This document proposes a framework for evaluating strategic information technology investment strategies. The framework uses fuzzy goal programming to integrate real option analysis with risk assessment. It involves five phases: 1) establishing an IT investment board, 2) identifying investment strategies, 3) prioritizing strategies using real option analysis, 4) prioritizing strategies based on risk assessment using group fuzzy analytic hierarchy process, and 5) developing an investment plan using a fuzzy goal programming model. The framework aims to determine the investment strategy with the most value by maximizing real option value while minimizing risk.
Dr. Prasanna Karhade is currently an Assistant Professor in the Department on Information Systems, Business Statistics and Operations Management at The Hong Kong University of Science and Technology.
The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
Drivers of e business value creation inIJMIT JOURNAL
With the development and growth of internet, its applications of e-banking, e-commerce, and e-business
became irreplaceable channels regarding its fast access, rich content, and smooth interactivity. High
investments are paid toward improving the quality of service offered by the banks. This paper is dedicated
to empirically investigating the drivers of e-Business value creation in the Jordanian banking sector. This
work summarizes the main differences among employees of Jordanian and foreign bank regarding their
perspectives. Many of the competing foreign banks to the Jordanian banks are enforced with huge financial
capital, having long periods of banking practices and are employing cutting-edge technologies and tools.
To minimize the technological gap, Jordanian banks are working hard to develop their e-Business services.
This in one hand has to enhance their trust, satisfaction, and commitment toward existing customers and
entice new comers on other hand. Based on business model of Amit and Zott, i.e. the four constructs of e-
Value framework (efficiency, complementarities, lock-in, and novelty), four hypotheses have been
formulated to test the differences in the drivers of e-Business value creation between Jordanian and foreign
banks. A survey questionnaire in a form of paper-and-pencil was delivered personally to 200 employees
from four main Jordanian banks and 200 employees from four foreign banks working in Jordan. The
questionnaire was formed and constructed to test the proposed hypotheses. the findings in this study based
on the SEM and T-test analyses, revealed important implications that will help banks’ managers to make
well-informed decisions and policies regarding investments and resources allocation for implementing e-
Business strategies and ventures. The paper concludes with discussing the importance of these findings for
practitioners and for future research on value accrued from e-Business services.
EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUESIAEME Publication
Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates.
Dr. Prasanna Karhade is currently an Assistant Professor in the Department on Information Systems, Business Statistics and Operations Management at The Hong Kong University of Science and Technology.
The Optimization of choosing Investment in the capital markets using artifici...inventionjournals
Optimization is one of crucial items in behavioural sciences. These daystheuse of Meta heuristic has grown considerably in all fields. In this study, we will look for optimization of selection in a portfolio of investment opportunities. We’ve been looking for a selection logic using a meta-heuristic algorithm Called artificial neural networks. The results showed that using artificial neural network algorithm had an optimization in decision-making and selection of investment opportunities. The research is applied one considering the purpose and is looking for developing knowledge in a particular field.
Drivers of e business value creation inIJMIT JOURNAL
With the development and growth of internet, its applications of e-banking, e-commerce, and e-business
became irreplaceable channels regarding its fast access, rich content, and smooth interactivity. High
investments are paid toward improving the quality of service offered by the banks. This paper is dedicated
to empirically investigating the drivers of e-Business value creation in the Jordanian banking sector. This
work summarizes the main differences among employees of Jordanian and foreign bank regarding their
perspectives. Many of the competing foreign banks to the Jordanian banks are enforced with huge financial
capital, having long periods of banking practices and are employing cutting-edge technologies and tools.
To minimize the technological gap, Jordanian banks are working hard to develop their e-Business services.
This in one hand has to enhance their trust, satisfaction, and commitment toward existing customers and
entice new comers on other hand. Based on business model of Amit and Zott, i.e. the four constructs of e-
Value framework (efficiency, complementarities, lock-in, and novelty), four hypotheses have been
formulated to test the differences in the drivers of e-Business value creation between Jordanian and foreign
banks. A survey questionnaire in a form of paper-and-pencil was delivered personally to 200 employees
from four main Jordanian banks and 200 employees from four foreign banks working in Jordan. The
questionnaire was formed and constructed to test the proposed hypotheses. the findings in this study based
on the SEM and T-test analyses, revealed important implications that will help banks’ managers to make
well-informed decisions and policies regarding investments and resources allocation for implementing e-
Business strategies and ventures. The paper concludes with discussing the importance of these findings for
practitioners and for future research on value accrued from e-Business services.
EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUESIAEME Publication
Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates.
APPLICATIONS OF DEEP LEARNING AND MACHINE LEARNING IN HEALTHCARE DOMAIN – A L...IAEME Publication
Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. Various automated systems and tools like Braincomputer interfaces (BCIs), arterial spin labelling (ASL) imaging, ASL-MRI, biomarkers, Natural language processing (NLP) and various algorithms helps to minimize errors and control disease progression. The computer assisted diagnosis, decision support systems, expert systems and implementation of software may assist physicians to minimize the intra and inter-observer variability. In this paper, a detailed literature review on application and implementation of Machine Learning, Deep Learning and Artificial Intelligence in the healthcare industry by various researchers.
Clustering Prediction Techniques in Defining and Predicting Customers Defecti...IJECEIAES
With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several ecommerce sites and compare their competitors‟ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-RecencyFrequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macroaveraging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers‟ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.
Voluntary Disclosure, Ownership Structure, Information Asymmetry and Cost of ...iosrjce
The aim of this study is at examining the influence of voluntary disclosure, information asymmetry
and ownership structure towards the cost of capital method by employing WACC (Weighted Average Cost of
Capital) method. The analysis method used in this research is the pathway analysis. The sample selection using
purposive sampling generates 93 observations (31 companies *3 years) manufacturing company in 2011-
2013. The results show that the extent of disclosures concerning the company's information will build investor
confidence in the investment, so that the expected rate of return is low and as a result the company’s incurred
capital costs is low. The low rate of return is due to the disclosure of required information by the company’s
management which would establish investor confidence in the investment.
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
Selecting Experts Using Data Quality Conceptsijdms
Personal networks are not always diverse or large enough to reach those with the right information. This
problem increases when assembling a group of experts from around the world, something which is a
challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel
of experts, specifically how to select a group of experts from a huge group of people. We propose an
approach which uses data quality dimensions to improve expert selection quality and provide quality
metrics to the forecaster. We performed a case study and successfully showed that it is possible to use data
quality methods to support the expert search process.
An Explanation Framework for Interpretable Credit Scoring gerogepatton
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
A presentation slides for ICEB 2019, UK, on the consumer research as part of Master's Degree requirement. The research was presented at the event of the 19th International Conference of Electronic Business, Newcastle upon Tyne, UK.
Corporate bankruptcy prediction using Deep learning techniquesShantanu Deshpande
Corporate Bankruptcy prediction using Recurrent neural networks – Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
APPLICATIONS OF DEEP LEARNING AND MACHINE LEARNING IN HEALTHCARE DOMAIN – A L...IAEME Publication
Artificial intelligence (AI) has been developing rapidly in recent years in terms of software algorithms, hardware implementation, and applications in a vast number of areas. In this review, we summarize the latest developments of applications of AI in biomedicine, including disease diagnostics, living assistance, biomedical information processing, and biomedical research. Various automated systems and tools like Braincomputer interfaces (BCIs), arterial spin labelling (ASL) imaging, ASL-MRI, biomarkers, Natural language processing (NLP) and various algorithms helps to minimize errors and control disease progression. The computer assisted diagnosis, decision support systems, expert systems and implementation of software may assist physicians to minimize the intra and inter-observer variability. In this paper, a detailed literature review on application and implementation of Machine Learning, Deep Learning and Artificial Intelligence in the healthcare industry by various researchers.
