Benaroch Kauffman 2000

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Benaroch & Kauffman/Electronic Banking & Real Options Analysis

Qarterly RESEARCH ARTICLEJUST IFY ING ELECTRONIC BANKINGNETWORK EXPANSION USING REALOPTIONS ANALYSIS^By: Michel BenarochSchool of ManagementSyracuse UniversitySyracu se, NY 13244-2130

[email protected] J. KauffmanCarlson School of ManagementUniversity of MinnesotaMinneapolis, MN [email protected]

AbstractThe applicatior} of real optioris ar}alysis toinformation technology investment evaluationproblems recently has been proposed in the ISliterature (Ch alasani et al. 1997; Dos Santos1991; Kam bil et at. 1993; Kum ar 1996: Taudes1998). The research reported on in this paperillustrates the value of applying real optionsanalysis in the context of a case study involvingthe deployment of point-of-sale (POS) debitservices by the Yanke e 24 sha red electronicbanking network of New E ngland. In the course of

so doing, the paper also attempts to operation-alize real options analysis concepts by examiningclaimed strengths of this ana lysis approach andbalancing them against methodological difficultiesthat this approach is believed to involve. Theresearch employs a version of the Black-Schoiesoption pricing model that is adjusted for risk-averse investors, showing how it is possible toobtain reliable values fo r Yankee 24's "investmenttiming option, "even in the absence of a market toprice it. To gather evidence for the existence ofthe timing option, basic scenario assumptions,and the parameters of the adjusted Btack-Scholesmod el, a structured interview format w as devel-oped. The results obtained using real optionsanalysis enabled the network's senior manage-men t to identify conditions fo r which entry into thePO S debit market wo uld be profitable. Theseresults also indicated that, in the absence offormal evaluation of the timing o ption, traditionalapproaches for evaluating information technologyinvestments would have produced the wrongrecommendations.

Keywords: Black-Scholes model, investmentdecision making under uncertainty, electronicbanking networks, POS debit systems, projectinvestments, IT investment evaluation, option


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When you make an initial investment ina research project, you are paying anentry fee for a right. ...To me, all businessdecisions are options. J . Lewent, CFO Merck & Co.,in a Harvard Business Review

interview {Nichols 1994)./ have b ecome convir)ced that it is timeto revisit the usefulness of NPV and toreconsider ust how much stock we wantto place in it.,. . For most investments, theusefulness of the NPV rule is severelylimited. If modern finance is to ha ve apractical and salutary impact on invest-ment decision making, it is now obligatedto treat all major investment decisions asoption pricing problems.

Stephen Ross, Yale UniversitySterling Professor of Economicsand Finance, in a keynote speech

to the 1994 Financial ManagementAssociation Annual Meeting

IntroductionThe application of option pricing models (0PM) toinformation technology (IT) investment evaluationproblems recently has been proposed in theinformation systems (IS) literature (Chalasani etal . 1997; Dos Santos 1991; Kambil et al. 1993;Kumar 1996; Taudes 1998). These papers makea strong case for new methods, in addition totraditional net present value (NPV) or discountedcash flow (DCF) approaches, and especially inlieu of leaving hard decisions that seniormanagers face regarding IT investment to exper-ienced intuition, Benaroch and Kauffman (1999)are the first to follow up on these proposals. Theyexamine the theoretical basis for applying OPMsto IT investment evaluation as we ll as the range ofevaluation situations where various OPMs can beapplied in light of their underlying assumptions.Moreover, they illustrate the feasibility of using aspecific 0PM. the Black-Scholes model, to

Yet, to date there has not been a study ttiat trulytests the claimed strengths of OPMs in the contextof IT evaluation problems while balancing thesestrengths against the methodological difficultiesthat OPMs are believed to involve, Ttie need forsucti a study is fueled by the expansion of work onreal options along two fronts. On one front, thebusiness world started to seriously attempt toapply OPMs. For example. In a Harvard BusinessReview interview, the Ctiief Financial Officer ofMerck & Co., discusses ways her firm evaluatesresearcti and development projects intended toyield new drugs by applying OPMs to abandon-ment, growth, and investment staging optionsembedded in these projects (Nichols 1994).Trigeorgis (1996) provides other examples of howthese models are applied to real-world businessinvestments, including natural resource miningprojects involving deferral, abandonment, andexpansion options.

Along anottier front, recent em pirical studies havebegun providing evidence in favor of using OPMs,In a survey of how financial officers deal withflexibility in capital appraisal. Busby and Pitts(1997, p. 169) found that "very few decisionmakers seemed to be aware of real optionresearch but, mostly, their intuitions agreed withthe qualitative prescriptions of such work." Axeland Howell (1996) offer stronger results based ona laboratory study with 82 experienced managersfrom large British companies. The study foundthat managers unaided by OPMs tended toovervalue real options, although their valuationsdid not differ significantly from those produced bythese models. While this study suggests thatmanagers can decide in a manner analogous toOPMs without having learned these models, italso shows that the least overvaluation tendencywas among managers from the oil and pharma-ceutical industries, two industries already usingreal option models in capital budgeting. Overall,the study indicates that OPMs are adequate forformalizing managers' intuition and that familiaritywith these models can improve the valuation ofinvestments involving options.


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Benaroch & Kauffman/Electronic Banking & R eal Options Analysis

We present the case study details behindYankee 24's IT investment in POS debitservices (the example in Benaroch andKauffman 1999). describe the structuredinterview used to obtain from Yankee 24'ssenior management evidence that enabled usto analyze this investment from a real optionsperspective, and subsequently use the analy-sis results to offer case study insights specificto electronic banking service deploymentdecision making.We put lo a real test the claimed strengthsand weaknesses of the Black-Scholes modelto show the pragmatic value of applying thismodel to realistic IT investment evaluationproblems. We specifically focus on two traitsof the model. One trait concerns the inves-tor's risk preferences assumed. In our earlierpaper, we explained the economic basis forthe risk-neutral valuation (defined later) of theBlack-Scholes model being valid in thecontext of IT investments embedding options.even in the absence of a market for ITinvestments. Yet, some researchers andpractitioners con tinue to claim that this modelwould tend to overvalue options becausedecision makers are usually risk-averse.Subsequently, we investigate the extent towhich this claim applies to the analysisresults that the Black-Scholes modelproduces for our case by adjusting theseresults for risk-averse investors. The secondtrait pertains to sensitivity analysis. We pre-sented the Black-Scholes' partial derivativesas a powerful sensitivity analysis tool(Benaroch and Kauffman 1999). We scru-tinize this claim in the context of our case,showing that the use of partial derivativeanalysis must be largely supplemented by theuse of conventional simulation-based sensi-tivity analysis.

