A fuzzy goal programming model for strategic information technology investment assessment Faramak Zandi Industrial Engineering Department, Faculty of Technology and Engineering, Alzahra University, Tehran, Iran, and Madjid Tavana Management Department, Lindback Distinguished Chair of Information Systems, La Salle University, Philadelphia, Pennsylvania, USA Abstract Purpose – The high expenditures in information technology (IT) and the growing usage that penetrates the core of business have resulted in a need to effectively and efficiently evaluate strategic IT investments in organizations. The purpose of this paper is to propose a novel two-dimensional approach that determines the deferrable strategy with the most value by maximizing the real option values while minimizing the risks associated with each alternative strategy. Design/methodology/approach – In the proposed approach, first, the deferrable investment strategies are prioritized according to their values using real option analysis (ROA). Then, the risks associated with each investment strategy are quantified using the group fuzzy analytic hierarchy process. Finally, the values associated with the two dimensions are integrated to determine the deferrable IT investment strategy with the most value using a fuzzy preemptive goal programming model. Findings – Managers face the difficulty that most IT investment projects are inherently risky, especially in a rapidly changing business environment. The paper proposes a framework that can be used to evaluate IT investments based on the real option concept. This simple, intuitive, generic and comprehensive approach incorporates the linkage among economic value, real option value and IT investments that could lead to a better-structured decision process. Originality/value – In contrast to the traditional ROA literature, the approach contributes to the literature by incorporating a risk dimension parameter. The paper emphasizes the importance of categorizing risk management in IT investment projects since some risk cannot be eliminated. Keywords Fuzzy control, Information technology, Value analysis, Risk analysis, Analytical hierarchy process Paper type Research paper 1. Introduction Information technology (IT) investments represent the largest capital expenditure items for many organizations and have a tremendous impact on productivity by reducing costs, improving quality and increasing value to customers. As a result, many organizations continue to invest large sums of money in IT in anticipation of a material return on their investment (Willcocks and Lester, 1996). The selection of appropriate IT investments has The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htm The authors would like to thank the anonymous reviewers and the Editor for their insightful comments and suggestions. BIJ 18,2 172 Benchmarking: An International Journal Vol. 18 No. 2, 2011 pp. 172-196 q Emerald Group Publishing Limited 1463-5771 DOI 10.1108/14635771111121667
Transcript
A fuzzy goal programmingmodel for strategic
information technologyinvestment assessment
Faramak ZandiIndustrial Engineering Department, Faculty of Technology and Engineering,
Alzahra University, Tehran, Iran, and
Madjid TavanaManagementDepartment, LindbackDistinguishedChair of InformationSystems,
La Salle University, Philadelphia, Pennsylvania, USA
Abstract
Purpose – The high expenditures in information technology (IT) and the growing usage thatpenetrates the core of business have resulted in a need to effectively and efficiently evaluate strategicIT investments in organizations. The purpose of this paper is to propose a novel two-dimensionalapproach that determines the deferrable strategy with the most value by maximizing the real optionvalues while minimizing the risks associated with each alternative strategy.
Design/methodology/approach – In the proposed approach, first, the deferrable investmentstrategies are prioritized according to their values using real option analysis (ROA). Then, the risksassociated with each investment strategy are quantified using the group fuzzy analytic hierarchyprocess. Finally, the values associated with the two dimensions are integrated to determine the deferrableIT investment strategy with the most value using a fuzzy preemptive goal programming model.
Findings – Managers face the difficulty that most IT investment projects are inherently risky,especially in a rapidly changing business environment. The paper proposes a framework that can beused to evaluate IT investments based on the real option concept. This simple, intuitive, generic andcomprehensive approach incorporates the linkage among economic value, real option value and ITinvestments that could lead to a better-structured decision process.
Originality/value – In contrast to the traditional ROA literature, the approach contributes to theliterature by incorporating a risk dimension parameter. The paper emphasizes the importance ofcategorizing risk management in IT investment projects since some risk cannot be eliminated.
Keywords Fuzzy control, Information technology, Value analysis, Risk analysis,Analytical hierarchy process
Paper type Research paper
1. IntroductionInformation technology (IT) investments represent the largest capital expenditure itemsfor many organizations and have a tremendous impact on productivity by reducing costs,improving quality and increasing value to customers. As a result, many organizationscontinue to invest large sums of money in IT in anticipation of a material return on theirinvestment (Willcocks and Lester, 1996). The selection of appropriate IT investments has
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
The authors would like to thank the anonymous reviewers and the Editor for their insightfulcomments and suggestions.
BIJ18,2
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Benchmarking: An InternationalJournalVol. 18 No. 2, 2011pp. 172-196q Emerald Group Publishing Limited1463-5771DOI 10.1108/14635771111121667
been one of the most significant business challenges of the last decade. Powell (1992)has studied the similarities and differences between IT investments and other capitalinvestments in organizations. He notes that IT investments are undertaken byorganizations to gain competitive advantage, to improve productivity, to enable new waysof managing and organizing and to develop new businesses. Appropriate strategic ITinvestments can help companies gain and sustain a competitive advantage (Melville et al.,2004). However, many large IT investment projects often do not meet original expectationsof cost, time or benefits. The rapid growth of IT investments has imposed tremendouspressure on management to take into consideration risks and payoffs promised by theinvestment in their decision making.
A review of the current literature offers several IT investment evaluation methodsthat provide frameworks for the quantification of risks and benefits. The net presentvalue (NPV) (Hayes and Abernathy, 1980; Kaplan and Atkinson, 1998), return oninvestment (Brealey and Myers, 1998; Farbey et al., 1993; Kumar, 2002; Luehrman,1997), cost benefit analysis (Schniederjans et al., 2004), information economics (Bakosand Kemerer, 1992; Parker and Benson, 1989) and return on management (Chen et al.,2006; Stix and Reiner, 2004; Strassmann, 1997) are among most widely used methods toassess the risks and payoffs associated with IT investments.
In addition to the above mentioned traditional quantitative approaches, there is astream of research studies which emphasizes real option analysis (ROA). The ROA differsfrom the traditional methods in terms of priceability of the underlying investment project(McGrath, 1997). With the traditional methods, the underlying investment project of anoption is priced as known (Black and Scholes, 1973) while in IT investment situations theprice of an underlying investment is rarely known (McGrath, 1997). The ROA uses threebasic types of data:
(1) current and possible future investment options;
(2) the desired capabilities sought by the organization; and
(3) the relative risks and costs of other IT investment options that could be used.