Clustering Prediction Techniques in Defining and Predicting Customers Defecti...IJECEIAES
With the growth of the e-commerce sector, customers have more choices, a fact which encourages them to divide their purchases amongst several ecommerce sites and compare their competitors‟ products, yet this increases high risks of churning. A review of the literature on customer churning models reveals that no prior research had considered both partial and total defection in non-contractual online environments. Instead, they focused either on a total or partial defect. This study proposes a customer churn prediction model in an e-commerce context, wherein a clustering phase is based on the integration of the k-means method and the Length-RecencyFrequency-Monetary (LRFM) model. This phase is employed to define churn followed by a multi-class prediction phase based on three classification techniques: Simple decision tree, Artificial neural networks and Decision tree ensemble, in which the dependent variable classifies a particular customer into a customer continuing loyal buying patterns (Non-churned), a partial defector (Partially-churned), and a total defector (Totally-churned). Macroaveraging measures including average accuracy, macro-average of Precision, Recall, and F-1 are used to evaluate classifiers‟ performance on 10-fold cross validation. Using real data from an online store, the results show the efficiency of decision tree ensemble model over the other models in identifying both future partial and total defection.
Voluntary Disclosure, Ownership Structure, Information Asymmetry and Cost of ...iosrjce
The aim of this study is at examining the influence of voluntary disclosure, information asymmetry
and ownership structure towards the cost of capital method by employing WACC (Weighted Average Cost of
Capital) method. The analysis method used in this research is the pathway analysis. The sample selection using
purposive sampling generates 93 observations (31 companies *3 years) manufacturing company in 2011-
2013. The results show that the extent of disclosures concerning the company's information will build investor
confidence in the investment, so that the expected rate of return is low and as a result the company’s incurred
capital costs is low. The low rate of return is due to the disclosure of required information by the company’s
management which would establish investor confidence in the investment.
Applying Convolutional-GRU for Term Deposit Likelihood PredictionVandanaSharma356
Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits.For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis caninfluence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludesthat proposed model attainsan accuracy of 89.59% and MSE of 0.1041 which outperform wellother baseline models.
Selecting Experts Using Data Quality Conceptsijdms
Personal networks are not always diverse or large enough to reach those with the right information. This
problem increases when assembling a group of experts from around the world, something which is a
challenge in Future-oriented Technology Analysis (FTA). In this work, we address the formation of a panel
of experts, specifically how to select a group of experts from a huge group of people. We propose an
approach which uses data quality dimensions to improve expert selection quality and provide quality
metrics to the forecaster. We performed a case study and successfully showed that it is possible to use data
quality methods to support the expert search process.
An Explanation Framework for Interpretable Credit Scoring gerogepatton
With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech),
applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This
deficiency of transparency limits their application in different domains including credit scoring. Credit
scoring systems help financial experts make better decisions regarding whether or not to accept a loan
application so that loans with a high probability of default are not accepted. Apart from the noisy and
highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the
`right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit
Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic
decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which
focuses on making black-box models more interpretable. In this work, we present a credit scoring model
that is both accurate and interpretable. For classification, state-of-the-art performance on the Home
Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient
Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework,
which provides different explanations (i.e. global, local feature-based and local instance- based) that are
required by different people in different situations. Evaluation through the use of functionally-grounded,
application-grounded and human-grounded analysis shows that the explanations provided are simple and
consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
A presentation slides for ICEB 2019, UK, on the consumer research as part of Master's Degree requirement. The research was presented at the event of the 19th International Conference of Electronic Business, Newcastle upon Tyne, UK.
Corporate bankruptcy prediction using Deep learning techniquesShantanu Deshpande
Corporate Bankruptcy prediction using Recurrent neural networks – Aim is to build a recurrent neural network-based model to predict whether company will become bankrupt or not using financial ratios of Polish companies.
Methodologies & Tools: CRISP-DM, SMOTE-ENN, GA Algorithm, LSTM network (type of RNN)
Risk Return Analysis - IT infrastructure - Risk ManagementJody Keyser
Applied Information Economics (AIE) Analysis
Of The Desktop Replacement Policy For The Environmental Protection Agency
Overview of the AIE Methodology
By: Douglas Hubbard
81119, 10(43 AMOriginality ReportPage 1 of 7httpsucum.docxblondellchancy
8/11/19, 10(43 AMOriginality Report
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Summer 2019 - InfoTech in a Global Economy (ITS-… • Week 14 - Written Assignment
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Sunil Kumar Reddy Donuru
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View Originality Report - Old Design
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11 M y p a p e rM y p a p e r 44 S t u d e n t p a p e rS t u d e n t p a p e r 22 S t u d e n t p a p e rS t u d e n t p a p e r
University of the Cumberlands
Sunil Reddy Donuru
Prof: Dr Jess Schwartz
The main purpose of this article is to show how the IT strategic emphases and IT investments can affect a firm’s profitability level and market
value. To further show the clear effect, the author used empirical tests founded from the documented information collected from over 300
firms in U.S. on this clear picture, the author concludes that organizations with a dual emphasis in their IT system have a higher Tobin's Q
than firms with an income or a cost emphasis at its mean estimation ventures. The author suggests that firms may decide on using IT strategic
emphasis for one reason alone and that is to moderate the strong correlation between IT investments and firm performance. The major
discovery of the research was that firms that have adopted dual-emphasis on IT strategies have a stronger IT–profitability relationship than sin-
gle-emphasis firms, these firms also have a stronger IT–Tobin’s Q relationship than firms that have adopted the revenue-emphasis strategy.
The conclusions from this research has been used all over the world by managers and business owners to help them in making informed deci-
sions on how to allocate assets for IT strategies that can efficiently support the overall goal of an organization. For average levels of IT ex-
penditure, a dual emphasis in IT strategy satisfies as long as a higher firm valuation, and a larger amount of IT speculations are made with d ...
Evaluating Total Cost of Ownership for University Enterprise Resource Plannin...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
JUSTIFICATION OF, AND BENEFITS REALIZATION BEYOND IT INVESTMENTS: ANALYSIS F...ijmvsc
This work looked at the justification of IT investments in general, to draw important conclusions that could
be beneficial to IT project managers and professionals, and then zero in on the angle of benefits realization
beyond IT investments.About 30 categories/sets of research outputs or articles out of more than 60 articles
reviewed were used for this work. No primary data was employed for this work. Articles were sourced from
databases such as Google Scholar, Research Gate, Academia.edu, Google search engine, Elsevier, and so
on. The main themes used for the search were IT investments, Justification for IT investments, IT
investments benefit determination, value creation beyond IT investments, and so on. The results showed
that justification is unique to every firm, it is contextual, and so stakeholders must take into consideration
environmental factors, corporate and strategic goals, experience and expertise of stakeholders, and so on,
to design its framework and measures to justify IT investments. Zeroing on benefits realization, two things
run through all the discussions: benefits realization of IT investments must take into consideration the
organization's strategic objectives and that they do not simply emerge, as if by magic. Their realization has
to be planned, delivered, reviewed, and exploited to ensure value realization on a more consistent/constant
basis.
We have concentrated on a range of strategies, methodologies, and distinct fields of research in this article, all of which are useful and relevant in the field of data mining technologies. As we all know, numerous multinational corporations and major corporations operate in various parts of the world. Each location of business may create significant amounts of data. Corporate decision-makers need access to all of these data sources in order to make strategic decisions.
CONTEXT, CONTENT, PROCESS” APPROACH TO ALIGN INFORMATION SECURITY INVESTMENTS...ijsptm
Today business environment is highly dependent on complex technologies, and information is considered
an important asset. Organizations are therefore required to protect their information infrastructure and
follow an inclusive risk management approach. One way to achieve this is by aligning the information
security investment decisions with respect to organizational strategy. A large number of information
security investment models have are in the literature. These models are useful for optimal and cost-effective
investments in information security. However, it is extremely challenging for a decision maker to select one
or combination of several models to decide on investments in information security controls. We propose a
framework to simplify the task of selecting information security investment model(s). The proposed
framework follows the “Context, Content, Process” approach, and this approach is useful in evaluation
and prioritization of investments in information security controls in alignment with the overall
organizational strategy.