We examine methodological issues involvedin using OPM s. We discuss factors that mustbe carefully analyzed before an IT investmentdecision like the one we study can be cast asa real options analysis problem (what kind of

option's underlying asset) involves majordifficulties and thus present practical guide-lines that can help to alleviate those claimeddifficulties.The rest of this paper is organized as follows. Thenext section introduces the fundamentals of OPMsand then explains why these models can beapplied to IT investments embedding options.The third section discusses preliminary case studydetails that enable the reader to understand thenature of Yankee 24 's IT investment decision andthe need to apply real options analysis to thisdecision. We then analyze Yankee 24's invest-ment decision from a real options perspective andoutline the structured interview we conducted withYankee 24's senior executives in order to obtaindetails for framing the decision as a real deferraloption and to elicit parameter values for the 0 PMused. The analysis results are presen ted and weexamine the ability of partial derivative analysisconcepts to deliver useful investment decisionmaking guidance. Finally, a retrospective inter-pretation is offered of why the recommendationsthat our real options analysis yielded would havebeen well suited to what actually happened inYankee 24's markets. The paper concludes witha discussion of the primary contributions of thisresearch and revisits some methodo logical issuesthat warrant additional investigation.

Pric ing Real ITInvestment OptionsWe next review the concepts underlying realoptions analysis and the fundamental models foranalyzing project investment decisions involvingreal options. We also discuss the economicrationale underlying the use of option pricingmodels for the evaluation of IT investmentsembedding real options.

Value of Manage rial Flexibility andProject Evaluation Methods


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Benaroch & KauffmarT/Electronic Banking & Real Options Analysis

that offers management the flexibility to takeactions which can change traits of the project overtime (Dixit and Pindyck 1994). The term flexibilityis "nothing more (or less) than a description of theoptions made available to management as part ofthe project" (Mason and Merton 1985, p. 32). Thisflexibility adds value to the passive NPV of aproject, where one has assum ed that no in-projectactions are possible to affect its expected valueoutcomes. It changes the probability distributionof project payoffs asymmetrically by enhancingthe upside potential or reducing the downside risk.This corresponds to the notion of an active NP V,whose expected value trajectory is controllable bymanagement. Figure 1 illustrates these changesand provides examples of specific real optionsthat cause them. Real options offering in-projectflexibility are termed operaf/ngopf/ons. They differfrom so-called growth options, whose value stemsfrom future investment opportunities that theyopen up. For more background information onreal options from the capita! budgeting literature,the reader is referred to Amram and Kulatilaka(1999). Dixit and Pindyck (1994), and Trigeorgis(1996).Two approaches commonly used to evaluateinvestments are DCF (NPV) analysis and decisiontree analysis (DTA) (see Figure 2). In addition tothe theoretical reasons for these approachesbeing inadequate for investments involvingoptions (Benaroch and Kauffman 1999), apragmatic question is; Why can't they be adaptedto such investments?The key problem with adapting DCF analysis Isthat it can evaluate only the actual cash flows aproject is expected to yield. DCF analysis doesnot explicitly recognize that managerial flexibilityhas a value equivalent to a "shadow," non-actualcash flow. Such flexibility is borne by thepresence of embedded options and it allowsmanagement to adjust traits of the investment(timing, scope, scale, etc.) to changing environ-mental cond itions. Even if DCF analysis were to

changes as a function of time and the uncertainsize of actual cash flows, it is neither possible topredict the option risk nor find a risk-adjusteddiscount rate that applies to it.DTA provides a significant conceptual improve-ment over the way DCF analysis handles options.A decision tree shows the expected projectpayoffs contingent on future in-project actions thatmanagement can take over time (e.g., abandonan operational project at time t, if the salvagevalue of resources used exceeds the payoffsarriving after () As the tree represents eachaction as a decision node, corresponding to anoption, evaluating the project requires workingbackward from the future to the present tocalculate how m uch the presence of these actionsadds to the project value. This approach yieldsuseful results only after poor tree branches arepruned. Pruning means finding out how em-bedded options alter the range of expectedpayoffs and then adjusting the discount rate torecognize the change in risk (or variability ofpayoffs). Unfortunately, DTA provides no directbasis for discount rate adjustment (Brealey andMyers 1988, p. 228). Only with a proper modi-fication involving an estimation of the investor's(management) utility function can DTA beadequately applied to projects embedding options(for details, see Smith and Nau 1995).

Real options analysis strives to complement theother two approaches, in light of the difficultiesinvolved in adapting these approaches to invest-ments embedding options. It looks at the activeNPV of a project as the sum of the passive NPVand the value of embedded options. The intuitionbehind how it evaluates an embedded optionresides in two factors. First, it models payoff con -tingencies using a probability distribution function(e.g., log-norma l, binomial), enabling it to translatethe presence of an option into expectations ofshifts in this distribution. Second, it replaces theactual probabilities of payoffs by risk-neutra


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ExamplesEXPAND: abiity to scale up a project investment whenmarVet demand for producls or services produced by ^ eproject appears lo be buildingDEFERRAL deferring a decision lo Invest in one IT instead

o( anottier (e.g.. Windows s. OS/2) may allow buyinginto emerging standards

ExamplesABANDONMENT: ability to abandon an ongoing projectinveslment and lo direct saNageable resources toalternate more vakiat>le usesDEFERR AL: atji l ity lo defer a project until a new tesscostly lectinology is proven feasible, without losing theinvestment opportunityLEASING: abiity to oulsource a project in order tot r ans fe r ' the risk of protect faMure

probability distritwtkxi of the passivo NP Vprobabtity distribution of ttw adive NP V

contrfbutton of tttereal optionOption enhances the upside po lenlial by possitjiy open ingfuture project investment opportunities, thus pus hingupwards the right tail of the probability distribution of ttieinvestment outctwne

Option reduces the dow nside potential t jy possiblytowering the project's cost and/or failure risk, thus pushingdownwards the left tail of Ihe prob abiity distribution ofthe investmeni outcome

Figure 1. Asymmetry of the Probability Distribution of Project Payoffs WhenReal Operating Options Are Involved

Goal:EvahiatB a protact investmentembedding a real opt ion, by ico fpo ra l ing k i fo rma ibn abou tthe asjnvnet i ic d istr ibut io i i o fexpected psyoRs.

Evaluation ApproachDiscounted cash flowand N PV analysis

prvbabUt

Cont r fcu tbn ofshadojv cash fbw

Workings and ConcernsCalculate tt ie pro|ecrs NPV baaad on actual sxpacle d cash l lows tn d anappfophate r isl i-ad|usted dscount la teDCF and NPV methods do not sea IM jhw Jow ' cash f low borne by wiBmbatJtted option (n-p rojac t flexlbHtty} Ev n i tt iesa metttods were to tieBdjusted, no single i is l i-ad juslK] dsc oun t rate could t> apphed lo Ihs'shadow " cash How tMcaus a it changes as a (unct ion o l tm e and t t ieuncaiia in nature of acbia l cash rtows a ipe cted from the

DBclslon tree analysis

itacM in nodt (cfKbn)

Evatuata the btarKhes, prune unattract ive bfanches. and calculata thaprojed 's act lvB NPV tsed on the remain ing txanct iesOnce Iha t re has ben pntnad. H s hard to tind a proper risK-adjusteddiscount rata that ref lect i Iha changa in t M dispersion (vananca) ofoutcoma.