The method can help assess the risks associated with IT investment decisions bytaking into consideration the changing nature of business strategies andorganizational requirements.
The real options are commonly valued with the Black-Scholes option pricing formula(Black and Scholes, 1973, 1974), the binomial option valuation method (Cox et al., 1979)and Monte-Carlo methods (Boyle, 1977). These methods assume that the underlyingmarkets can be imitated accurately as a process. Although this assumption may hold forsome quite efficiently traded financial securities, it may not hold for real investments thatdo not have existing markets (Collan et al., 2009). Recently, a simple novel approach toROA called the Datar-Mathews method (Datar and Mathews, 2004, 2007; Mathews andSalmon, 2007) was proposed where the real option value is calculated from a pay-offdistribution, derived from a probability distribution of the NPV for an investment projectgenerated with a Monte-Carlo simulation. This approach does suffer from the marketprocess assumptions associated with the Black-Scholes method (Black and Scholes, 1974).
When valuating an investment using ROA, it is required to estimate severalparameters (i.e. expected payoffs and costs or investment deferral time). However, theestimation of uncertain parameters in this valuation process is often very challenging.Most traditional methods use probability theory in their treatment of uncertainty.
Fuzzy goalprogramming
model
173
Fuzzy logic and fuzzy sets can represent ambiguous, uncertain or imprecise informationin ROA by formalizing inaccuracy in human decision making (Collan et al., 2009).For example, fuzzy sets allow for graduation of belonging in future cash-flow estimation(i.e. future cash flow at year 5 is about 5,000 dollars). Fuzzy set algebra developed byZadeh (1965) is the formal body of theory that allows the treatment of impreciseestimates in uncertain environments.
In recent years, several researchers have combined fuzzy sets theory with ROA.Carlsson and Fuller (2003) introduced a (heuristic) real option rule in a fuzzy setting,where the present values of expected cash flows and expected costs are estimated bytrapezoidal fuzzy numbers. Chen et al. (2007) developed a comprehensive but simplemethodology to evaluate IT investment in a nuclear power station based on fuzzy riskanalysis and real option approach. Frode (2007) used the conceptual real optionframework of Dixit and Pindyck (1994) to estimate the value of investment opportunitiesin the Norwegian hydropower industry. Villani (2008) combined two successful theories,namely real options and game theory, to value the investment opportunity and the valueof flexibility as a real option while analyzing the competition with game theory.Collan et al. (2009) presented a new method for real option valuation using fuzzy numbers.Their method considered the dynamic nature of the profitability assessment, that is, theassessment changes when information changes. As cash flows taking place in the futurecome closer, information changes and uncertainty is reduced. Chrysafis andPapadopoulos (2009) presented an application of a new method of constructing fuzzyestimators for the parameters of a given probability distribution function using statisticaldata. Wang and Hwang (2007) developed a fuzzy research and development portfolioselection model to hedge against the environmental uncertainties. They applied fuzzy settheory to model uncertain and flexible project information. Since traditional projectvaluation methods often underestimate the risky project, a fuzzy compound-optionsmodel was used to evaluate the value of each project. Their portfolio selection problemwas formulated as a fuzzy zero-one integer programming model that could handle bothuncertain and flexible parameters and determine the optimal project portfolio. A newtransformation method based on qualitative possibility theory was developed to convertthe fuzzy portfolio selection model into a crisp mathematical model from the risk-averseperspective. The transformed model was solved by an optimization technique.
We propose a novel two-dimensional approach that determines the deferrablestrategy with the most value by maximizing the real option values while minimizing therisks associated with each alternative strategy. First, the deferrable investmentstrategies are prioritized according to their values using the ROA. Then, the risksassociated with each investment strategy are quantified using the group fuzzy analytichierarchy process (GFAHP). Finally, the values associated with the two dimensions areintegrated to determine the deferrable IT investment strategy with the most value usinga fuzzy preemptive goal programming model. The proposed framework:
. addresses the gaps in the IT investment assessment literature on the effectiveand efficient evaluation of IT investment strategies;
. provides a comprehensive and systematic framework that combines ROA witha group fuzzy approach to assess IT investment strategies;
. considers fuzzy logic and fuzzy sets to represent ambiguous, uncertain orimprecise information; and
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. it uses a real-world case study to demonstrate the applicability of the proposedframework and exhibit the efficacy of the procedures and algorithms.
This paper is organized into five sections. In Section 2, we illustrate the details of theproposed framework followed by a case study in Section 3. In Section 4, we presentdiscussion and practical perspectives and in Section 5, we conclude with our conclusionsand future research directions.
2. The proposed frameworkThe mathematical notations and definitions used in our model are presented in theAppendix. The framework shown in Figure 1 is proposed to assess alternative ITinvestment strategies. The framework consists of several steps modularized into fivephases.
Phase 1: establishment of the IT investment boardWe institute a strategic IT investment board to acquire pertinent investmentinformation. Executive management is typically responsible for creating the board,specifying its responsibilities and defining its resources. Let us assume that l strategicIT investment board members are selected to participate in the evaluation process:
Phase 2: identification of the IT investment strategiesNext, the strategic IT investment board identifies a set of alternative deferrable ITinvestment strategies. Let us assume that n alternative IT investments with themaximum deferral time of Tm are under consideration:
a ¼ ½a1; a2; . . . ; ai; . . .an�
Phase 3: prioritization of the IT investment strategies: real option considerationsIn this phase, the real options equations suggested by Dos Santos (1994) are used toprioritize IT investments strategies. This phase is divided into the following three steps.