Knowledge Management and Predictive Analytics in IT Project Risksijtsrd
"Knowledge management and predictive analytics are considered to be unusual partners in today’s technology. However, they can be very good tools that would solve current problems in valuing data. Predictive analytics has now become one of the forecasting tools that is of huge help in information management. Its application in IT project development risk management is very important, where a lot of raw data is involved with risk analysis and prediction. The use of IT project risk management as supported by knowledge management KM will help increase the success rate of IT projects. Knowledge management will bring about additional value to the data needed. This paper presents the usage of KM and predictive analytics to increase the success ratings of projects by predicting the risks that might happen during project development. It explores how KM and predictive analytics can identify risks in IT project development and give recommendations in evaluating the risks that could affect successful completion of IT projects. Mia Torres-Dela Cruz | Subashini A/P Ganapathy | Noor Zuhaili Binti Mohd Yasin ""Knowledge Management and Predictive Analytics in IT Project Risks"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Special Issue | International Conference on Advanced Engineering and Information Technology , November 2018, URL: https://www.ijtsrd.com/papers/ijtsrd19142.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/19142/knowledge-management-and-predictive-analytics-in-it-project-risks/mia-torres-dela-cruz"
Adomavicius et al.Technology Trends in the IT LandscapeSP.docxdaniahendric
Adomavicius et al./Technology Trends in the IT Landscape
SPECIAL ISSUE
MAKING SENSE OF TECHNOLOGY TRENDS IN THE
INFORMATION TECHNOLOGY LANDSCAPE:
A DESIGN SCIENCE APPROACH1
By: Gediminas Adomavicius
Information and Decision Sciences and
MIS Research Center
Carlson School of Management
University of Minnesota
321 19th Avenue South
Minneapolis, MN 55455
U.S.A.
[email protected]
Jesse C. Bockstedt
Information Systems and Operations Management
School of Management
George Mason University
4400 University Drive
Fairfax, VA 22030
U.S.A.
[email protected]
Alok Gupta
Information and Decision Sciences and
MIS Research Center
Carlson School of Management
University of Minnesota
321 19th Avenue South
Minneapolis, MN 55455
U.S.A.
[email protected]
1Sandeep Purao was the guest associate editor for this paper.
Robert J. Kauffman
Center for Advancing Business through
Information Technology
W. P. Carey School of Business and
School of Computing and Informatics
Arizona State University
Tempe, AZ 85257
U.S.A.
[email protected]
Abstract
A major problem for firms making information technology
investment decisions is predicting and understanding the
effects of future technological developments on the value of
present technologies. Failure to adequately address this
problem can result in wasted organization resources in ac-
quiring, developing, managing, and training employees in the
use of technologies that are short-lived and fail to produce
adequate return on investment. The sheer number of avail-
able technologies and the complex set of relationships among
them make IT landscape analysis extremely challenging.
Most IT-consuming firms rely on third parties and suppliers
for strategic recommendations on IT investments, which can
lead to biased and generic advice. We address this problem
by defining a new set of constructs and methodologies upon
which we develop an IT ecosystem model. The objective of
these artifacts is to provide a formal problem representation
structure for the analysis of information technology devel-
opment trends and to reduce the complexity of the IT
landscape for practitioners making IT investment decisions.
We adopt a process theory perspective and use a combination
MIS Quarterly Vol. 32 No. 4, pp. 779-809/December 2008 779
Adomavicius et al./Technology Trends in the IT Landscape
of visual mapping and quantification strategies to develop
our artifacts and a state diagram-based technique to repre-
sent evolutionary transitions over time. We illustrate our
approach using two exemplars: digital music technologies
and wireless networking technologies. We evaluate the utility
of our approach by conducting in-depth interviews with IT
industry experts and demonstrate the contribution of our
approach relative to existing techniques for technology
forecasting.
Keywords: Design science, IT ecosystem model, IT
landscape analysis, management of technology, technology
evolution, IT investment
Introduction
The information te ...
Multi-objective IT Project Selection Model for Improving SME Strategy Deploym...IJECEIAES
Due to the limited financial resources of small and Medium-sized enterprises (SMEs), the proven approaches for selecting IT project portfolio for large enterprises may fail to perform in SMEs; SME top management want to make sure that the corporate strategy is carried out effectively by IT project portfolio before investing in such projects. In order to provide automated support to the selection of IT projects, it seems inevitable that a multiobjective approach is required in order to balance possible competing and conflicting objectives. Under such an approach, individual projects would be evaluated not just on their own performance but on the basis of their contribution to balance the overall portfolio. In this paper, we extend and explore the concept of IT project selection to improve SME strategy deployment. In particular, we present a model that assesses an individual project in terms of its contribution to the overall strategic objectives of the portfolio. A simulation using the model illustrates how SME can rapidly achieve maximal business goals by deploying the multi-objective algorithm when selecting IT projects.
1. The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
BIJ
18,2 A fuzzy goal programming
model for strategic
information technology
172
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
Benchmarking: An International continue to invest large sums of money in IT in anticipation of a material return on their
Journal investment (Willcocks and Lester, 1996). The selection of appropriate IT investments has
Vol. 18 No. 2, 2011
pp. 172-196
q Emerald Group Publishing Limited
1463-5771
The authors would like to thank the anonymous reviewers and the Editor for their insightful
DOI 10.1108/14635771111121667 comments and suggestions.
2. been one of the most significant business challenges of the last decade. Powell (1992) Fuzzy goal
has studied the similarities and differences between IT investments and other capital programming
investments in organizations. He notes that IT investments are undertaken by
organizations to gain competitive advantage, to improve productivity, to enable new ways model
of managing and organizing and to develop new businesses. Appropriate strategic IT
investments can help companies gain and sustain a competitive advantage (Melville et al.,
2004). However, many large IT investment projects often do not meet original expectations 173
of cost, time or benefits. The rapid growth of IT investments has imposed tremendous
pressure on management to take into consideration risks and payoffs promised by the
investment in their decision making.
A review of the current literature offers several IT investment evaluation methods
that provide frameworks for the quantification of risks and benefits. The net present
value (NPV) (Hayes and Abernathy, 1980; Kaplan and Atkinson, 1998), return on
investment (Brealey and Myers, 1998; Farbey et al., 1993; Kumar, 2002; Luehrman,
1997), cost benefit analysis (Schniederjans et al., 2004), information economics (Bakos
and 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 to
assess the risks and payoffs associated with IT investments.
In addition to the above mentioned traditional quantitative approaches, there is a
stream of research studies which emphasizes real option analysis (ROA). The ROA differs
from the traditional methods in terms of priceability of the underlying investment project
(McGrath, 1997). With the traditional methods, the underlying investment project of an
option is priced as known (Black and Scholes, 1973) while in IT investment situations the
price of an underlying investment is rarely known (McGrath, 1997). The ROA uses three
basic 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 by
taking into consideration the changing nature of business strategies and
organizational 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 underlying
markets can be imitated accurately as a process. Although this assumption may hold for
some quite efficiently traded financial securities, it may not hold for real investments that
do not have existing markets (Collan et al., 2009). Recently, a simple novel approach to
ROA called the Datar-Mathews method (Datar and Mathews, 2004, 2007; Mathews and
Salmon, 2007) was proposed where the real option value is calculated from a pay-off
distribution, derived from a probability distribution of the NPV for an investment project
generated with a Monte-Carlo simulation. This approach does suffer from the market
process assumptions associated with the Black-Scholes method (Black and Scholes, 1974).
When valuating an investment using ROA, it is required to estimate several
parameters (i.e. expected payoffs and costs or investment deferral time). However, the
estimation of uncertain parameters in this valuation process is often very challenging.
Most traditional methods use probability theory in their treatment of uncertainty.
3. BIJ Fuzzy logic and fuzzy sets can represent ambiguous, uncertain or imprecise information
18,2 in 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 by
Zadeh (1965) is the formal body of theory that allows the treatment of imprecise
estimates in uncertain environments.
174 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 by
trapezoidal fuzzy numbers. Chen et al. (2007) developed a comprehensive but simple
methodology to evaluate IT investment in a nuclear power station based on fuzzy risk
analysis and real option approach. Frode (2007) used the conceptual real option
framework of Dixit and Pindyck (1994) to estimate the value of investment opportunities
in the Norwegian hydropower industry. Villani (2008) combined two successful theories,
namely real options and game theory, to value the investment opportunity and the value
of 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, the
assessment changes when information changes. As cash flows taking place in the future
come closer, information changes and uncertainty is reduced. Chrysafis and
Papadopoulos (2009) presented an application of a new method of constructing fuzzy
estimators for the parameters of a given probability distribution function using statistical
data. Wang and Hwang (2007) developed a fuzzy research and development portfolio
selection model to hedge against the environmental uncertainties. They applied fuzzy set
theory to model uncertain and flexible project information. Since traditional project
valuation methods often underestimate the risky project, a fuzzy compound-options
model was used to evaluate the value of each project. Their portfolio selection problem
was formulated as a fuzzy zero-one integer programming model that could handle both
uncertain and flexible parameters and determine the optimal project portfolio. A new
transformation method based on qualitative possibility theory was developed to convert
the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse
perspective. The transformed model was solved by an optimization technique.