Option pricing analysis Calculats tha passiva N PV using a d iscount ra le that ignores tha upsidapotent ia l, and add to It tt ie value co ntrbutad by tne embedded op lnn .A difficulty ts m choo shg tha approp r iata op tbn fxtang modal to matcntt>e key elements of tt ie investm ent balng analyzed An d. wtiichavar Is tnechosen nnodel, ( Is necessary lo identify the antant to which key


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Benarocti & Kauffman/Electronic Banking & Real Qptions Analysis

sion tree without having to worry about discountrate adjustment.^ However, these factors raisetwo issues. The first requires estimating thevariability of uncertain payoffs and costs modeledusing probab ility distributions. As to the otherfactor, the validity of discounting by the risk-freerate is questionable when options are not tradedin a market. We return to these issues later, toshow that they do not limit the applicability of realoptions analysis to IT options.In the rest of this section, we formalize real optionpricing concepts based on prior work in finance.We focus in particular on deferral options becausethe case study we present in later sectionsinvolves a deferral option.

Option Pricing Concepts Appliedto Real Deferral Op tionsThe fundamental options are financial calls andputs. A European call (put) on some underlyingasset, whose current value is V, gives its holderthe right to buy (sell) the asset for an agreedexercise price, X , at a ixedexpiration date, T. Forinstance, a "June 99 call" on IBM stock with a $75strike price allows its holder to buy IBM shares for$75 on June 15, 1999. This call is worth exer-cising only if the value of an IBM share on June 15exceeds $75, in which case it is said to be irt-the-money- Thus, the terminal value of a call, or itsvalue on expiration, Cj, is max(O, V^-X), whereVj is the terminal value of the underlying asset.An American option is like a European option , butit can be exercised at any time t, t < T. We firstfocus on European calls because they are simplerto understand, and later return to discussAmerican options.The current value of a call, C, is partially deter-mined by the vo latility (variability) of the underlying

^In this sense, real options analysis is an adjustedversion of decision tree analysis, involving a redistri-

asset's value, a, and the length of time to itsmaturity, T. Before the option expires, V can godown only to zero (downside risk limit) or up toinfinity (unlimited upside poten tial). This asym-metrical distribution of V means that, the higher ois, the greater is the chance that V,.will exceed Xfor the call to end in-the-money and the higher isthe call value. Likewise, the longer is the time toexpiration, T. the more chance there is that Vwilrise above X, so that the call will end in-the-money. So far we see that C depends on para-meters V, X, T, and o. We will see that C alsodepends on the risk-free interest rate.

For a firm facing a project embedding the right todefer investment, the analogy w ith a financial callIs direct. The firm can get the value of the opera-tional project via imm ediate investment, V- X, orhold on to the investment opportunity. This is akinto a call option to convert the opportunity into anoperational project. The option (opportunity)offers the flexibility to defer conversion until cir-cumstances turn m ost favorable, or to back out ifthey are not satisfactory. Its value corresponds tothe active NPV. equaling the passive NPV plusthe value of the deferral flexibility. The optionparameters are (1} the time to expiration, T. is thetime that the oppo rtunity can be deferred ; (2) theunderlying asset. V, is the present value of riskypayoffs expected upon undertaking the invest-ment; (3) the exercise price, X, is the irreversiblecost of making the investment; and (4) thevolatility, o, is the standard deviation of riskypayoffs from the investment. When V ca nfluctuate, the unexercised option (opportunity) canbe more valuable than immediate investment,max(\/ - X) > V ~ X. The value of the optiondepends on how much the decision makerexpects to learn about the way the value of riskypayoffs, V, will evolve due to changes that m ighoccur within the firm or in its environment duringdeferral. The more uncertain is V, the morelearning can take place during deferral, and themore valuable is the option. This is consistentwith what finance theory postulates about theeffect of 0, the variability of V. on the value offinancial options.


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assumptions and thus compute a similar optionvalue for options maturing in a year or longer(Benaroch and Kauffman 1999), we rely here onlyon the Black-Scholes model to price the optionidentified later in our case study. The Black-Scholes model js a closed-form formula thatcomputes the price of a European call option fo rarisk-neutral investor.'' It is written as

C =

in(VYX) ' [r, oV2)7o/T

d, - (1)where N () is the cumulative normal distribution, Vis an underlying asset that is assumed to be log-normally distributed so as to reflect the asym-metric nature of payoffs from an investmentembedd ing the option (Figure 1), o is the volatilityof V, X is the option's exercise price, T is the timeto maturity, and r,is the risk-free rate. This equa-tion has a simple intuition. As Vj- X is the call'sterminal in-the-money value, v'-e"'''^X 's thecurrent in-the-money value. To cover the casethat the call might be unattractive to exercise, Vand X are weighted by the probabilities N(rf,) andN(d2), respectively.In light of the similarity of a deferral option to afinancial option, we should be able to apply theBlack-Scholes model to real IT options. Benarochand Kauffman support this assertion by showingthat the economic rational for the risk-neutralityassumption of the Black-Scholes m odel fits in the

The Black-Scholes model assumes that the option ispriced for a risk-neutral investor (who is indifferentbetween an investment with a certain rate of retum andan investment with an uncertain rate of return whoseexpected value matches that of the investment with thecertain rate of retum). Underlying this assumption is arequirement that Vbe an asset that is traded in a market

context of IT investment evaluation, even thoughmany IT investments are not traded. However,recall that one goal of this paper is to examine theimpact of adjusting the risk-neutral option valuecalculated by this model to the case of risk-averseinvestors. This examination is meant to addressthe claim that, because most decision makers arerisk-averse, risk-neutral valuation overvaluesoptions embedded in non-traded investments.Trigeorgis explains this claim as follows: Mana-gers evaluating an investment that is subject to afirm- and/or industry-specific risk not shared by allmarket investors must discount the option valueby a factor corresponding to the investment'sunique risk (p- 101). Ana logously, if the assetunderlying an option is not traded in limited supp lyby a large number of investors (so that demandfor the asset exceeds supply), the asset's returnrate, a, may fall below the equilibrium expectedrate of return investors require from an equivalent-risk traded asset, a* . The rate of retum shortfall,5 = a* - a , necessitates an adjustment in theoption valuation. A version of the Black-Scholesmodel that reflects this rate shortfall adjustment is

In(WX) * (r,-5a/t

d\ = d', - (1')A simple conclusion follows- Risk-neutral valua-tion does not pose a roadblock to implementingreal options analysis using the Black-Scholesmodel. Even for a non-traded underlying asset,we can apply risk-neutral valuation using theBlack-Scholes model adjusted by an appropriaterate of return shortfall, 6.'' Following one of ourgoals, we later check the impact of adjusting theBlack-Schoies model by 6 on the analysis resultsfor the case study presented sho rtly.