Step 3.1: construction of the individual real option matrices. The following individualreal option matrices are given by each strategic IT investment board member:
Fuzzy numbers are often represented by triangular or trapezoidal fuzzy sets. In thisstudy, we use trapezoidal fuzzy sets. A major advantage of trapezoidal fuzzy numbers is
Fuzzy goalprogramming
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175
Figure 1.The proposed framework
Phase 1Establishment of the IT investment board
Phase 2Identification of the IT investment strategies
Phase 3Prioritization of the IT investment strategies: real option considerations
Phase 4Prioritization of the IT investment strategies: risk considerations
Phase 5Development of the strategic IT investment plan
Step 5.2Computation of the goal values
Step 5.1Determination of the goal and priority levels
Step 5.3Construction of the proposed goal
programming model
Step 4.1Identification of the criteria and sub-criteria
for the GFAHP model
Step 4.2Construction of the individual fuzzy pairwise
comparison matrices
Step 4.3Construction of the weighted collective fuzzy
pairwise comparison matrix
Step 4.4Computation of the vector of the risk value for
the IT investment strategies
Step 3.1Construction of the individual real option
matrices
Step 3.2Construction of the weighted collective real
option matrix
Step 3.3Computation of the vector of the real option
value for the IT investment strategies
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that many operations based on the max-min convolution can be replaced by directarithmetic operations (Dubois and Prade, 1988). The following trapezoidal fuzzy numbersare used for the individual fuzzy present values of the expected cash flows and the cost ofthe ith IT investment at time Tj by strategic IT investment board member (ITIB)k:
~Bk
i ðTjÞ ¼ Bki ðTjÞ
� �o; Bk
i ðTjÞ� �a
; Bki ðTjÞ
� �b; Bk
i ðTjÞ� �g� �
~Ck
i ¼ Cki ðTjÞ
� �o; Ck
i ðTjÞ� �a
; Cki ðTjÞ
� �b; Ck
i ðTjÞ� �g� �
For j ¼ 1; 2; . . . ;m:
ð2Þ
That is, we have the following intervals:
Bki ðTjÞ
� �o; Bk
i ðTjÞ� �aj k
the most possible values for the expected cash flows ofthe ith IT investment at time Tj evaluated by strategicIT investment board member (ITIB)k.
Bki ðTjÞ
� �oþ Bk
i ðTjÞ� �g� �
the upward potential for the expected cash flows of theith IT investment at time Tj evaluated by strategic ITinvestment board member (ITIB)k.
Bki ðTjÞ
� �o2 Bk
i ðTjÞ� �b� �
the downward potential for the expected cash flows ofthe ith IT investment at time Tj evaluated by strategicIT investment board member (ITIB)k.
Cki ðTjÞ
� �o; Ck
i ðTjÞ� �aj k
the most possible values of the expected cost of the ithIT investment at time Tj evaluated by strategic ITinvestment board member (ITIB)k.
Cki ðTjÞ
� �oþ Ck
i ðTjÞ� �g� �
the upward potential for the expected cost of the ith ITinvestment at time Tj evaluated by strategic ITinvestment board member (ITIB)k.
Cki ðTjÞ
� �o2 Ck
i ðTjÞ� �b� �
the downward potential for the expected cash flows ofthe ith IT investment at time Tj evaluated by strategicIT investment board member (ITIB)k.
Consequently, substituting equation (2) into matrix (1), the individual real optionmatrices can be rewritten as:
~Ak
RO1ðTiÞ¼
a1
a2
..
.
an
~B Tið Þ ~C Tið Þ
Bk1 Tið Þ
� �o; Bk
1 Tið Þ� �a
; Bk1 Tið Þ
� �b; Bk
1 Tið Þ� �g� �
Bk2 Tið Þ
� �o; Bk
2 Tið Þ� �a
; Bk2 Tið Þ
� �b; Bk
2 Tið Þ� �g� �
..
.
Bkn Tið Þ
� �o; Bk
n Tið Þ� �a
; Bkn Tið Þ
� �b; Bk
n Tið Þ� �g� �
26666666666664
Ck1 Tið Þ
� �o; Ck
1 Tið Þ� �a
; Ck1 Tið Þ
� �b; Ck
1 Tið Þ� �g� �
Ck2 Tið Þ
� �o; Ck
2 Tið Þ� �a
; Ck2 Tið Þ
� �b; Ck
2 Tið Þ� �g� �
..
.
Ckn Tið Þ
� �o; Ck
n Tið Þ� �a
; Ckn Tið Þ
� �b; Ck
n Tið Þ� �g� �
37777777777775
ð3Þ
Fuzzy goalprogramming
model
177
Step 3.2: construction of the weighted collective real option matrix. This frameworkallows for assigning different voting power weights given to each investment boardmember:
Therefore, in order to form a fuzzy weighted collective real option matrix, the individualfuzzy real option matrices will be aggregated by the voting powers as follows:
~ARO2ðTiÞ ¼
a1
a2
..
.
an
~BðTiÞ~CðTiÞ
~B1ðTiÞ ~C1ðTiÞ
~B2ðTiÞ ~C2ðTiÞ
..
. ...
~BnðTiÞ ~CnðTiÞ
26666664
37777775
ð5Þ
where:
~BiðTiÞ ¼
Plk¼1ðwðvpÞkÞ
~Bk
i ðTiÞ� �
Plk¼1wðvpÞk
ð6Þ
~CiðTiÞ ¼
Plk¼1ðwðvpÞkÞ
~Ck
i ðTiÞ� �
Plk¼1wðvpÞk
ð7Þ
Step 3.3: Computation of the vector of the real option value for the IT investmentstrategies. The real option values of the investment strategies at times T1;T2; . . . ;Tm
can be determined by the following fuzzy real option value matrix:
~AFROV ¼
a1
a2
..
.
a4
T1 T2 . . . Tm
FROV 1ðT1Þ FROV 1ðT2Þ . . . FROV 1ðTmÞ
FROV 2ðT1Þ FROV 2ðT2Þ . . . FROV 2Tm
..
. ...
. . . ...
FROVnðT1Þ FROVnðT2Þ . . . FROVnTm
26666664
37777775
ð8Þ
or:
~AFROV ðTiÞ¼
a1
a2
..
.
a4
~B1ðTiÞ ·e2dTi ·N ðD11ðTiÞÞ2
~C1ðTiÞ ·e2rTi ·NðD21ðTiÞÞ
~B2ðTiÞ ·e2dTi ·N ðD12ðTiÞÞ2 ~C2ðTiÞ ·e
2rTi ·NðD22ðTiÞÞ
..
.
~BnðTiÞ ·e2dTi ·N ðD1nðTiÞÞ2 ~CnðTiÞ ·e
2rTi ·NðD2nðTiÞÞ
26666664
37777775¼
FROV 1ðTiÞ
FROV 2ðTiÞ
..
.
FROVnðTiÞ
26666664
37777775
ð9Þ
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where the IT investment strategy ith cumulative normal probabilities for the D1and D2
are as follows:
ARO3ðTiÞ ¼
a1
a2
..
.
an
NðD1ðTiÞÞ N ðD2ðTiÞÞ
N ðD11ðTiÞÞ N ðD21ðTiÞÞ
N ðD12ðTiÞÞ N ðD22ðTiÞÞ
..
. ...
N ðD1nðTiÞÞ N ðD2nðTiÞÞ
26666664
37777775
ð10Þ
ARO4ðTÞ ¼
a1
a2
..