We 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. First, the deferrable investment
strategies are prioritized according to their values using the ROA. Then, the risks
associated with each investment strategy are quantified using the group fuzzy analytic
hierarchy process (GFAHP). 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. The proposed framework:
.
addresses the gaps in the IT investment assessment literature on the effective
and efficient evaluation of IT investment strategies;
.
provides a comprehensive and systematic framework that combines ROA with
a group fuzzy approach to assess IT investment strategies;
.
considers fuzzy logic and fuzzy sets to represent ambiguous, uncertain or
imprecise information; and
4. .
it uses a real-world case study to demonstrate the applicability of the proposed Fuzzy goal
framework and exhibit the efficacy of the procedures and algorithms.
programming
This paper is organized into five sections. In Section 2, we illustrate the details of the model
proposed framework followed by a case study in Section 3. In Section 4, we present
discussion and practical perspectives and in Section 5, we conclude with our conclusions
and future research directions. 175
2. The proposed framework
The mathematical notations and definitions used in our model are presented in the
Appendix. The framework shown in Figure 1 is proposed to assess alternative IT
investment strategies. The framework consists of several steps modularized into five
phases.
Phase 1: establishment of the IT investment board
We institute a strategic IT investment board to acquire pertinent investment
information. Executive management is typically responsible for creating the board,
specifying its responsibilities and defining its resources. Let us assume that l strategic
IT investment board members are selected to participate in the evaluation process:
ITIB ¼ ½ðITIBÞ1 ; ðITIBÞ2 ; . . . ; ðITIBÞk ; . . . ; ðITIBÞl Š
Phase 2: identification of the IT investment strategies
Next, the strategic IT investment board identifies a set of alternative deferrable IT
investment strategies. Let us assume that n alternative IT investments with the
maximum deferral time of Tm are under consideration:
a ¼ ½a1 ; a2 ; . . . ; ai ; . . .an Š
Phase 3: prioritization of the IT investment strategies: real option considerations
In this phase, the real options equations suggested by Dos Santos (1994) are used to
prioritize IT investments strategies. This phase is divided into the following three steps.
Step 3.1: construction of the individual real option matrices. The following individual
real option matrices are given by each strategic IT investment board member:
~
BðT 1 Þ ~ ~ ~ ~
BðT 2 Þ . . . BðT m Þ CðT 1 Þ CðT 2 Þ . . . CðT m Þ ~
2 3
a1 ~k ~K ~k
B1 ðT 1 Þ B1 ðT 2 Þ . . . B1 ðT m Þ ~k ~k ~k
C1 ðT 1 Þ C1 ðT 2 Þ . . . C1 ðT m Þ
6 7
6 ~k ~k ~k ~k ~k ~k 7
~ k ¼ a2 6 B2 ðT 1 Þ B2 ðT 2 Þ . . . B2 ðT m Þ C2 ðT 1 Þ C2 ðT 2 Þ . . . C2 ðT m Þ 7
ARO1 6 7
. 6 .
. 6 . . . . . . 7 ð1Þ
. 6 . . . . . . 7
. ... . . . ... . 7
4 k
5
k
an B ðT Þ BK ðT Þ . . . BK ðT Þ C ðT Þ C k ðT Þ . . . C k ðT Þ
~
~ 1 2 m m 2 m
n n n n n n
For k ¼ 1; 2; . . . ; l:
Fuzzy numbers are often represented by triangular or trapezoidal fuzzy sets. In this
study, we use trapezoidal fuzzy sets. A major advantage of trapezoidal fuzzy numbers is
5. BIJ
Phase 1
18,2 Establishment of the IT investment board
Phase 2
Identification of the IT investment strategies
176
Phase 3
Prioritization of the IT investment strategies: real option considerations
Step 3.1
Construction of the individual real option
matrices
Step 3.2
Construction of the weighted collective real
option matrix
Step 3.3
Computation of the vector of the real option
value for the IT investment strategies
Phase 4
Prioritization of the IT investment strategies: risk considerations
Step 4.1
Identification of the criteria and sub-criteria
for the GFAHP model
Step 4.2
Construction of the individual fuzzy pairwise
comparison matrices
Step 4.3
Construction of the weighted collective fuzzy
pairwise comparison matrix
Step 4.4
Computation of the vector of the risk value for
the IT investment strategies
Phase 5
Development of the strategic IT investment plan
Step 5.1
Determination of the goal and priority levels
Step 5.2
Computation of the goal values
Step 5.3
Construction of the proposed goal
Figure 1. programming model
The proposed framework
6. that many operations based on the max-min convolution can be replaced by direct Fuzzy goal
arithmetic operations (Dubois and Prade, 1988). The following trapezoidal fuzzy numbers
are used for the individual fuzzy present values of the expected cash flows and the cost of
programming
the ith IT investment at time Tj by strategic IT investment board member (ITIB)k: model
b
o a g
~ k ðT j Þ ¼ Bk ðT j Þ ; Bk ðT j Þ ; Bk ðT j Þ ; Bk ðT j Þ
Bi i i i i
o
a b
g
177
~k
Ci ¼ C k ðT j Þ ; C k ðT j Þ ; C k ðT j Þ ; C k ðT j Þ ð2Þ
i i i i
For j ¼ 1; 2; . . . ; m:
That is, we have the following intervals:
j
o k
a
Bk ðT j Þ ; Bk ðT j Þ
i i the most possible values for the expected cash flows of
the ith IT investment at time Tj evaluated by strategic
IT investment board member (ITIB)k.
o
g
k k
Bi ðT j Þ þ Bi ðT j Þ the upward potential for the expected cash flows of the
ith IT investment at time Tj evaluated by strategic IT
b investment board member (ITIB)k.
o
Bk ðT j Þ 2 Bk ðT j Þ
i i the downward potential for the expected cash flows of
the ith IT investment at time Tj evaluated by strategic
IT investment board member (ITIB)k.
j
o k
a
k k
C i ðT j Þ ; C i ðT j Þ the most possible values of the expected cost of the ith
IT investment at time Tj evaluated by strategic IT
investment board member (ITIB)k.
o
g
k k
C i ðT j Þ þ C i ðT j Þ the upward potential for the expected cost of the ith IT
investment at time Tj evaluated by strategic IT
b investment board member (ITIB)k.
o
C k ðT j Þ 2 C k ðT j Þ
i i the downward potential for the expected cash flows of
the ith IT investment at time Tj evaluated by strategic
IT investment board member (ITIB)k.
Consequently, substituting equation (2) into matrix (1), the individual real option
matrices can be rewritten as:
~
BðT i Þ ~
CðT i Þ
2 o a b g o a b g 3
6 Bk ðT i Þ ; Bk ðT i Þ ; Bk ðT i Þ ; Bk ðT i Þ
1 1 1 1 C k ðT i Þ ; C k ðT i Þ ; C k ðT i Þ ; C k ðT i Þ
1 1 1 1 7
a1 6 7
6
6 o a b g o a b g 7
7
k k k k
6 B2 ðT i Þ ; B2 ðT i Þ ; B2 ðT i Þ ; B2 ðT i Þ k k k k 7
6 C 2 ðT i Þ ; C 2 ðT i Þ ; C 2 ðT i Þ ; C 2 ðT i Þ 7
~k
ARO1 ðT i Þ ¼ a2 6 7
6 7
. 6 .
. .
. 7
.