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Option-Based Decision Ruiefor Investment TimingHaving seen why it is reasonable to use the Black-Scholes model in tbe context of real IT options,the question that a firm must answer for adeferrable investment opportunity is; How long dowe postpone the investment up to T time periods?Economists study many variants of this kind ofinvestment-timing problem (e.g., cyclical demandfor goods to be produced by a deferrable project).They use different specialized solution ap-proaches, many of which are isomorphic to theoption pricing approach (Bernanke 1983). Forexample. McDonald and Siegel (1986) study theproblem for the case of stochastic project costs,showing that, under risk-neutrality and non-stochastic project costs, their model reduces tothe B lack-Scholes model. Likewise, Smit Han andAnkum (1993) say that the general investment-timing problem '7s analogous to the timing ofexercising of a call option" (p. 242), and thusexplain how the simplicity and clarity of real op-tions analysis enables them to study the problemunder various competitive market structures.From a real options perspective, the intuitionbehind the evaluation principle for solving aninvestmen t-timing problem like the one we presentshortly is as follows. Holding a deferrable invest-ment opportunity is equivalent to holding anAmerican call option. At any moment, the investorcan own either the option (investment opportunity)or the asset obtained upon exercising the option(operational investment). The option parametersare the present value of risky payoffs from theinvestment (V), the cost of making the investment(X), the standard deviation of risky payoffs (o ), themaximum deferral period (7), and the risk-freeinterest rate (r,). Holding the option unexercised(postponing investment) for time / has tv*/ocompe ting effects: V is lowered by the amount offoregone cash flows and market share lost tocompetition and X is lowered because it isdiscounted during the deferral period, t.

exceed original estimates, investment can bejustified by the rise in the payoff expected frominvesting; otherwise, the irreversible sunk cost (X)can be avoided by not investing, at a loss of onlythe cost of obtaining the deferral flexibility.Consequently, the following decision rule leads tothe optimal investment strategy, given today'sinformation set.

Decision Rule: Where the maximumdeferral time is T, make the investment(exercise the option) at time f, 0 : t*< T.for which the option, C,., is positive an dtakes on itsmaximum value.

C,, = max1-0 T

- Xe-'''U(d\).(2)where c/", and cf are defined in equation (1'), andV, equals V less the present value of foregonecash flows and market share lost tocompetition.Of course, this decision rule has to be reappliedevery time new information arrives during thedeferral period to see how the optimal investmentstrategy might change in light of the newinformation.Because the Black-Scholes model issuitable forpricing only European options, it is not directlyapplicable with a decision rule involving anAmerican deferral option . However, wewill seelater a specific variant of the B lack-Scholes modelthat can be directly applied with the abovedecision rule.

A Planning Retrospectivefor Point-of-Sale Debit atYankee 24 ^ m^ ^ ^ mIn this section, we discuss the background ofshared electronic banking services in relation toYankee 24 to pave the way for our evaluation ofan IT investment embedding a deferral option.We examine the investment scenario that Yankee


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Benaroch & Kauffman/Electronic Banking & Real Qptions Analysis

Electronic Banking andPoint-of-Saie DebitElectronic banking was instituted in the nnid-1960sto facilitate the execution of financial transactionsusing credit cards. Due to the popularity of thisservice among consumers, retailers rapidly cameto accept credit cards on an almost universalbasis. This service was followed in the early1970s by the deployment of automated tellermachines (ATMs).Later, a "middle ground" service emerged,combining the speed and ease of a credit cardtransaction for the consumer and the low risk of acredit or check-free transaction for the merchant.In 1972. First Federal Savings of Lincoln,Nebraska, was the first bank to install ATM-likedevices in supermarkets, enabling its depositorsto use plastic cards to pay in the Hinky Dinkysupermarket chain. The mechanism involved abook transfer at the bank, resulting in a debit tothe purchaser's account and a credit to themerchant's account. This service became knownas point'Of-sale (POS) debit. Hinky Dinky'sservice was not very successful because it wasconfined to First Federal Saving's depositors.Retailers simply did not want to install systemswith restricted availability to their broad spectrumof customers. LSince that time, there were more successfulattempts to establish POS debit services. Around1985, for example, four major banks in Californiacollaborated to introduce the "InterLink" paymentsystem. At the time, since these banks heldbetween 50% and 60% of all checking accounts inCalifornia, retailers, and especially supermarkets.rapidly adopted the service. Around the sametime, other shared ATM networks observed theemergence of this POS debit payment system andbegan to consider its applicability to their ownmarketplaces.

Electronic Banking and POS DebitServices in New EnglandYankee 24 (hereafter. Yankee ), a regional shared

than 200 member firms. Many member firmsdeployed their own ATM hardware and software.Others outsourced all ATM transaction processingto the network. Charges for network servicesinvolved an initial membership fee and fees for alltransactions processed through Yankee's switch.Despite its limited focus on Connecticut, by 1985Yankee became the largest shared network inNew England. Yankee subsequently expanded tothe remainder of New England, experiencing400% growth in transactions in 1987. By 1990, itsATM transaction volume had reached about 20million per mon th. Table 1 provides additionalinformation about Yankee and others among thelargest regional shared electronic banking net-works in the U,S- This information reveals fourfacts about the 1990 time frame. First, the WestCoast had the largest number of POS terminalsinstalled by STAR. Second. NYCE owned about15% of the POS terminals in the North East, butnone in the New England area. Third, Yankeehad no POS terminals installed in New England.Finally, although Yankee is small in terms of thenumber of network cards it services, this numberis still significant.

In 1987. Richard Yanak. Yankee 's president, firstconsidered supporting POS debit networkservices. Yanak's initial perception was that thisinvestment in new infrastructure was risky. ButYanak also viewed POS debit as a way forYankee to expand its franchise in the market,increase its transactions volume and revenues,and thus increase the network's value to itsmember firms. In addition, one potential newbusiness of interest was applying the POS debitpayment system to the electronic distribution offood stamps and a host of government welfarebenefits.Given the strategic nature of a move into POSdebit. Yanak began bu ilding a business case thatwould convince the board of directors to under-take this project. In Yanak 's initial view, enteringthis market seemed workable because of itssimilarity to the ATM market, which was wellunderstood by the board of directors. Bothmarkets have resulted from societal change inconsumer payments mechanisms and the trainingconcerns and technological infrastructure em-


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Benaroch & Kauffman/Electroriic Banking & Real Options Analysis

Additional Sensitivity AnaiysisOur next step was to analyze partial derivatives inthe context of the Biack-Scholes model to seewhat additional useful results can be obtained forYankee's situation and to assess Benaroch andKauffman's claim that these derivatives offersimple and powerful sensitivity analysis capa-bilities. These derivatives measure the sensitivityof a call option to changes in volatility (o), thevalue of the underlying investment asset {V), thecost to exercise the option (X), the option's timedecay as exp iration nears (0. and changes in therisk-free rate (r,):

vega = A = ^ , d e/fa = A = ^ ,^ 6 0 dV

XI = - = dCax' theta = e = dCat'

dr. (5)

