorithms by including real options analysis to compute the value of intangible strategic assets. This may be a fruitful area for further research.

Conclusion

This paper has examined the issues in developing an e x a n t e estimate of the value of a technology investment and in measuring the e x p o s t value that has been created. In both areas, the intangible value created by technology is a

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key concept. We must improve techniques for both predicting and measuring the intangible value created if we are going to effectively manage technology investments.

Real option analysis provides a potential framework that can augment tra- ditional DCF analysis to estimate value. While we have not presented a defini- tive proof, there is at least enough circumstantial evidence to suggest that real options are a valid approach. We have illustrated that real options may com- pute a much higher value than DCF when implementing a technology that enables a truly new business process. However, real options may produce a substantially lower value when computing the marginal value of how a new technology may improve an existing business process. This is consistent with the over-exuberant investment we see during technology investment bubbles.

Intangible asset accounting procedures need to improve before we can ad- equately measure the ex post intangible value created by a technology invest- ment. Baruch Lev and Paul Strassmann have at least begun to develop approaches for measuring intangible assets at an aggregate level. These algo- rithms provide a basis upon which we can build. However, considerable work remains to develop approaches that can measure the intangible returns from individual technology investments.

Note

The author would like to thank Professor Rias van Wyk of the University of Minnesota for reviewing this manuscript and for his encouragement in pursuing this line of research.

References

The 1997 Nobel Prize in Economics. <http://www.ams.org/new-in-math/nobe11997econ.html.> Benaroch, M. (2002). Managing Information Technology Investment Risk: A Real Options Perspec-

tive. Syracuse University. <http://sominfo.syr.edu/facstaff/mbenaroc/PAPERS/jmis/jmis-1.pdf.> Boer, EP. (2002). The Real Options Solution: Finding Value in a High-Risk World. New York, John

Wiley & Sons. Brynjolfsson, E. and Hitt, L.M. (1998). Beyond the Productivity Paradox: Computers Are the Cata-

lyst for Bigger Changes. MIT Sloan School and University of Pennsylvania. <http:// grace.wharton.upenn.edu/~lhitt/bpp.pdf.>

Brynjolfsson, E. and Yang, S. (1999). The Intangible Costs and Benefits of Computer Investments: Evidence from the Financial Markets. Cambridge: MIT Sloan School. <http://ebusiness.mit.edu/ erik/ITQ00-11-25.pdf.>

Cox, J.C., Ross, S.A., and Rubinstein, M. (1979). Option Pricing: A Simplified Approach. <http:// www.in-the-money.com/artandpap/Option%20Pricing%20-20A%20Simplified%20 Approach.doc.>

Dai, Q., Kauffman, R.J., and March, S.T. (2000). Analyzing Investments in Object-OrientedMicMleware: An Options Perspective. Minneapolis: University of Minnesota. <http://misrc.umn.edu/wpaper/ WorkingPapers/misrc_dkm itm fullpaper.pdf.>

Devaraj, S. and Kohli, R. (2002). The IT Payoff. New York: Prentice Hall. Drucker, P.F. (1999). "Beyond the Information Revolution." The Atlantic Monthly, October 1999. Foley, S. and Mahmood, T. (1996). Wal-Mart Stores, Inc. Harvard Business School Case Study 9-

794-024. Gordon, R.J. (2002). Hi-Tech Innovation and Productivity Growth: Does' Supply Create Its Own

Demand? Northwestern University, <http://faculty-web.at.northwestern.edu/economics/gordon/ NBERPaper.pdf>

Page 21: Full Text

64 Knowledge, Technology, & Policy / Fall 2004-Winter 2005

Gu, F. and Lev, B. (2001). Intangible Assets: Measurement, Drivers, Usefulness. <http:// pages.stem.nyu.edu/-blev/intangible-assets.doc>

Gwartney, J.D., Stroup, R.L., and Sobel, R.S. (2000). Macroeconomics: Private and Public Choice. Fort Worth: The Dryden Press.

Kambal, A., Henderson, J., and Mohsenzadeh, H. (1992). Strategic Management of Information Technology Investments: An Options Perspective, <http://pages.stem.nyu.edu/~akambil/publica- tions/option~ l.pdf.>

Kulatilaka, N., B alasubramanian, P., and Storck, J. (1996). Managing Information Technology Invest- ments: A Capability-based Real Options Approach. Boston: Boston University. http:// management.bu.edu/pdf/ITvalue.pdf.

Kumar, R.L. DSS Value and lime-Constrained Decision Making. Charlotte: University of NC at Charlotte. http://hsb.baylor.edu/ramsowner/ais.ac.97/papers/kumar.htm.

Lev, B. (2001). Intangibles: Management, Measurement, and Reporting. Washington: Brookings Institution Press.

Perez, C. (2002). Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages. Cheltenham: Edward Elgar Publishing Limited.

Richardson, V.J. and Zmud, R.J. (2002). The Value Relevance of lnformation Technology Investment Announcements: Incorporating Industry Strategic IT Role. Proceedings of the 35th Hawaii Inter- national Conference on System Science. IEEE Computer Society. <http://dlib2.computer.org/ conferen/hicss/1435/pdf/14350216.pdf.>

Strassmann, P.A. (1997). The Squandered Computer. New Canaan: The Information Economics Press.

Strassmann, P.A. (1998). The Value of Knowledge Capital. <http://www.strassmann.com/pubs/ valuekc>

Strassmann, P.A. (1999). Information Productivity: Assessing the Information Management Costs of US Industrial Corporations. New Canaan: The Information Economics Press.

Van Wyk, R.J. (1999). Technology and the Corporate Board. Minneapolis: University of Minnesota.