. 6 . . 7
6 7
6 o a b g 7
an 4 Bk ðT Þ o ; Bk ðT Þ a ; Bk ðT Þ b ; Bk ðT Þ g k k k k
C n ðT i Þ ; C n ðT i Þ ; C n ðT i Þ ; C n ðT i Þ 5
n i n i n i n i
ð3Þ
7. BIJ Step 3.2: construction of the weighted collective real option matrix. This framework
allows for assigning different voting power weights given to each investment board
18,2 member:
W ðvpÞ ¼ ½wðvpÞ1 ; wðvpÞ2 ; . . . ; wðvpÞj ; . . . ; wðvpÞl Š ð4Þ
Therefore, in order to form a fuzzy weighted collective real option matrix, the individual
178 fuzzy real option matrices will be aggregated by the voting powers as follows:
~
BðT i Þ ~
CðT i Þ
2 3
a1 ~
B1 ðT i Þ ~
C1 ðT i Þ
6~ ~ 7
6 B2 ðT i Þ C2 ðT i Þ 7
ARO2 ðT i Þ ¼ a2
~ 6 7 ð5Þ
. 6 . . 7
. 6 . . 7
. 6 . . 7
4 5
an ~
Bn ðT i Þ ~ n ðT i Þ
C
where:
Pl ~k
k¼1 ðwðvpÞk Þ Bi ðT i Þ
~
Bi ðT i Þ ¼ Pl ð6Þ
k¼1 wðvpÞk
Pl
~k
k¼1 ðwðvpÞk Þ Ci ðT i Þ
~
Ci ðT i Þ ¼ Pl ð7Þ
k¼1 wðvpÞk
Step 3.3: Computation of the vector of the real option value for the IT investment
strategies. The real option values of the investment strategies at times T 1 ; T 2 ; . . . ; T m
can be determined by the following fuzzy real option value matrix:
T1 T2 ... Tm
2 3
a1 FROV 1 ðT 1 Þ FROV 1 ðT 2 Þ ... FROV 1 ðT m Þ
6 7
6 FROV 2 ðT 1 Þ FROV 2 ðT 2 Þ ... FROV 2 T m 7
AFROV ¼ a2 6
~ 7 ð8Þ
. 6
. 6 .
. .
. .
.
7
7
. 6 . . ... . 7
4 5
a4 FROV n ðT 1 Þ FROV n ðT 2 Þ ... FROV n T m
or:
2 3 2 3
~ ~
a1 B1 ðT i Þ·e 2dT i ·N ðD11 ðT i ÞÞ2 C1 ðT i Þ·e 2rT i ·NðD21 ðT i ÞÞ FROV 1 ðT i Þ
6~ ~ 7 6 7
a2 6 B2 ðT i Þ·e 2dT i ·N ðD12 ðT i ÞÞ2 C2 ðT i Þ·e 2rT i ·NðD22 ðT i ÞÞ 7 6 FROV 2 ðT i Þ 7
6 7 6 7
AFROV ðT i Þ ¼ . 6
~ . 7¼6 . 7 ð9Þ
.6
.6 .
.
7 6
7 6 .
.
7
7
4 5 4 5
~ ~
a4 Bn ðT i Þ·e 2dT i ·N ðD1n ðT i ÞÞ2 Cn ðT i Þ·e 2rT i ·NðD2n ðT i ÞÞ FROV n ðT i Þ
8. where the IT investment strategy ith cumulative normal probabilities for the D1and D2 Fuzzy goal
are as follows:
programming
NðD1 ðT i ÞÞ N ðD2 ðT i ÞÞ model
2 3
a1 N ðD11 ðT i ÞÞ N ðD21 ðT i ÞÞ
6 7
6 N ðD12 ðT i ÞÞ
ARO3 ðT i Þ ¼ a2 6
N ðD22 ðT i ÞÞ 7
7 ð10Þ
179
. 6
. 6 .
. .
.
7
7
. 6 . . 7
4 5
an N ðD1n ðT i ÞÞ N ðD2n ðT i ÞÞ
D1 ðT i Þ D2 ðT i Þ
2 3
a1 D11 ðT i Þ D21 ðT i Þ
6 7
6 D ðT Þ D22 ðT i Þ 7
ARO4 ðTÞ ¼ a2 6 12 i 7 ð11Þ
. 6
. 6 .
. .
.
7
7
. 6 . . 7
4 5
an D1n ðT i Þ D2n ðT i Þ
or equivalently:
D1 ðT i Þ D2 ðT i Þ
a1 2 3
~ ~
LnðEðB1 ðT i ÞÞ=EðC1 ðT i ÞÞÞþð ðr 1 2d1 þs2 ðT i ÞÞ=2Þ · T i ~
LnðEðB1 ðT i ÞÞ=EðC1 ðT i ÞÞÞþð ðr1 2d1 2s2 ðT i ÞÞ=2Þ · T i
~
pffiffiffiffi 1 pffiffiffiffi 1
6 s1 ðT i Þ T i s2 ðT i Þ Ti 7
6 1
7
a2 6 LnðEðB2 ðT i ÞÞ=EðC2 ðT i ÞÞÞþð ðr2 2d2 þs2 ðT i ÞÞ=2Þ · T i
6 ~ ~ ~
LnðEðB2 ðT i ÞÞ=EðC2 ðT i ÞÞÞþð ðr2 2d2 2s2 ðT i ÞÞ=2Þ · T i
~ 7
7
6 pffiffiffiffi 2 pffiffiffi 2
7
ARO4 ðT i Þ ¼ 6 s2 ðT i Þ T i s2 ðT i Þ T 7
6 7
. 6
. 6
.
. .
.
7
7
. 6 . . 7
6 LnðEðB ðT ÞÞ=EðC ðT ÞÞÞþ r 2d þs2 ðT Þ =2 · T
~n ~ 7
4 ~n i i ð ðffiffiffiffi n n i Þ Þ i
pn LnðEðBn ðT i ÞÞ=EðCn ðT i ÞÞÞþð ðr n 2dn 2s2 ðT i ÞÞ=2Þ · T i 5
~
pffiffiffiffi n
an
sn ðT i Þ T i sn ðT i Þ T i
ð12Þ
2
where E and s denote the possibilistic mean value and possibilistic variance
operators as follows:
~
EðBðT i ÞÞ ~
EðCðT i ÞÞ s 2 ðT i Þ
2 3
a1 ~
EðB1 ðT i ÞÞ ~
EðC1 ðT i ÞÞ s2 ðT i Þ
1
6 7
6 EðB ðT ÞÞ ~ s2 ðT i Þ 7
ARO5 ðT i Þ ¼ a2 6 ~2 i EðC2 ðT i ÞÞ 2 7 ð13Þ
6 7
.
. 6 . . . 7
. 6 .
. .
. . 7
. 7
6
4 5
~
an EðBn ðT i ÞÞ ~
EðCn ðT i ÞÞ s2 ðT i Þ
n
9. ˜ ˜
BIJ Since Bi and Ci are trapezoidal fuzzy numbers, we use the formulas proposed by
´
Carlsson and Fuller (2003) to find their expected value and the variance:
18,2
~ ðBðT j ÞÞo þ ðBðT j ÞÞa ðBðT j ÞÞg 2 ðBðT j ÞÞb
EðBi ðT j ÞÞ ¼ þ
2 6
o a
~ ðCðT j ÞÞ þ ðCðT j ÞÞ ðCðT j ÞÞ 2 ðCðT j ÞÞb
g
180 EðCi ðT j ÞÞ ¼ þ
2 6
ððBðT j ÞÞa 2 ðBðT j ÞÞo Þ2 ððBðT j ÞÞa 2 ðBðT j ÞÞo ÞððBðT j ÞÞb þ ðBðT j ÞÞg Þ
s2 ðT j Þ ¼
i þ
4 6
ððBðT j ÞÞb þ ðBðT j ÞÞg Þ2
þ
24
ð14Þ
Phase 4: prioritization of the IT investment strategies: risk considerations
In this phase, the strategic IT investment board identifies the evaluation criteria and
sub-criteria and uses GFAHP to measure the risk for each criterion and sub-criterion
associated with the investment projects. This phase is divided into the following four
steps.
Step 4.1: identification of the criteria and sub-criteria for the GFAHP model. In this
step, the strategic IT investment board will determine a list of the criteria and
sub-criteria for the GFAHP model. Let c1 ; c2 ; . . . ; cp and sc1 ; sc2 ; . . . ; scq be the criteria
and sub-criteria, respectively.
Step 4.2: construction of the individual fuzzy pairwise comparison matrices. The
hierarchal structure for ranking the IT Investments strategies in the risk dimension
consists of four levels. The top level consists of a single element and each element of a
given level dominates or covers some or all of the elements in the level immediately
below. At the second level, the individual fuzzy pairwise comparison matrix of the p
criteria of IT investment risk evaluated by strategic IT investment board member
(ITIB)k will be as follows:
c1 c2 . . . cp
2 k 3
~ ~k ~k
c1 6 b11 b12 . . . b1p 7
2 k 6 7
c 6 ~k ~k ~k 7
AR ¼ 2 6 b21 b22 . . . b2p 7
~ ð15Þ
. 6 .