On the positive s ide, these derivatives do help toanswer some questions regarding the effect ofchanges in m odel parameters on the value of theinvestment opportunity. For example, based onideas discussed in McGrath (1997). one questioncould be: What is the maximum pre-investmentYankee should be willing to make to ensure that owon't drop by 1% (e.g,. due to a lower chance forregulatory changes in Massachusetts in the lackof lobbying)? This question could be answeredusing i^ega, A, which tells us by how much theoption (investment opportunity) value changes asa result of a 1% change in o, the variance ofexpected revenues, in our case, (assuming (= 3,\/ = $387,166 for a New England market 25% thesize of California's, X = $400,000, and o = 50% ),A = 218,284 means that an increase in o from50% to 5 1 % increases the net value of thedeferred investment option by $2,183. (This Isconfirme dby what-if simulation results in Table 2.)We also found that additional useful results can

explain why Yankee's management consideredwaiting three years, instead of two or four years.We speculate that, after about three years, theexpected value of the underlying POS debitnetwork asset would grow more slowly than thevalue of foregone revenues in the absence ofPOS debit roll out.On the negative side, upon further probing into theuse of these derivatives in Yankee's case, weidentified twoweaknesses that make these deriva-tives of limited value. One weakness is theirability to yield valid answers only for questionsinvolving a small change in one parameter. Likewith what-if analysis, we must assume a specificanchor point (e.g., ( = 3, V = $387,166 for a NewEngland market 25% the size of Oalifornia's, X =$400,000, o = 50% , and 5 = 0% ), Now, becausethe derivatives are not linear in their variables,they provide reliable answers only in the imme-diate vicinity of this ancho r point. As Figure 6illustrates, the degree of non-linearity can varyand thus impact the size of error made based onlinear extrapolation. For a change of X from$400,000 to $500,000, extrapolation based on xi ,E, would predict a drop of $33,214 in the invest-ment opportunity value, which deviates by morethan 14% from the $29,100 drop predicted bynumeric simulation. (Note that E = -0 .3 3 m eansthat a $1 increase in the cost to enter the POSdebit market would cause only a net decline of 33cents in the investment opportunity value, asconfirmed by what-if results in Table 2.) In thecase of o, for a change from 50% to 60%, extrapo-lation based on A would predict an increase of$21,828 in the investment opportunity va lue, anddeviate by only 1.66% from the $21,472 increasepredicted by numeric simulation. However, notethat the dashed graph in Figure 6b becomeshighly non-linear under different assumed para-meter values (e.g,, 6 > 0).When it is not possible to assum e an anchor pointwith a high degree of certainty, the last obser-vation has implications on simulation-basedsensitivity analysis as well. In Yankee's situation,choosing an initial plausible value of 50% for oamounts to choosing an uncertain anchor point.


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Benarocty & Kauffman/Electronic Banking & Real Options Analysis

chnofd

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0.14-r . . 0.120 .10-0.080 . 0 6 -0 . 0 4 -0 . 0 2 -

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(a) results for X

assumed "anchor" pointbased on numericsimulation

InWar technical cost (X)

(b) results for o

-g 1180,000oc1 1 1 0 , 0 0 0

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Volatmty of revenues (o)

Figure 6. Sensitivity Analysis Results Obtained Using Derivative-BasedExtrapolation and Numeric Simulation


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Benarocti & Kauffman/Electronic Banking & Real Options Analysis

the proximity of the assumed anchor point.Indeed, in Yankee's case we found that certainparameter values lead to results that slightlydeviate from the results reported in Table 2. Forexample, compared to the eariier recom mendationreported based on the assumed anchor point (i.e.,Sigma = 50%). when X = $200,000 and 5 = 5%,the recommendation w ill no longer hold when o isbelow 15%, Overall, not being able to choose ananchor point with certainty (e,g,, due to parameterestimation difficulties) requires putting more effortinto sensitivity analysis.Another weakness of partial derivative analysis isthat it can provide answers only for parametersthat plug directly into the Black-Scholes model.For example, a question that could be reallyinteresting to Yankee is; what would happen if theassumed New England market size relative toCalifornia's market was 1 % larger? There Is noway to answer this question using derivativeanalysis. Since V depends on the market sizerelative to California, delta with a value of A =0.738 would only tell us that a $1 increase in Vcauses a net increase of 74 cents in the value ofthe investment opportunity. Some additional com-putation is needed to produce the result neces-sary to answer the above question, A somewhatsimilar observation applies to ro, p. In our case ,p = 398,567 suggests that a 1% change in therisk-free interest rate, r,. changes the investmentopportunity (option) value by $3,985, only 2,3%of its original value. However, even here we mustcaution the reader w ith respect to the reliability ofthis result. In Yankee's case, p is not useful byitself because we cannot express V (and X) asexplicit functions of r,. Since cf' depends on V,,by knowing o alone we cannot say anything abou thow C^ would change; a change in r, would alsomean changing the discount rate r(o f 12%) usedto estimate V, based on the cash flows arrivingafter entry into the POS debit market (seeFigure 4). Hence, here again, some form of what-if simulation seems m ore appropriate.

o', is the variance of the underlying asset that isconsistent w ith (or implied by) the other variables,including the observed market value of the option.Assuming that o is unknown and that all otherparameters, including the option value, are given ,one should be able to compute the Black-Scholesimplied volatility. However, when we applied thisconcept to Yankee's case (using Excel's goal-seeking capabilities), some interesting questionsarose. Specifically, by setting the investmentopportunity value to zero, we hoped to find theminimum volatility level below which deferral neednot be considered, But, to find this m inimum levelin the context of Black's approximation, should weset to zero the value of the American op tion (Cj )or the European option (Cj) and for what timepoint (, 0 < f ^ 4? Setting Cj -0 yields an impliedvolatility that we could not interpret when theoption is Am erican , By contrast, setting Cj = 0for the optimal de ferral time recomm ended (of t =3) surprisingly yielded a negative implied volatility,suggesting a possible idiosyncrasy of the Black-Scholes model with respect to computing impliedvolatility under certain parameter values. Weconcluded that the ability to calculate impliedvolatility using the Black-Scholes model is of novalue in Yankee's case (and probably other non-trivial case s).