.
an
D1ðTiÞ D2ðTiÞ
D11ðTiÞ D21ðTiÞ
D12ðTiÞ D22ðTiÞ
..
. ...
D1nðTiÞ D2nðTiÞ
26666664
37777775
ð11Þ
or equivalently:
ARO4ðTiÞ ¼
a1
a2
..
.
an
D1ðTiÞ D2ðTiÞ
LnðEð ~B1ðTiÞÞ=Eð~C1ðTi ÞÞÞþ r12d1þs2
1ðTi Þð Þ=2ð Þ ·Ti
s1ðTi ÞffiffiffiffiTi
pLnðEð ~B1ðTiÞÞ=Eð
~C1ðTi ÞÞÞþ r12d12s21ðTi Þð Þ=2ð Þ ·Ti
s21ðTiÞ
ffiffiffiffiTi
p
LnðEð ~B2ðTiÞÞ=Eð~C2ðTi ÞÞÞþ r22d2þs2
2ðTi Þð Þ=2ð Þ ·Ti
s2ðTi ÞffiffiffiffiTi
pLnðEð ~B2ðTiÞÞ=Eð
~C2ðTi ÞÞÞþ r22d22s22ðTi Þð Þ=2ð Þ ·Ti
s2ðTiÞffiffiffiT
p
..
. ...
LnðEð ~BnðTiÞÞ=Eð~CnðTiÞÞÞþ rn2dnþs2
nðTi Þð Þ=2ð Þ ·Ti
snðTiÞffiffiffiffiTi
p LnðEð ~BnðTi ÞÞ=Eð~CnðTiÞÞÞþ rn2dn2s2
nðTi Þð Þ=2ð Þ ·Ti
snðTiÞffiffiffiffiTi
p
2666666666664
3777777777775
ð12Þ
where E and s 2 denote the possibilistic mean value and possibilistic varianceoperators as follows:
ARO5ðTiÞ ¼
a1
a2
..
.
an
Eð ~BðTiÞÞ Eð ~CðTiÞÞ s 2ðTiÞ
Eð ~B1ðTiÞÞ Eð ~C1ðTiÞÞ s21ðTiÞ
Eð ~B2ðTiÞÞ Eð ~C2ðTiÞÞ s22ðTiÞ
..
. ... ..
.
Eð ~BnðTiÞÞ Eð ~CnðTiÞÞ s2nðTiÞ
266666664
377777775
ð13Þ
Fuzzy goalprogramming
model
179
Since Bi and Ci are trapezoidal fuzzy numbers, we use the formulas proposed byCarlsson and Fuller (2003) to find their expected value and the variance:
Eð ~BiðTjÞÞ ¼ðBðTjÞÞ
o þ ðBðTjÞÞa
2þ
ðBðTjÞÞg 2 ðBðTjÞÞ
b
6
Eð ~CiðTjÞÞ ¼ðCðTjÞÞ
o þ ðCðTjÞÞa
2þ
ðCðTjÞÞg 2 ðCðTjÞÞ
b
6
s2i ðTjÞ ¼
ððBðTjÞÞa 2 ðBðTjÞÞ
oÞ2
4þ
ððBðTjÞÞa 2 ðBðTjÞÞ
oÞððBðTjÞÞb þ ðBðTjÞÞ
gÞ
6
þððBðTjÞÞ
b þ ðBðTjÞÞgÞ2
24ð14Þ
Phase 4: prioritization of the IT investment strategies: risk considerationsIn this phase, the strategic IT investment board identifies the evaluation criteria andsub-criteria and uses GFAHP to measure the risk for each criterion and sub-criterionassociated with the investment projects. This phase is divided into the following foursteps.
Step 4.1: identification of the criteria and sub-criteria for the GFAHP model. In thisstep, the strategic IT investment board will determine a list of the criteria andsub-criteria for the GFAHP model. Let c1; c2; . . . ; cp and sc1; sc2; . . . ; scq be the criteriaand sub-criteria, respectively.
Step 4.2: construction of the individual fuzzy pairwise comparison matrices. Thehierarchal structure for ranking the IT Investments strategies in the risk dimensionconsists of four levels. The top level consists of a single element and each element of agiven level dominates or covers some or all of the elements in the level immediatelybelow. At the second level, the individual fuzzy pairwise comparison matrix of the pcriteria of IT investment risk evaluated by strategic IT investment board member(ITIB)k will be as follows:
~A2
R
� �k
¼
c1
c2
..
.
cp
c1 c2 . . . cp
~bk
11~bk
12 . . . ~bk
1p
~bk
21~bk
22 . . . ~bk
2p
..
. ...
. . . ...
~bk
p1~bk
p2 . . . ~bk
pp
2666666664
3777777775
ð15Þ
Let the individual fuzzy comparison qualification between criteria i and j evaluated bystrategic IT investment board member (ITIB)k be the following trapezoidal fuzzynumbers:
~bk
ij ¼ bkij
� �o
; bkij
� �a
; bkij
� �b
; bkij
� �g� �
ð16Þ
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Consequently, substituting equation (18) into matrix (17), the individual fuzzycomparison qualification between criteria i and j evaluated by strategic IT investmentboard member (ITIB)k can be rewritten as:
ð ~A2
RÞk¼
c1
c2
..
.
cp
C1 c2 ... Cp
ððbk11Þo;ðbk11Þ
a;ðbk11Þb;ðbk11Þ
gÞ ððbk12Þo;ðbk12Þ
a;ðbk12Þb;ðbk12Þ
gÞ ... ððbk1pÞo;ðbk1pÞ
a;ðbk1pÞb;ðbk1pÞ
gÞ
ððbk21Þo;ðbk21Þ
a;ðbk21Þb;ðbk21Þ
gÞ ððbk22Þo;ðbk22Þ
a;ðbk22Þb;ðbk22Þ
gÞ ... ððbk2pÞo;ðbk2pÞ
a;ðbk2pÞb;ðbk2pÞ
gÞ
..
. ...
... ...
ððbkp1Þo;ðbkp1Þ
a;ðbkp1Þb;ðbkp1Þ
gÞ ððbkp2Þo;ðbkp2Þ
a;ðbkp2Þb;ðbkp2Þ
gÞ ... ððbkppÞo;ðbkppÞ
a;ðbkppÞb;ðbkppÞ
gÞ
266666664
377777775
ð17Þ
At the third level, the individual fuzzy pairwise comparison matrix of IT investmentrisk sub-criteria with respect to p IT investment risk criteria evaluated by strategic ITinvestment board member (ITIB)k will be as follows:
~A3
R
� �k
¼
sc1
sc2
..