. 6 . .
7
. 7
. 6 . . ... . 7
. . 7
6
4 k
cp b k k 5
~ ~
b ... b~
p1 p2 pp
Let the individual fuzzy comparison qualification between criteria i and j evaluated by
strategic IT investment board member (ITIB)k be the following trapezoidal fuzzy
numbers:
o a b g
~k ¼
bij bk ; bk ; bk ; bk ð16Þ
ij ij ij ij
10. Consequently, substituting equation (18) into matrix (17), the individual fuzzy Fuzzy goal
comparison qualification between criteria i and j evaluated by strategic IT investment
board member (ITIB)k can be rewritten as:
programming
model
C1 c2 ... Cp
c1
2 3
ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ
11 11 11 11 12 12 12 12 ... ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ
1p 1p 1p 1p
2 k
6
c2 6 ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ
7
... ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ 7
181
ðAR Þ ¼ 6 21
~
6
21 21 21 22 22 22 22 2p 2p 2p 2p 7
7
.6
.
.6
.
. .
. .
.
7
7
6 . . ... . 7
4 5
cp ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ ððbk Þo ;ðbk Þa ;ðbk Þb ;ðbk Þg Þ k o k a k b k g
... ððbpp Þ ;ðbpp Þ ;ðbpp Þ ;ðbpp Þ Þ
p1 p1 p1 p1 p2 p2 p2 p2
ð17Þ
At the third level, the individual fuzzy pairwise comparison matrix of IT investment
risk sub-criteria with respect to p IT investment risk criteria evaluated by strategic IT
investment board member (ITIB)k will be as follows:
sc1 sc2 ... scq
2 k k k
3
d~ ~
d12 ... ~
d1q
sc1 6 11 P k P P 7
3 k 6 k 7
~ sc 6 d ~ ~
d22 ... ~k
d2q 7
AR ¼ 2 6 21 P 7 ð18Þ
. 6 P P7
.
. 6 . . . 7
6 . . . 7
6 . k. ... k. 7
scq 4 ~k ~ ~ 5
dq1 dq2 ... dqq
P P P
The individual fuzzy comparison qualification between sub-criterions i with
sub-criterion j with respect to criterion p evaluated by strategic IT investment board
member (ITIB)k are the following trapezoidal fuzzy numbers:
k o a b g
dij ¼ dk ; dk ; d k ; d k
~
ij ij ij ij ð19Þ
p p
Therefore, we have:
sc1 sc2 ... scq
sc1 2 3
ððdk Þo ;ðdk Þa ;ðdk Þb ;ðdk Þg Þp ððd k Þo ;ðdk Þa ;ðd k Þb ;ðd k Þg Þp
11 11 11 11 12 12 12 12 ... ððdk Þo ;ðd k Þa ;ðdk Þb ;ðdk Þg Þp
1q 1q 1q 1q
6 7
6 ððdk Þo ;ðdk Þa ;ðdk Þb ;ðdk Þg Þ ððd k Þo ;ðdk Þa ;ðd k Þb ;ðd k Þg Þ ... ððd k Þo ;ðd k Þa ;ðdk Þb ;ðdk Þg Þ 7
6 21 21 21 21 p 22 22 22 22 p 2q 2q 2q 2q 7
~3 sc 6 7
ðAR Þk ¼ 2 6 . . . 7
. 66 . . . 7
.
. 4
. . ... . 7
5
ððdq1 Þ ;ðdq1 Þ ;ðdq1 Þ ;ðdq1 Þ Þp ððdq2 Þ ;ðdq2 Þ ;ðd k Þb ;ðd k Þg Þp
k o k a k b k g k o k a
q2 q2 ... ððd k Þo ;ðd k Þa ;ðdk Þb ;ðdk Þg Þp
qq qq qq qq
scq
ð20Þ
At the fourth level, the individual fuzzy pairwise comparison matrix of n IT investment
strategies with respect to q IT investment risk sub-criteria evaluated by strategic
IT investment board member (ITIB)k will be as follows:
11. BIJ a1 a2 ... an
18,2 2À k Á À Á À Á 3
r
~ ~k
r12 ... ~k
r1n
a1 6 11 q q
7
q
4 k 6À k Á À k
Á À Á 7
~ a2 6 r21 q
6 ~ r22
~ q
... ~k 7
r2n q 7
AR ¼ 6 7 ð21Þ
182 . 6 .
. 6 . . . 7
. 6 . .
. ... . 7
. 7
6 7
4À k Á À Á À kÁ 5
an rn1 q
~ ~k
rn2 q
... rnn q
~
The individual fuzzy comparison qualification between IT investment strategies i with
IT investment strategy j with respect to sub-criterion q evaluated by strategic IT
investment board member (ITIB)k are the following trapezoidal fuzzy numbers:
o a b g
~k k k k k
rij ¼ r ij ; r ij ; r ij ; r ij ð22Þ
q q
or equivalently:
a1 a2 ... an
a1
2 3
ððr 11 Þo ;ðr11 Þa ;ðr11 Þb ;ðr 11 Þg Þq ððr 12 Þo ;ðr12 Þa ;ðr 12 Þb ;ðr 12 Þg Þq ... ððr 1n Þo ;ðr 1n Þa ;ðr1n Þb ;ðr 1n Þg Þq
k k k k k k k k k k k k
6 7
6 ððr k Þo ;ðr k Þa ;ðr k Þb ;ðr k Þg Þ ððr k Þo ;ðr k Þa ;ðr k Þb ;ðr k Þg Þ ... ððr k Þo ;ðr k Þa ;ðr k Þb ;ðr k Þg Þ 7
ðAR Þ k ¼ a2 6 21
~4 6 21 21 21 q 22 22 22 22 q 2n 2n 2n 2n q7
7
6 7
. 6
. 6 .
. .
. .
. 7
. 6 . . ... . 7
7
4 5
k o k a k b k g k o k a k b k g k o k a k b k g
an ððr n1 Þ ;ðr n1 Þ ;ðrn1 Þ ;ðrn1 Þ Þq ððrn2 Þ ;ðrn2 Þ ;ðr n2 Þ ;ðrn2 Þ Þq ... ððrnn Þ ;ðrnn Þ ;ðrnn Þ ;ðrnn Þ Þq
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 IT
investment risk criteria will be as follows:
c1 c2 ... cp
c1 2 3
ððb11 Þo ;ðb11 Þa ;ðb11 Þb ;ðb11 Þg Þ ððb12 Þo ;ðb12 Þa ;ðb12 Þb ;ðb12 Þg Þ ... ððb1p Þo ;ðb1p Þa ;ðb1p Þb ;ðb1p Þg Þ
6 7
6 ððb Þo ;ðb Þa ;ðb Þb ;ðb Þg Þ ððb Þo ;ðb Þa ;ðb Þb ;ðb Þg Þ ... ððb Þo ;ðb Þa ;ðb Þb ;ðb Þg Þ 7
6 21 21 21 21 22 22 22 22 2p 2p 2p 2p 7
6 7
~2 c
AR ¼ 2 6 7
6 .
. .
. .
. 7
.6
.6
. . ... . 7
7
.4 5
ððbp1 Þo ;ðbp1 Þa ;ðbp1 Þb ;ðbp1 Þg Þ ððbp2 Þo ;ðbp2 Þa ;ðbp2 Þb ;ðbp2 Þg Þ ... ððbpp Þo ;ðbpp Þa ;ðbpp Þb ;ðbpp Þg Þ
cp
ð24Þ
12. or: Fuzzy goal
c1 c2 . . . cp
programming
2~ ~ ~ 3 model
c1 b11 b12 ... b1p
6~ ~ ~ 7
~2 c 6 b21 b22 ... b2p 7
AR ¼ 2 6 7 ð25Þ
6 . . 7
.
. 6 .
6 .
.
. . 7 183
. . ... . 7
4 5
cp ~
bp1 ~
bp2 ... ~
bpp
where:
Pl k !