In summary, our experience with Yankee's casesuggests that Black-Scholes' derivatives cannoteasily reproduce the results produced usingsimulation-based sensitivity analysis. Neve rthe-less, we must emphasize that even simulation-based results are obtained as an integral part ofreal options analys is. More prec isely, it is the factthat the Black-Scholes model is a closed-formformula that allows obtaining simulation-basedsensitivity analysis results with minimal effort(compared to, say, ttie binomial method). Ouroverall conclusion is that the ability of the Black-Scholes model and its variants (e,g.. Black'sapproximation) to usefully support sensitivityanalysis cannot be discarded or ignored.Finally, we checked if the Black-Scholes modelalso supports break-even analysis, following the


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is not worthwhile and that a rational recommen-dation would be to defer entry for a period of threeyears. Of course, this recomm endation is basedsolely on the information Yankee had at the timeof the analysis in 1987. Any new informationarriving with the occurrence of events or changesduring the recommended deferral period wouldrequire repeating the analysis to see whether andhow the recommendation has to be revised.What is the key benefit from using real optionsanalysis in Yankee s case? The key benefit is thatthis analysis generates reliable results, regardlessof whether the passive NPV is negative or positiveand regardless of the decision maker's assumedrisk preferences. Moreover, even if the NPVdecision rule were to be revised to choose adeferral period that maximizes the passive NPV,the results wou ld still be faulty (see footnote 5), Inthis regard, a comment is warranted regarding the5.5 years analysis horizon Yankee used . As hasbeen argued before (e.g,, Trigeorgis 1996), a firmcan almost arbitrarily choose to shorten orlengthen the analysis horizon and thus affect thesize and the sign of the passive NPV. Yankee'scase shows that real options analysis yields morereliable results independent of the exact analysishorizon cons idered. This benefit generally comesat the cost of having to estimate additionalparameters. Estimating these parameters forYankee's case did not overly complicate theanalysis, its results, or their interpretation, largelybecause real options analysis provides for aneasierderivation of meaningful sensitivity analysisresults and their interpreta tion. However, werecognize that this might not be the situation inmore complicated cases.In light of the above discussion, we feel thatapplying real options analysis to Y ankee's case iswell justified^the results of our analysis canexplain rationally the actual actions taken byYankee. Ultimately, largely based on intuition andexperience, it was decided that Yankee woulddefer entry into the market for POS debit services.Yankee made the move in 1989, hoping to havethe POS debit service operational by early 1990,and it was very successful in that regard. Yanak

had begun to occur in California's POS debitmarket. Second, Yankee's ATM business hadreached a mature stage, freeing up resources topush POS debit. Third, and most important,however, was an unexpected event in mid-1989.The Food Market Institute, the primary tradeassociation for the grocery business, released astudy that clearly demonstrated the benefits ofPOS debit transactions. The study said that forretailers the average transaction cost per sale was0.82% of the sale value for POS debit, in contrastto 1,2% for checks and 2.1% for cash . (Checksinvolve depository handling costs and risk that thewriter has insufficient funds; cash is subject tomishandling and pilfering and must be physicallymoved from the supermarket to the bank bysecure means.) The results of this study becamethe primary tool in educating retailers,^Yanak went to Yankee's board of directors, inearly 1989, arguing in favor of rapid entry intoPOS debit. Yanak's strategy was to go after thelargest 21 supermarket chains in New Englandfirst. By mid-1990, Yankee had one commitmentfrom Hannaford Brothers, one of the largestsupermarket chains, which decided to pilot theservice in nine supermarkets in Maine and NewHam pshire, It took about seven months to get thetechnology in place and the service was opera-tional in early 1991. Yankee's second m ajor sign-up was Stop & Shop, the largest conveniencestore chain in New England. Stop & Shop choseto pilot POS debit in Rhode Island in order toassist Yankee in its efforts to persuade legislatorsthat POS debit was a service in the public interest.It was hoped that this would result in a change ofthe law in Massachusetts that was a seriousinhibitor to an earlier rollout. Since then, Yankeehas been largely successful in getting the majorsupermarket retailers. In 1995 , it had about 40supermarket chains signed, out of tbe 100operating in New England.The growth has been phenomenal, from no POSdebit terminals in 1990 to about 27,000 terminalsin early 1993. That contrasts with a total of about


24/30


4.000 network ATM s, built up since 1984. Thebusiness volume grew rapidly and is expected tocontinue for the next few years. Estimates for1996 were for more than 40 million transactionsper year.

Conclusion and FutureResearchThe present paper illustrates the value of applyingreal options analysis to an IT investmentembedding a real operating option. The majorconclusion of our study is that real optionsanalysis provides a powerful complementaryapproach for evaluating real-world IT investmentslike the one in Yankee 24's case. Real optionsanalysis proved suitable for structuring seniormanagement's view of the strategic value of aninvestment involving an option, enabling a logicaland intuitive interpretation of the analysis results.Moreover, it facilitates conducting sensitivityanalysis, which helps to probe and subsequentlyto understand the nature of an investment in termsthat match the way a manager thinks about theproblem.Beyond just illustrating the value of real optionsanalysis, our study also investigated severalmethodological issues that had to beaddressed inthe context of Yankee's case. We feel that ourexperience with respect to these issues can helpto make the use of real options analysis morepractical for senior managers.One methodological issue, which arose when ourinterviewees had some difficulty expressing thevariability of expected project payoffs as a singlenumber, o, is the need to develop ways toestimate this number. In Yankee's case, insteadof precisely estimating o. we used an approachthat leads us to make our first recomm endation.

Recommendation 1: When it is difficultto obtain a precise estimate of o {e.g.,because of non-tangible benefits), start

This approach worked weli in Yankee's case,although it required putting more effort into sensi-tivity analysis (For the reasons discussed earlier).However, are there situations were this approachwill not work? Or is it possible to structure theapproach better so that it would fit a wide range ofsituations? We referred to alternative estimationschemes in Appendix B. Can such schemes leadto more useful results? If so, under what circum -stances should each scheme be used? Moregenerally, thinking of variability as just anotherword for risk brings to mind de m on s (1991), whoshowed that IT managers deal with risk of variousforms (functionality risk, project risk, market risk,etc.). Would linking the variability of expectedpayoffs to specific sources of risks present in atarget investment simplify the estimation task?Another important methodological issue weexamined pertains to the notion of risk-neutralvaluation. Since the introduction of the realoptions approach in the IS literature, the risk-neutral valuation of this approach has beencriticized as being inadequate for options on non-traded investments (e.g., Kauffman et al. 1993. p.588). Elsewhere we offered economic argum entsthat address this criticism (Benaroch and Kauff-man 1999). Here we used a version of the B lack-Scholes model that adjusts for risk-aversion bydiscounting the value of an option by the so-calledrate of return shortfall. 5. While 5 is anotherdifficult to estimate parameter, our experience inYankee's case suggests the following recom-mendation.

Recom mendation 2: If you don't sub- 'scribe to risk-neutral valuation, and thushave to estimate the rate of return short-fall, 5, first calculate a risk-neutral optionvalue using the Black-Scholes modeland then use sensitivity analysis with theadjusted Black-Scholes model to seehow robust is the option value withrespect to 0. 'In Yankee's case, even when 6 is at its upperlimit, corresponding to the case of a very risk-


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Benaroct) & Kauffman/Electror^ic Banking & Real Options Atialysis

enough to change the investment decisionsuggested by risk-neutral valuation. This conclu-sion is consistent with what the finance iiteraturepostuiates about the sensitivity of options todiscount rates.To summarize the estimation issues, a pragmaticmessage from our study is that the lack of exactparameter estimates is not always cruc iai. Onlywhen the calculated value of an investment (plusembedded options) is marginally positive areprecise parameter estimates necessary. Sensi-tivity analysis, which is always needed for real-world decision problems, is an effective way toobtain useful and reliable results in the absence ofexact parameter estimates.Relative to sensitivity analysis, another methodo-logical issue we s tudied, our experience with theBlack-Scholes model in Yankee's case suggeststhe following. Whereas partial derivative analysisseems to be of little value in supporting sens itivityanalysis, the closed-form of this model permitseasy genera tion of useful what-if sensitivity analy-sis results. This suggests the next recommen -dation.