.
scq
sc1 sc2 . . . scq
~dk
11
� �P
~dk
12
� �P
. . . ~dk
1q
� �P
~dk
21
� �P
~dk
22
� �P
. . . ~dk
2q
� �P
..
. ...
. . . ...
~dk
q1
� �P
~dk
q2
� �P
. . . ~dk
qq
� �P
2666666664
3777777775
ð18Þ
The individual fuzzy comparison qualification between sub-criterions i withsub-criterion j with respect to criterion p evaluated by strategic IT investment boardmember (ITIB)k are the following trapezoidal fuzzy numbers:
~dk
ij
� �p¼ dkij
� �o
; dkij
� �a
; dkij
� �b
; dkij
� �g� �
p
ð19Þ
Therefore, we have:
sc1 sc2 ... scq
ð ~A3
RÞk¼
sc1
sc2
..
.
scq
ððdk11Þo;ðdk11Þ
a;ðdk11Þb;ðdk11Þ
gÞp ððdk12Þo;ðdk12Þ
a;ðdk12Þb;ðdk12Þ
gÞp ... ððdk1qÞo;ðdk1qÞ
a;ðdk1qÞb;ðdk1qÞ
gÞp
ððdk21Þo;ðdk21Þ
a;ðdk21Þb;ðdk21Þ
gÞp ððdk22Þo;ðdk22Þ
a;ðdk22Þb;ðdk22Þ
gÞp ... ððdk2qÞo;ðdk2qÞ
a;ðdk2qÞb;ðdk2qÞ
gÞ
..
. ...
... ...
ððdkq1Þo;ðdkq1Þ
a;ðdkq1Þb;ðdkq1Þ
gÞp ððdkq2Þo;ðdkq2Þ
a;ðdkq2Þb;ðdkq2Þ
gÞp ... ððdkqqÞo;ðdkqqÞ
a;ðdkqqÞb;ðdkqqÞ
gÞp
266666664
377777775
ð20Þ
At the fourth level, the individual fuzzy pairwise comparison matrix of n IT investmentstrategies with respect to q IT investment risk sub-criteria evaluated by strategicIT investment board member (ITIB)k will be as follows:
Fuzzy goalprogramming
model
181
~A4
R
� � k
¼
a1
a2
..
.
an
a1 a2 . . . an
~rk11
� �q
~rk12
� �q
. . . ~rk1n� �
q
~rk21
� �q
~rk22
� �q
. . . ~rk2n� �
q
..
. ...
. . . ...
~rkn1
� �q
~rkn2
� �q
. . . ~rknn� �
q
26666666664
37777777775
ð21Þ
The individual fuzzy comparison qualification between IT investment strategies i withIT investment strategy j with respect to sub-criterion q evaluated by strategic ITinvestment board member (ITIB)k are the following trapezoidal fuzzy numbers:
~rkij
� �q¼ r kij
� �o
; r kij
� �a
; r kij
� �b
; r kij
� �g� �
q
ð22Þ
or equivalently:
ð ~A4
RÞk¼
a1
a2
..
.
an
a1 a2 ... an
ððr k11Þo;ðr k11Þ
a;ðr k11Þb;ðr k11Þ
gÞq ððr k12Þo;ðr k12Þ
a;ðr k12Þb;ðr k12Þ
gÞq ... ððr k1nÞo;ðr k1nÞ
a;ðr k1nÞb;ðr k1nÞ
gÞq
ððr k21Þo;ðr k21Þ
a;ðr k21Þb;ðr k21Þ
gÞq ððr k22Þo;ðr k22Þ
a;ðr k22Þb;ðr k22Þ
gÞq ... ððr k2nÞo;ðr k2nÞ
a;ðr k2nÞb;ðr k2nÞ
gÞq
..
. ...
... ...
ððr kn1Þo;ðr kn1Þ
a;ðr kn1Þb;ðr kn1Þ
gÞq ððr kn2Þo;ðr kn2Þ
a;ðr kn2Þb;ðr kn2Þ
gÞq ... ððr knnÞo;ðr knnÞ
a;ðr knnÞb;ðr knnÞ
gÞq
2666666664
3777777775
ð23Þ
Step 4.3: construction of the weighted collective fuzzy pairwise comparison matrix.At the second level, the fuzzy weighted collective pairwise comparison matrix of p ITinvestment risk criteria will be as follows:
c1 c2 ... cp
~A2
R¼
c1
c2
..
.
cp
ððb11Þo;ðb11Þ
a;ðb11Þb;ðb11Þ
gÞ ððb12Þo;ðb12Þ
a;ðb12Þb;ðb12Þ
gÞ ... ððb1pÞo;ðb1pÞ
a;ðb1pÞb;ðb1pÞ
gÞ
ððb21Þo;ðb21Þ
a;ðb21Þb;ðb21Þ
gÞ ððb22Þo;ðb22Þ
a;ðb22Þb;ðb22Þ
gÞ ... ððb2pÞo;ðb2pÞ
a;ðb2pÞb;ðb2pÞ
gÞ
..
. ...
... ...
ððbp1Þo;ðbp1Þ
a;ðbp1Þb;ðbp1Þ
gÞ ððbp2Þo;ðbp2Þ
a;ðbp2Þb;ðbp2Þ
gÞ ... ððbppÞo;ðbppÞ
a;ðbppÞb;ðbppÞ
gÞ
2666666664
3777777775
ð24Þ
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or:
~A2
R ¼
c1
c2
..
.
cp
c1 c2 . . . cp
~b11~b12 . . . ~b1p
~b21~b22 . . . ~b2p
..
. ...
. . . ...
~bp1~bp2 . . . ~bpp
26666664
37777775
ð25Þ
where:
ð~bijÞj ¼
Plk¼1ðwðvpÞkÞ
~bk
ij
� �j
� Pl
k¼1wðvpÞkð26Þ
At the third level, the fuzzy weighted collective pairwise comparison matrix of the ITinvestment risk sub-criteria with respect to the p IT investment risk criteria will be asfollows:
~A3
R ¼
sc1
sc2
..
.
scq
sc1 sc2 . . . scq
ððd11Þo; ðd11Þ
a; ðd11Þb; ðd11Þ
gÞp ððd12Þo; ðd12Þ
a; ðd12Þb; ðd12Þ
gÞp . . . ððd1qÞo; ðd1qÞ
a; ðd1qÞb; ðd1qÞ
gÞp
ððd21Þo; ðd21Þ
a; ðd21Þb; ðd21Þ
gÞp ððd22Þo; ðd22Þ
a; ðd22Þb; ðd22Þ
gÞp . . . ððd2qÞo; ðd2qÞ
a; ðd2qÞb; ðd2qÞ
gÞ
..