~
k¼1 ðwðvpÞk Þ bij
j
~
ðbij Þj ¼ Pl ð26Þ
k¼1 wðvpÞk
At the third level, the fuzzy weighted collective pairwise comparison matrix of the IT
investment risk sub-criteria with respect to the p IT investment risk criteria will be as
follows:
sc1 sc2 ... scq
2 o a b g o a b g 3
sc1 ððd 11 Þ ; ðd 11 Þ ; ðd 11 Þ ; ðd 11 Þ Þp ððd 12 Þ ; ðd 12 Þ ; ðd12 Þ ; ðd 12 Þ Þp ... ððd 1q Þ ; ðd 1q Þa ; ðd 1q Þb ; ðd 1q Þg Þp
o
6 7
~3 sc 6 ððd 21 Þo ; ðd 21 Þa ; ðd 21 Þb ; ðd 21 Þg Þp ððd 22 Þo ; ðd 22 Þa ; ðd22 Þb ; ðd 22 Þg Þp ... ððd 2q Þo ; ðd 2q Þa ; ðd 2q Þb ; ðd 2q Þg Þ 7
AR ¼ 2 6 7
. 6 . . . 7
. 6 . . . 7
. 6 . . ... . 7
4 5
scq ððd q1 Þ ; ðd q1 Þ ; ðd q1 Þb ; ðd q1 Þg Þp
o a
ððd q2 Þo ; ðd q2 Þa ; ðdq2 Þb ; ðd q2 Þg Þp ... o a b
ððd qq Þ ; ðd qq Þ ; ðd qq Þ ; ðd qq Þ Þpg
ð27Þ
or:
sc1 sc2 ... scq
2 ~ ~ ~ 3
sc1 ðd11 ÞP ðd12 ÞP ... ðd1q ÞP
6 ~ ~ ~ 7
~3 sc 6 ðd21 ÞP ðd22 ÞP ... ðd2q ÞP 7
AR ¼ 2 6 7 ð28Þ
. 6 . . . 7
. 6 . . . 7
. 6 . . ... . 7
4 5
scq ~
ðdq1 ÞP ~
ðdq2 ÞP ... ~
ðdqq ÞP
where:
Pl k !
~
ðwðvpÞk Þ dij
k¼1
p
~
ðdij Þj ¼ Pl ð29Þ
k¼1 wðvpÞk
At the fourth level, the fuzzy weighted collective pairwise comparison matrix of the n
IT investment strategies with respect to the q IT investment risk sub-criteria will be as
follows:
13. BIJ a1
a1 a2 ... an
2 3
18,2 ððr 11 Þ ;ðr 11 Þ ;ðr 11 Þ ;ðr 11 Þ Þq ððr 12 Þ ;ðr 12 Þ ;ðr12 Þ ;ðr 12 Þ Þq ... ððr 1n Þ ;ðr 1n Þ ;ðr 1n Þb ;ðr1n Þg Þq
o a b g o a b g o a
6 7
6 ððr 21 Þo ;ðr 21 Þa ;ðr 21 Þb ;ðr 21 Þg Þq ððr 22 Þo ;ðr 22 Þa ;ðr22 Þb ;ðr 22 Þg Þq ... ððr 2n Þo ;ðr 2n Þa ;ðr 2n Þb ;ðr2n Þg Þq 7
AR ¼ a2 6
~4 7
6
. 6 . . . 7
. 6 . . . 7
. . . ... . 7
4 5
184 o a b g o a b g o a
an ððr n1 Þ ;ðr n1 Þ ;ðr n1 Þ ;ðrn1 Þ Þq ððr n2 Þ ;ðr n2 Þ ;ðrn2 Þ ;ðrn2 Þ Þq ... ððr nn Þ ;ðrnn Þ ;ðr nn Þ ;ðr nn Þ Þq b g
ð30Þ
or:
a1 a2 ... an
2 3
a1 ð~11 Þq
r ð~12 Þq
r ... ð~1n Þq
r
6 7
6 ð~21 Þq
r ð~22 Þq
r ... ð~2n Þq 7
r
A 4 ¼ a2 6
~ 7 ð31Þ
6 .
. 6 . . . 7
. 6 .
. .
. ... . 7
. 7
4 5
an ð~n1 Þq
r ð~n2 Þq
r ... ð~nn Þq
r
where:
Pl
k¼1 ðwðvpÞk Þ rk
~ij
rij ¼
~ Pl ð32Þ
k¼1 wðvpÞk
Step 4.4: computation of the vector of the risk value for the IT investment strategies. The
fuzzy composite vector of the deferrable IT investment strategies at the fourth level
will be calculated based on the corresponding eigenvectors:
~ ~ ~2
FRV ¼ A 4 · A 3 · W R ¼ ½ FRV 1 FRV 2 ... FRV n ŠT ð33Þ
or:
FRV ¼ ½ððFRV Þo ; ðFRV Þa ; ðFRV Þb ; ðFRV Þg ÞR1
ððFRV Þo ; ðFRV Þa ; ðFRV Þb ; ðFRV Þg ÞR2 . . . ððFRV Þo ; ðFRV Þa ; ðFRV Þb ; ðFRV Þg ÞRn ÞŠT
ð34Þ
where:
~ ~4
A4 ¼ b W R 1 ~4
W R2 ... ~4
W Rq c ð35Þ
~ ~3
A 3 ¼ b W R1 ~3
W R2 ... ~3
W Rp c ð36Þ
h
~2
AR · e
~2
W R ¼ Lim 2 h h!1 ð37Þ
~
e T · AR · e
14. h
~3
AR · e Fuzzy goal
~3
W Rp ¼ Lim 3 h h!1 ð38Þ programming
eT · A~ ·e R model
4 h
~
AR · e
~4
W Rq ¼ Lim 4 h h!1 ð39Þ
~ 185
e T · AR · e
e ¼ ð1 1 . . . 1 ÞT ð40Þ
Phase 5: development of the strategic IT investment plan
Decision makers also must consider the interaction between the real option and the
investment risks. Therefore, in this phase, the IT investment strategy with the most
value is determined in terms of real option and risk values in Phases 2 and 3. For this
purpose, they are considered as the coefficients of the objective functions in the
following fuzzy preemptive goal programming model with a series of applicable
constraints. This phase is divided into the following three steps.
Step 5.1: determination of the goal and priority levels. The goals in the fuzzy
preemptive goal programming model can be written as follows:
For the first priority level, there are two goals. These goals are equally important so
they can have the same weight:
Max Z 1 ¼ E½FROV 1 ðT 1 ÞŠ · x11 þ E½FROV 1 ðT 2 ÞŠ · x12 þ · · · þ E½FROV 1 ðT m ÞŠ · x1m þ
E½FROV 2 ðT 1 ÞŠ · x21 þ E½FROV 2 ðT 2 ÞŠ · x22 þ · · · þ E½FROV 2 ðT m ÞŠ · x2m þ
.
.
.
E½FROV n ðT 1 ÞŠ · xn1 þ E½FROV n ðT 2 ÞŠ · xn2 þ · · · þ E½FROV n ðT m ÞŠ · xnm
Min Z 2 ¼ EðFRV 1 Þ · ðx11 þ x12 þ · · · þ x1m Þ þ EðFRV 2 Þ · ðx21 þ x22 þ · · · þ x2m Þþ
· · · þ EðFRV n Þ · ðxn1 þ xn2 þ · · · þ xnm Þ
For the second priority level, we have:
f 1 ðx11 ; x12 ; . . . ; xnm Þ # 0
f 2 ðx11 ; x12 ; . . . ; xnm Þ # 0
.
.
.
f r ðx11 ; x12 ; . . . ; xnm Þ # 0
xi ¼ 0; 1 ði ¼ 1; 2; . . . ; nÞ
15. BIJ Max Z 1 ¼ E½FROV 1 ðT 1 ÞŠ · x11 þ E½FROV 1 ðT 2 ÞŠ · x12 þ · · · þ E½FROV 1 ðT m ÞŠ · x1m þ
18,2 E½FROV 2 ðT 1 ÞŠ · x21 þ E½FROV 2 ðT 2 ÞŠ · x22 þ · · · þ E½FROV 2 ðT m ÞŠ · x2m þ
.
.
.
E½FROV n ðT 1 ÞŠ · xn1 þ E½FROV n ðT 2 ÞŠ · xn2 þ · · · þ E½FROV n ðT m ÞŠ · xnm
186
Min Z 2 ¼ EðFRV 1 Þ · ðx11 þ x12 þ · · · þ x1m Þ þ EðFRV 2 Þ · ðx21 þ x22 þ
· · · þ x2m Þ þ · · · þ EðFRV n Þ · ðxn1 þ xn2 þ · · · þ xnm Þ
Subject to: (Model P)
x11 þ x12 þ · · · þ x1m # 1
x21 þ x22 þ · · · þ x2m # 1
.