Re com me nda tion 3: For sensitivityanaiysis purposes, it Is more useful torely on numeric, simulation-based analy-sis capabilities than on the capabilitiesassociated with Black-Scholes' partialderivatives.

We must admit that, knowing that partial deriva-tive ana lysis is much used in the investment arenaleaves us with the ques tion: ts there a way tomake partial derivative ana lysis more useful in thecontext of IT capital investments?Our experience w ith the Yankee case also helpedto surface other important methodological issuesrelevant to investments that are more complexthan the one we presented. Such investmentstypically embed multiple cascading (compound)options. For example, for some projects, it ispossible to stage the investment, and defer som e

One set of guidelines should help to recognize theoptions potentially present in an investment- Ourexperience indicates the need for a taxonomy ofreal IT options that identifies the exact assump-tions, conditions, and prerequisites underlying theexistence of each option type. Using such ataxonomy, it should be possible to develop struc-tured questionnaires that can help an analystidentify readily all of the options that might beinvolved in a given situation and obtain the evi-dence necessary to establish the existence of afew central ones.Another set of guidelines should help identifywhich of the options potentially present in aninvestment ought to be brought into existencethrough additional investment. These guidelinesmust consider that the cost of creating an option,keeping it alive, and exercising it could exceed thevalue that the option adds to the investment. Thisis especially true when the value of a compoundoption involving a series of cascading options issmaller than the sum of values of the individualoptions (for deta ils, see Trigeorg is 1996). In thissense, identifying which options are worth creatingalso requires using an option pricing model that isintuitive, flexible, and does not require managersto understand all of the mechanics of pricingcomp lex options. So far the IS literature on IToptions has examined three m odels: the binomial,the Black-Scholes. and the asset-for-assetexchange m odels. The finance literature offersother models for different types of real options(Hull 1993). In Yankee's case, the choice ofmodel was relatively straightforward. However,when the investment is more com plex, identifyingthe right model to employ requires mappingcharacteristics of the specific IT option beinganalyzed to the assumptions that each mode!makes (Benaroch and Kauffman 1999).In conclusion, we invite the reader to consider thestrengths of real options analysis in a variety of ITinvestment contexts. To this end. we illustratedhow the BlackScholes model can be applied in thecase of an IT investment option and we exploredthe power of its sensitivity analysis capabilities as


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Benaroch & Kauffman/Eiectronic Banking & Real Options Analysis

and accessible to IT practitioners andresearchers.

AcknowledgementsThe authors wish to thank Richard Yanak. pastpresident of New England Network, Inc., whichoperated the Yankee 24 electronic bankingnetwork prior to its merger with the New YorkCash Exchange and the formation of Infinet, Allerrors of fact or interpretation are the soleresponsibility of the au thors.

ReferencesAmram, M., and K ulatilaka, N. "Disciplined Deci-

sions: Aligning Strategy with the FinancialMarkets," Harvard Business Review (77:1),January-February 1999, pp. 95-104.

Axel, J. J., and Howe ll, S. D. "Evidence on HowManagers Intuitively Value Real GrowthOptions: A Laboratory Study," Working PaperSeries, Center of Business Research, Man-chester Business School, University of Man-chester, June 1996.

Benaroch, M., and Kauffman, R, J, "A Case forUsing Real Option Pricing Analysis to Evaluateinformation Technology Project Investments,"information Systems Research {10:1), 1999, pp.70-86.

Bernanke, B. S, "Irreversibility, Uncertainty, andCyclical Investment," The Quarterly Journal ofEconomics. February 1983, pp. 85-106.

Brealey, R. A., and Myers, S, C. Principies o fCorporate Finance, NY: McGraw-Hill, New York,1988.

Busby, J, S.. and Pitts, C, G, C. "Real Options inPractice: An Exploratory Survey of HowFinance Officers Deal with Flexibility in CapitalAppraisal," Management Accounting Research(8:2), 1997, pp, 169-186,

Chalasani, P, Jha, S., and Su llivan, K. "AnOptions Approach to Software Prototyping,"

Copeland, T, E., and Weston F. J. FinancialTheory and Corporate Policy. Addison-Wesley,Reading, MA, 1988.Cox, J, C, and Rubinstein, M. Options Markets.Prentice Hall, Englewood Cliffs, NJ, 1985,Dixit, A. K., and P indyck, R. S, Investment UnderUncertainty. Princeton University Press, Prince-ton, NJ, 1994.

Dos Santos, B. L. "Justifying Investment in NewInformation Technologies," Journal of Manage-ment Information Systems (7:4), Spring 1991 ,pp. 71-89.

Hull, J. C. Options, Futures, and O ther DerivativeSecurities (2"'' ed,). Prentice Hall, Englewood-Ciiffs, NJ, 1993.Kambil, A., Henderson, J. C , and Mohsenzadeh,H. "Strategic Managem ent of Information Tech-nology: An Options Perspective," in StrategicInformation Technology Management: Perspectives on Organizational Growth and CompetitiveAdvantage. R, D, Banker, R. J. Kauffman, andM, A, Mahmood (eds,). Idea Group Publishing,Middietown, PA, 1993.

Kauffman, R. J. , Konsynski, B,, and Kriebel, C, H,"Evaluating Research Approaches to IT Busi-ness Value Assessment with the SeniorManagement Audience in Mind: A Question andAnswer Session," in Strategic Information Tech-nology Management: Perspectives on Organi-zational Growth and Competitive Advantage. R.D. Banker, R. J. Kauffman, and M. A. M ahmood(eds.). Idea Group P ublishing, M iddietown, PA,1993.

Kumar, R. "A Note on Project Risk and OptionValues of Investments in Information Techno-logies," Journal of Management InformationSystems (13:1), 1996, pp. 187-193,Luehrman, T. A. "Investment Opportunities asReal Options: Getting Started on the Numbers,"Harvard Business Review (76), July-August1998, pp. 3-15.Mason, S., and Merton, R. "The Role of Contin-gent Claims A nalysis in Corporate Finance," inRecent Advances in Corporate Finance. E, I.Altman and M, G. Subrahmanyam (eds.),Richard D. Irwin, Homewood, IL, 1985.