. ...
. . . ...
ððdq1Þo; ðdq1Þ
a; ðdq1Þb; ðdq1Þ
gÞp ððdq2Þo; ðdq2Þ
a; ðdq2Þb; ðdq2Þ
gÞp . . . ððdqqÞo; ðdqqÞ
a; ðdqqÞb; ðdqqÞ
gÞp
26666664
37777775
ð27Þ
or:
~A3
R ¼
sc1
sc2
..
.
scq
sc1 sc2 . . . scq
ð~d11ÞP ð~d12ÞP . . . ð~d1qÞP
ð~d21ÞP ð~d22ÞP . . . ð~d2qÞP
..
. ...
. . . ...
ð~dq1ÞP ð~dq2ÞP . . . ð~dqqÞP
26666664
37777775
ð28Þ
where:
ð~dijÞj ¼
Plk¼1ðwðvpÞkÞ
~dk
ij
� �p
� Pl
k¼1wðvpÞkð29Þ
At the fourth level, the fuzzy weighted collective pairwise comparison matrix of the nIT investment strategies with respect to the q IT investment risk sub-criteria will be asfollows:
Fuzzy goalprogramming
model
183
~A4
R¼
a1
a2
..
.
an
a1 a2 ... an
ððr11Þo;ðr11Þ
a;ðr11Þb;ðr11Þ
gÞq ððr12Þo;ðr12Þ
a;ðr12Þb;ðr12Þ
gÞq ... ððr1nÞo;ðr1nÞ
a;ðr1nÞb;ðr1nÞ
gÞq
ððr21Þo;ðr21Þ
a;ðr21Þb;ðr21Þ
gÞq ððr22Þo;ðr22Þ
a;ðr22Þb;ðr22Þ
gÞq ... ððr2nÞo;ðr2nÞ
a;ðr2nÞb;ðr2nÞ
gÞq
..
. ...
... ...
ððrn1Þo;ðrn1Þ
a;ðrn1Þb;ðrn1Þ
gÞq ððrn2Þo;ðrn2Þ
a;ðrn2Þb;ðrn2Þ
gÞq ... ððrnnÞo;ðrnnÞ
a;ðrnnÞb;ðrnnÞ
gÞq
26666664
37777775
ð30Þ
or:
~A 4 ¼
a1
a2
..
.
an
a1 a2 . . . an
ð~r11Þq ð~r12Þq . . . ð~r1nÞq
ð~r21Þq ð~r22Þq . . . ð~r2nÞq
..
. ...
. . . ...
ð~rn1Þq ð~rn2Þq . . . ð~rnnÞq
26666664
37777775
ð31Þ
where:
~rij ¼
Plk¼1ðwðvpÞkÞ ~rkij
� �Pl
k¼1wðvpÞkð32Þ
Step 4.4: computation of the vector of the risk value for the IT investment strategies. Thefuzzy composite vector of the deferrable IT investment strategies at the fourth levelwill be calculated based on the corresponding eigenvectors:
Phase 5: development of the strategic IT investment planDecision makers also must consider the interaction between the real option and theinvestment risks. Therefore, in this phase, the IT investment strategy with the mostvalue is determined in terms of real option and risk values in Phases 2 and 3. For thispurpose, they are considered as the coefficients of the objective functions in thefollowing fuzzy preemptive goal programming model with a series of applicableconstraints. This phase is divided into the following three steps.
Step 5.1: determination of the goal and priority levels. The goals in the fuzzypreemptive goal programming model can be written as follows:
For the first priority level, there are two goals. These goals are equally important sothey can have the same weight:
where f iðx1; x2; . . . ; xnÞ are given functions of the n investments.Step 5.2: computation of the goal values. In this step, instead of trying to optimize
each objective function, the strategic IT investment board will specify a realistic goalor target value that is the most desirable value for that function.
Step 5.3: construction of the proposed goal programming model. The first objectivefunction is to be maximized and the second objective function is to be minimized.Therefore, the proposed fuzzy goal programming model for the above two-objectivestrategic IT investment decision will be the following single-objective model:
The optimal solution for model (F) is the deferrable IT investment strategy with themost values at the time Ti. Next, we present a numerical example to demonstrate theimplementation process of this framework.
3. Case studyWe implemented the proposed model at Mornet[1], a large mortgage company in thecity of Philadelphia with an urgent need to select an optimal IT investment strategy fortheir deferrable investment opportunities.
In Phase 1, the chief executive officer instituted a committee of four strategic ITinvestment board members, including:
(ITIB)1. The chief operating officer.
(ITIB)2. The chief information officer.
(ITIB)3. The heads of the business unit.
(ITIB)4. The chief financial officer.
In Phase 2, the investment board identifies five different types of deferrable investmentopportunities with the following characteristics (Table I) as suggested by Carlsson et al.(2007):
a1. Project 1 has a large negative estimated NPV (due to huge uncertainties) andcan be deferred up to two years (v(FNPV) , 0, T ¼ 2).
a2. Project 2 includes positive NPV with low risks and has no deferral flexibility(v(FNPV) . 0, T ¼ 0).
Fuzzy goalprogramming
model
187
a3. Project 3 has revenues with large upward potentials and managerial flexibility,but its “reserve costs” (c) are very high.
a4. Project 4 requires a large capital expenditure once it has been undertaken andhas a deferral flexibility of a maximum of one year.
a5. Project 5 represents a small flexible project with low revenues, but it opens thepossibility of further projects that are much more profitable.
In Phase 3, the fuzzy real option values of the five different deferrable investmentopportunities shown in Figure 2 were determined for years 1 and 2.
In Phase 4, the strategic IT investment board determined the GFAHP three criteriaof firm-specific risks, development risks and external environment risks assuggested by Benaroch (2002). The firm-specific risks were further divided into foursub-criteria: organizational risks, user risks, requirement risks and structural risks.