.
.
xn1 þ xn2 þ · · · þ xnm # 1
f 1 ðx11 ; x12 ; . . . ; xnm Þ # 0
f 2 ðx11 ; x12 ; . . . ; xnm Þ # 0
.
.
.
f r ðx11 ; x12 ; . . . ; xnm Þ # 0
xij ¼ 0; 1 ði ¼ 1; 2; . . . ; n; j ¼ 1; 2; . . . ; mÞ
where f i ðx1 ; x2 ; . . . ; xn Þ 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 goal
or target value that is the most desirable value for that function.
Step 5.3: construction of the proposed goal programming model. The first objective
function is to be maximized and the second objective function is to be minimized.
Therefore, the proposed fuzzy goal programming model for the above two-objective
strategic IT investment decision will be the following single-objective model:
À Á
Min D ¼ P 1 sþ þ s2 þ P 2 s2 þ · · · þ P rþ2 s2
1 2 3 r
Subject to: (Model F)
E½FROV 1 ðT 1 ÞŠ · x11 þ E½FROV 1 ðT 2 ÞŠ · x12 þ · · · þ E½FROV 1 ðT m ÞŠ · x1m þ
E½FROV 2 ðT 1 ÞŠ · x21 þ E½FROV 2 ðT 2 ÞŠ · x22 þ · · · þ E½FROV 2 ðT m ÞŠ · x2m þ
.
.
.
E½FROV n ðT 1 ÞŠ · xn1 þ E½FROV n ðT 2 ÞŠ · xn2 þ · · · þ E½FROV n ðT m ÞŠ · xnm
S2 2 Sþ ¼ l1
1 1
16. EðFRV 1 Þ · ðx11 þ x12 þ · · · þ x1m Þ þ EðFRV 2 Þ · ðx21 þ x22 þ Fuzzy goal
· · · þ x2m Þ þ · · · þ EðFRV n Þ · ðxn1 þ xn2 þ · · · þ xnm Þ þ s2 2 sþ ¼ u1
2 2 programming
f 1 ðx11 ; x12 ; . . . ; xnm Þ þ sþ þ sþ ¼ 0 model
3 3
f 2 ðx11 ; x12 ; . . . ; xnm Þ þ sþ þ s2 ¼ 0
4 4
.
.
.
187
f r ðx11 ; x12 ; . . . ; xnm Þ þ sþ þ s2 ¼ 0
rþ2 rþ2
x11 þ x12 þ · · · þ x1m # 1
x21 þ x22 þ · · · þ x2m # 1
.
.
.
xn1 þ xn2 þ · · · þ xnm # 1
xij ¼ 0; 1 ði ¼ 1; 2; . . . ; n; j ¼ 1; 2; . . . ; mÞ
sþ ; s2
h h $0 ðh ¼ 1; 2; . . . ; r þ 2Þ
sþ · s2 ¼ 0
h h
The optimal solution for model (F) is the deferrable IT investment strategy with the
most values at the time Ti. Next, we present a numerical example to demonstrate the
implementation process of this framework.
3. Case study
We implemented the proposed model at Mornet[1], a large mortgage company in the
city of Philadelphia with an urgent need to select an optimal IT investment strategy for
their deferrable investment opportunities.
In Phase 1, the chief executive officer instituted a committee of four strategic IT
investment 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 investment
opportunities 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) and
can 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).
17. BIJ a3. Project 3 has revenues with large upward potentials and managerial flexibility,
18,2 but its “reserve costs” (c) are very high.
a4. Project 4 requires a large capital expenditure once it has been undertaken and
has a deferral flexibility of a maximum of one year.
a5. Project 5 represents a small flexible project with low revenues, but it opens the
188 possibility of further projects that are much more profitable.
In Phase 3, the fuzzy real option values of the five different deferrable investment
opportunities shown in Figure 2 were determined for years 1 and 2.
In Phase 4, the strategic IT investment board determined the GFAHP three criteria
of firm-specific risks, development risks and external environment risks as
suggested by Benaroch (2002). The firm-specific risks were further divided into four
sub-criteria: organizational risks, user risks, requirement risks and structural risks.
Deferral
time Project 1 Project 2 Project 3 Project 4 Project 5
0 FNPV ¼ ((75%), FNPV ¼ (12%, FNPV ¼ (5%, FNPV ¼ ((12%), FNPV ¼ ((5%),
Table I. 17%, 15%, 126%) 20%, 45%, 56%) 24%, 17%, 218%) 85%, 71%, 6%) 12%, 4%, 358%)
The five deferrable IT 1 U U U U
investment opportunities 2 U U U
Deferral Project Project Project Project Project
time 1 2 3 4 5
0
FNPV = FNPV = FNPV = FNPV = FNPV =
((75%),17%,15%,126%) (12%,20%,45%,56%) (5%,24%,17%,218%) ((12%),85%,71%,6%) ((5%),12%,4%,358%)
M = (10.5%) M = 17.8% M = 48.0% M = 25.7% M = 62.5%
s = 71.5% s = 24% s = 56.0% s = 62.0% s = 81.0%
1
FROV1 = FROV1 = FROV1 = FROV1 =
((90%),20%,18%,151%) (6%,26%,19%,240%) ((15%),106%,89%,8%) ((6%),13%,4%,394%)
M = (12.6%) M = 52.8% M = 32.1% M = 68.8%
s = 85.8% s = 61.6% s = 77.5% s = 89.1%
Figure 2.
The fuzzy real option 2
values of the five FROV2 = FROV2 = FROV2 =
deferrable IT investment ((104%),23%,21%,174%) (7%,31%,23%,288%) ((7%),14%,5%,433%)
M = (14.5%) M = 63.4% M = 75.7%
opportunities s = 98.7% s = 73.9% s = 98.0%
18. The development risks were further divided into two sub-criteria: team risks and Fuzzy goal
complexity risks. External environment risks were further divided into two sub-criteria:
competition risks and market risks.
programming
Next, the possibilistic mean risk values of the investment opportunities presented in model
Table II were calculated.
In Phase 5, assuming a per annum investment, the deferrable IT investment strategy
with the most value was determined using the following two-objective decision-making 189
model:
Min Z 2 ¼ 0:45ðx10 þ x11 þ x12 Þ þ 0:1x20 þ 0:35ðx30 þ x31 þ x32 Þ þ 0:15ðx40 þ x41 Þ
þ 0:05ðx50 þ x51 þ x52 Þ
Subject to: (Model P)
x10 þ x11 þ x12 # 1
x21 # 1
x30 þ x31 þ x32 # 1
x40 þ x41 # 1
x50 þ x51 þ x52 # 1
x10 þ x20 þ x30 þ x40 þ x50 # 1
x11 þ x31 þ x41 þ x51 # 1
x12 þ x32 þ x52 # 1
x10 ; x11 ; x12 ; x20 ; x30 ; x31 ; x32 ; x40 ; x41 ; x50 ; x51 ; x52 ¼ 0; 1
Therefore, the goal programming model for the above two-objective strategic IT
investment decision will be the following single objective model:
À Á
Min D ¼ P 1 · s2 þ sþ
1 2
Subject to: (Model F)
ð20:105Þx10 þ ð20:126Þ · x11 þ ð20:145Þ · x12 þ 0:178x20 þ 0:48x30 þ 0:528x31
þ 0:634x32 þ 0:257x40 þ 0:321x41 þ 0:625x50 þ 0:688x51 þ 0:757x52
À Á
þ s2 2 sþ ¼ 1:5
1 1
0:45ðx10 þ x11 þ x12 Þ þ 0:1x20 þ 0:35ðx30 þ x31 þ x32 Þ þ 0:15ðx40 þ x41 Þ
À Á
þ 0:05ðx50 þ x51 þ x52 Þ þ s2 2 sþ ¼ 0:6
2 2
x10 þ x11 þ x12 # 1
x20 # 1
Table II.
Project 1 Project 2 Project 3 Project 4 Project 5 The possibilistic mean
risk value of the IT
E(FRV1) ¼ 0.45 E(FRV2) ¼ 0.10 E(FRV3) ¼ 0.35 E(FRV4) ¼ 0.15 E(FRV5) ¼ 0.05 investment opportunities