McDonald, R., and Siege l, D. "The Value of


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Academy of Manage ment Review (22;4), 1997,pp . 974-996.Nichols, N. A. "Scientific Management at Merck:An Intervievi/ with CFO Judy Lewent." Harvard

Business Review, January-February 1994, pp.88-99Smit Han, T, J,, and Ankum, L. A- "A RealOptions and Gam e-Theoretic Approach to Cor-porate Investment Strategy Under Comp etition,"Financial Management. Autumn 1993. pp. 241-250,Smith, J. R., and Nau, R. F. "Valuing RiskyProjects: Option Pricing Theory and DecisionAnalysis." Management Science {41). 1995, pp.

795-816.Taudes, A. "Software Grov fth Options," Journa /ofManagem ent Information Systems (15:1),1998. pp. 165-185.Trigeorgis, L. Real Options, MIT Press. Cam-bridge, MA, 1996.

About the AuthorsMichel Benaroch is an associate professor ofInformation Systems in the School of Manage-ment, Syracuse University. His current researchinterests focus on knowledge modeling, evaluationof Investments in information technology usingoption pricing models, and intelligent decisionsupport methods in Finance and Economics. He

has published in a variety of outlets, includingInformation Systems Research, IEEE Transac-tions on Knowledge and Data Engineering,International Jourr)al of Hum an-Com puter Interac-tion. Decision S ciences, International Journal ofEconomic D ynamics and Control, Decision Sup-port Systems, and INFORM S Journal on Com-puting. He vi'as tvi/ice the recipient of the "Excep-tional Research and Scholarship Avi/ard" atSyracuse University (1996 and 1998).Robert J . Kauffman is an associate professor ofInformation Systems at the Carlson School ofManagement of the University of Minnesota. Hespecializes in research on financial informationsystems, evaluation of information technologyinvestments, and the adoption of new techno-logies. His papers have appea red in InformationSystems Research. MIS Quarterly, IEEE Transactions on Software Engineering. Decision SciencesElectronicMarkets, Journal of Management infor-mation Systems. Information and Software Tech-nologies. Organ ization Science, and elsewhere.He is a past co-chair of the Workshop on IS andEconomics (1991 and 1998) and recently guest-edited special issues of Com munications of the^ C M a n d InternationalJoum al of Electronic Com -merce on information systems research issuesinvolving theoretical perspectives from economicsand finance.

APPENDIX ADCF An alysis for Yankee 24'sImmediate Entry ^^^^^^MThe data gathered using our structured interview with Yankee 24's senior management suggests thefollowing assumptions concerning the parameters involved in an immediate entry into the POS debit


28/30


Until the end of 1991 . the total number of POS debit transactions in California was around 12million; by the end of 199 2. the number of transactions per month rose to 10 million. Thesefigures imply a 16% per month growth rate in transaction volume in California between 1985 and1992, consistent with expert estimates of the growth rate expected between 1993 and 1996. Toobtain the periodic transaction volume in New England, we applied this growth rate to a base of2,500.000 transactions for December 199 2, based on the 10.000,000 figure in California. Thebase figure is discounted back by the 16% growth rate per m onth, and the monthly transactionvolumes are agg regated.

2. The revenue per transaction is 10 cents.3. The operational marketing cost is estimated at $40,000 a year.4. The initial technical investment cost is estimated at $400,000.5. The discount rate, r, used to compute the passive NPV (ignoring the deferral flexibility) is 12%.6. The analysis horizon is 5.5 years, from early 1987 until (and including) early 1992.7. The time it takes to begin servicing customers (and receiving revenues) once an entry decision is

made is one year.Based on these assumptions. Table A l shows the (passive) NPV we calculated for Yankee 24's imm ediateentry.

Table A 1 . Passive NPV A nalysis of Yank ee's Immediate Entry into POS Debit 1ServicesYear-Month

Jan. 87July 87Jan. 88July 88Jan. 89July 89Jan. 90July 90Jan. 91July 91Jan. 92

Number ofTransactions00

3,5328,606

20,96951,088

124.470303,258738.857

1.800.1494,385,877

OperationalRevenues$0$0

$353$861

$2,097$5,109

$12,447$30,326$73,886

$180,015$438,588

OperationalCosts$0$0

$20,000$20,000$20,000$20,000$20,000$20,000$20,000$20,000$20,000

NetRevenues$0$0

($19,647)($19,139)($17,903)($14,891)

($7,553)$10,326$53,886

$160,015$418,588

InvestmentCost$400,000

$0$0$0$0$0$0$0$0$0$0

Cash Flows($400,000)

$0($19,647)($19,139)($17,903)($14,891)

($7,553)$10,326$53,886

$160,015$418,588NPV: ($76,767)


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APPENDIX BPlausible Schemes for Estimating oop tion pricing m odels represent the uncertain payoffs expected from an investment, V, using a probabilitydistribu tion, and this requires having an estimate of the variability of V. o. To this end. the recent literatureon real options discusses several schemes for estimating o based on market data (e.g,, Amram andKulatilaka 1999; Luehrman 1998). Here we summ arize only a few of the more basic schemes that can beused to estimate o.1. Supposing that an estimate of V is available, a subjective prediction that V will deviate by A% meansthat A is 0 in percent terms {Brealey and Myers 1988, p, 497). This scheme is straightforward, butsomewhat naTve, Management would rarely be able to directly come up with an adequate estimateof A%.2. Assuming that multiple sets of contingent cash flows exist, each with different subjective probabilities,let V, be set / of predicted payoffs. By computing a separate interna l rate of return (IRR) for each Vy,o can be the standard deviation of the computed IRRs (Copeland and Weston 1988, p. 426).Compared to the first scheme, this scheme forces management to take an extra step that can makethe estimate of o more reliable.3. If we know the probab ility distribution of the expected project revenues and we can specifymathematically the functional relationships between input and output variables, a Monte Carlo

simulation can be used to estimate o (Luehrman 1998). Thus, since the variance associated with thepresent value of expected cash flows captures the uncertainty due to multiple possible futureoutcomes, a Monte Carlo simu lation of the future outcomes can establish o. As a variation of thesecond schem e, this scheme forces managem ent to probe deeper Into the uncertain nature of V inorder to produce an even more reliable estimate of o,4. Where S is the price of a "twin security"a traded security that has the same risk characteristics as(i.e., is perfectly or highly correlated with) the project under considerationboth V and S have thesame rate of return and volatility. Thus, o can be estimated as the variability of the rate of return onS. This scheme is readily applicable in two cases . One is when there is a publicly traded firm whose

primary revenue generating services (e.g., ATM services, Internet advertising) parallel the servicesthat the target project would yield to generate payoffs. Another case is when the primary risk in thetarget project is due to reliance on a risky IT that is the main product sold by a traded firm (e.g,, CASEtools, multimedia tools).5. Where the sources of project value uncertainty have been recognized (technical risk, competition risk,e tc) , we p ropose that o can be plausibly broken down into its com ponents. If r, is one of the riskscontributing to the uncertainty of V and air^) denotes the direct contribution of r, to the variance of V,then o can be estimated as:


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