Table I.The five deferrable ITinvestment opportunities
Figure 2.The fuzzy real optionvalues of the fivedeferrable IT investmentopportunities
Deferraltime
Project1
Project2
Project3
Project4
Project5
0FNPV =
((75%),17%,15%,126%)M = (10.5%)s = 71.5%
FNPV =(12%,20%,45%,56%)
M = 17.8%s = 24%
FNPV =(5%,24%,17%,218%)
M = 48.0%s = 56.0%
FNPV =((12%),85%,71%,6%)
M = 25.7%s = 62.0%
FNPV =((5%),12%,4%,358%)
M = 62.5%s = 81.0%
1FROV1 =
((90%),20%,18%,151%)M = (12.6%)s = 85.8%
FROV1 =(6%,26%,19%,240%)
M = 52.8%s = 61.6%
FROV1 =((15%),106%,89%,8%)
M = 32.1%s = 77.5%
FROV1 =((6%),13%,4%,394%)
M = 68.8%s = 89.1%
2FROV2 =
((104%),23%,21%,174%)M = (14.5%)s = 98.7%
FROV2 =(7%,31%,23%,288%)
M = 63.4%s = 73.9%
FROV2 =((7%),14%,5%,433%)
M = 75.7%s = 98.0%
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The development risks were further divided into two sub-criteria: team risks andcomplexity risks. External environment risks were further divided into two sub-criteria:competition risks and market risks.
Next, the possibilistic mean risk values of the investment opportunities presented inTable II were calculated.
In Phase 5, assuming a per annum investment, the deferrable IT investment strategywith the most value was determined using the following two-objective decision-makingmodel:
The optimal solution for model (F) given in Table III shows Projects 1 and 2 wererejected. Project 3 was approved for to start immediately, Project 4 was approved to startnext year and Project 5 was approved to start in two years.
4. Discussion and practical perspectivesIt is hard to say for sure which IT investment strategy is the best, but, we can make theselection process more comprehensive and systematic. The group decision process usedat Mornet was intended to enhance decision making and promote consensus. Our fourinvestment board members were highly educated; three of them held graduate degreesin business and one of them held a doctorate in economics. To this end, a more logical andpersuasive multi-criteria decision-making method was necessary to gain theirconfidence and support. Although our board members were educated and creative,their managerial judgment and intuition was limited by background and experience.One manager lacked strategic management skills while another had limited experiencein banking. Upon completion of the IT investment strategy selection process, we held ameeting with the board to discuss the results and finalize our recommendation. The fourboard members unanimously agreed that the proposed framework provided invaluableanalysis aids and information processing support. They were convinced that the resultwas unbiased and consistent.
Armed with this feedback, we were confident that we could sell our recommendationto the top management. Nevertheless, we were all aware that consensus building atMornet was a gradual process and could not be achieved overnight. We knew thatbuilding internal alliances and selecting an IT investment strategy that could cut acrossdifferent functional areas was a difficult task. The board members agreed to targetvarious groups and key people at Mornet in order to gain their support. They began
building internal alliances with functional units and focused their efforts on gettingother line managers on board. This process involved fostering collaboration andavoiding alienation of potential internal allies. The board also decided to get the linemanagers on board. Gaining the line management support resulted in the dedication ofsome line budget to the implementation process. This led to a virtuous circle since thefact that some line mangers agreed to pay for some of the implementation expensesincreased their commitment. This encouraged other line managers to jump on thebandwagon and participate in the selection process.
The internal alliance building process would not be complete without topmanagement support. Our board was adamant about the importance of gaining supportfrom the top management. Gaining the top management support was easier than it mayseem from the outside. The board members had already built internal alliances andsupport of various key people and line managers. We discussed the overwhelminginternal support and the tangible and intangible benefits of our IT investment strategywith the top management who in turn agreed to implement our recommendation. Wewere also required to develop a long-term plan to measure the IT investment selectionsuccess through qualitative and quantitative measures.
The analysis of this case study allows the articulation of a series of key factors thatcan be considered as important in contributing to the successful selection andimplementation of IT investment strategies. The first is building internal alliances. Thesecond element is getting the line managers on board. The third factor is the full andcontinual support given by top management. The fourth key ingredient is the persistentand systematic processes in place to measure the IT investment success.
5. Conclusions and future research directionsIT investments represent the largest capital expenditure items for many organizationsand have a tremendous impact on productivity by reducing costs, improving qualityand increasing value to customers. As a result, many organizations continue to investlarge sums of money in IT in anticipation of a material return on their investment. Theselection of appropriate IT investments has been one of the most significant businesschallenges of the last decade.
In this paper, we proposed a novel two-dimensional approach that determinedthe deferrable strategy with the most value by maximizing the real option valueswhile minimizing the risks associated with each alternative strategy. First, the deferrableinvestment strategies were prioritized according to their values using the ROA. Then, therisks associated with each investment strategy were quantified using the GFAHP. Finally,the values associated with the two dimensions were integrated to determine the deferrableIT investment strategy with the most value using a fuzzy preemptive goal programmingmodel. This framework can be easily generalized to N-dimensional problems. We havedeveloped a framework that can be used to evaluate IT investments based on the realoption concept. This approach incorporates the linkage among economic value, real optionvalue and IT investments that could lead to a better-structured decision process.
The proposed approach provides guidelines for managing IT investment projects.Managers face the difficulty that most IT investment projects are inherently risky,especially in a rapidly changing business environment. Over the past several years,increasingly sophisticated analytical techniques have been developed for selecting the ITinvestments, but not implemented within organizations. Our approach provides a simple,
Fuzzy goalprogramming
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191
intuitive, generic and comprehensive investment management tool. The trapezoidal fuzzynumbers used in this study allows the proposed model to be implemented easily with themost commonly used spreadsheet software. Managers can easily understand how toimplement the proposed approach to assess their technology portfolio requirements.
In contrast to the traditional ROA literature, our approach contributes to theliterature by incorporating a risk dimension parameter. We emphasize the importanceof categorizing risk management in IT investment projects since some risk cannot beeliminated. After estimating the possibility and severity of each risk factor, we obtainan overall risk level for each IT investment under consideration. This assumes byimplication that all risk factors are independent. However, in practice, there may besome interaction between different risk factors and their influence on the expectedpayoffs could be not independent. Future research considering correlation coefficientsbetween risk factors is rather challenging but necessary to gain insight into thisinteraction influence in the application of ROA to IT investment decisions.
We have developed a framework that can be used to evaluate IT investmentstrategies based on the real option concept. This approach incorporates the linkageamong economic value, real option value and IT investments that could lead to abetter-structured decision process. The overall contributions of the novel frameworkproposed in this study are threefold:
(1) Our framework addresses the gaps in the IT investment planning literature onthe effective and efficient assessment of IT investment opportunities.
(2) Our framework provides a comprehensive and systematic framework thatcombines ROA with a fuzzy group multi-criteria approach to assess ITinvestment strategies.
(3) Current IT investment assessment models are somewhat limited in their abilityto come to grips with issues of inference and fuzziness. Our frameworkconsiders fuzzy logic and fuzzy sets to represent ambiguous, uncertain orimprecise information in the It investment evaluation process.
Future research considering correlation coefficients between the risk and benefit factorsis rather challenging but necessary to gain insight into this interaction influence in theapplication of ROA to strategic IT investment decision in organizations. Anotherpossible future research direction is to investigate other drivers that influence the ITinvestment decisions. These value drivers could also be incorporated into the modelproposed in this study.
Note
1. The name is changed to protect the anonymity of the company.
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Appendix. The mathematical notationsLet us introduce the following mathematical notations and definitions used throughout thispaper:
cj The jth criterion.
ai The ith IT investment strategy.
p The number of IT investment risk criteria.
q The number of IT investment risk sub-criteria.
l The number of IT investment board members.
n The number of alternative IT investment strategies.
Ti The time to maturity of the ith IT investment strategy.
Tm The maximum deferral time of the IT investments.
T1 The minimum deferral time of the IT investments.
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ri The risk-free interest rate.
w(vp)K The voting power of the IT investment board member (ITIB)k (K ¼ 1,2, . . . , l ).
~Bk
i ðTjÞ The individual fuzzy present value of the expected cash flows of the ith ITinvestment strategy at time Tj evaluated by strategic IT investment boardmember (ITIB)k.
Bi(Tj) The weighted collective fuzzy present value of the expected cash flows of the ithIT investment strategy at time Tj.
E(Bi(Tj)) The possibilistic mean value of the weighted collective present value of expectedcash flows of the ith IT investment strategy at time Tj.
~Ck
i ðTjÞ The individual fuzzy present value of the expected cost of the ith IT investmentstrategy at time Tj evaluated by strategic IT investment board member (ITIB)k.
Ci(Tj) The weighted collective fuzzy present value of the expected cost of the ith ITinvestment strategy at time Tj.
E(Ci(Tj)) The possibilistic mean value of the weighted collective expected costs of the ithIT investment strategy at time Tj.
di The value loss over the duration of the option.
(s 2(Tj))i The variance of the weighted collective fuzzy present value of expected cashflows of the ith IT investment strategy at time Tj evaluated by strategic ITinvestment board member (ITIB)k.
N(D1i(Tj)) The IT investment strategy ith cumulative normal probability for the D1.
N(D2i(Tj)) The IT investment strategy ith cumulative normal probability for the D2.
~bk
ij The individual fuzzy comparison qualification between criterion i with criterion jevaluated by strategic IT investment board member (ITIB)k.
~dk
ij
� �p
The individual fuzzy comparison qualification between sub-criterion i withsub-criterion j with respect to criterion p evaluated by strategic IT investmentboard member (ITIB)k.
~rkij
� �q
The individual fuzzy comparison qualification between IT investment strategy iwith IT investment strategy j with respect to sub-criterion q evaluated bystrategic IT investment board member (ITIB)k.
~bij The weighted fuzzy collective comparison qualification between criterion i withcriterion j.
ð~dijÞj The weighted fuzzy collective comparison qualification between sub-criterion iwith sub-criterion j with respect to criterion j.
ð~rijÞj The weighted fuzzy collective comparison qualification between IT investmentstrategy i with IT investment strategy j with respect to sub-criterion j.
sþh The amount by which we numerically exceed the hth goal.
s2h The amount by which we numerically fall short of the hth goal.
~A2
R
� �K
The individual fuzzy pairwise comparison matrix of p criteria of IT investmentrisk evaluated by strategic IT investment board member (ITIB)k.
Fuzzy goalprogramming
model
195
~A3
R
� �K
The individual fuzzy pairwise comparison matrix of IT investment risksub-criteria with respect to the p IT investment risk criteria evaluated bystrategic IT investment board member (ITIB)k.
~A4
R
� �K
The individual fuzzy pairwise comparison matrix of n IT investment strategieswith respect to the q IT investment risk sub-criteria evaluated by strategic ITinvestment board member (ITIB)k.
~A2
R
� �The weighted fuzzy collective pairwise comparison matrix of the p IT investmentrisk criteria.
~A3
R
� �The weighted fuzzy collective pairwise comparison matrix of IT investment risksub-criteria with respect to the p IT investment risk criteria.
~A4
R
� �The weighted fuzzy collective pairwise comparison matrix of the n IT investmentstrategies with respect to the q IT investment risk sub-criteria.
~A2
R
� �K
The weighted fuzzy collective IT investment risk matrix evaluated by strategicIT investment board member (ITIB)k.
FROVi(Tj) The fuzzy real option value of the ith IT investment strategy at time Tj.
FRVi The fuzzy risk value of the ith IT investment strategy.
AFROV The fuzzy real option value matrix of the deferrable IT investment strategies.
FRV The fuzzy risk value vector of the IT investment strategies.
About the authorsFaramak Zandi is an Assistant Professor of Information Systems and Chairman of the IndustrialEngineering Department at Alzahra University in Iran. He holds a PhD in IndustrialEngineering. His research interests include IT, enterprise architectures, decision making, qualitymanagement systems and transportation planning. He has published in the International Journalof Business Information Systems, International Journal of Mathematics in Operational Research,International Journal of Information Technology and Management, IEEE Computer Society,Journal of Tehran University and Amirkabir Journal of Science & Technology and QuarterlyJournal of Educational Innovations.
Madjid Tavana is a Professor of Management Information Systems and Decision Sciences andthe Lindback Distinguished Chair of Information Systems at La Salle University where he servedas Chairman of the Management Department and Director of the Center for Technology andManagement. He has been a distinguished Faculty Fellow at NASA’s Kennedy Space Center,NASA’s Johnson Space Center, Naval Research Laboratory – Stennis Space Center and Air ForceResearch Laboratory. He was awarded the prestigious Space Act Award by NASA. He holds anMBA, a PMIS and a PhD in Management Information Systems. He received his Post-doctoralDiploma in strategic information systems from the Wharton School of the University ofPennsylvania. He is the Editor-in-Chief for the International Journal of Strategic Decision Sciences,The International Journal of Enterprise Information Systems and The International Journal ofApplied Decision Sciences. He has published in journals such as Decision Sciences, Interfaces,Information Systems, Information andManagement,Computers andOperationsResearch, Journalof the Operational Research Society and Advances in Engineering Software, among others.Madjid Tavana is the corresponding author and can be contacted at: [email protected]
BIJ18,2
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