International Journal of Information Technology & Decision Making

On behalf of the editorial advisory board of the International Journal of Information Technology and Decision Making (IT&DM), the Editor-in-Chief reviews the current research trend of this journal based on all the papers published in 2008. They are web-based decision analysis, credit scoring techniques and new data mining methods which combine both decision-making techniques and information technology tools. In addition, the Editor-in-Chief summarizes the key ideas of contributions in this new issue that may contain new research trend of IT&DM in 2009.

Tangibles have measurements generally on ratio scales with arbitrary units that are always interpreted by using judgments as to what particular purpose the measurements serve. How two measurements on a ratio scale are related with respect to dominance leads to forming their ratio which is a dimensionless number. The judgment of an expert can be used to estimate this ratio when the objects are homogeneous. The process of using judgments to make comparisons is then extended by clustering and using pivots to measure inhomogeneous objects or criteria. The paper shows the generality of the analytic network process (AHP) as a method of measurement comparing it with direct measurement and with the utility approach through an example. It is also shown with an application from economics that the judgment process can be sufficiently accurate to produce numerical outcomes that are close to what one obtains by conventional methods, thus serving to validate its use in the measurement of intangibles when informed people are involved.

We propose a data envelopment analysis (DEA)-based approach to ranking multi-criteria alternatives. We call it "the area of the efficiency score graph" (AES) approach. Unlike the classical DEA score and D_k measure that counts the number of DMUs (alternatives in DEA terminology) that should be removed from the set for each DMU k to become efficient, AES is not fully dependent on relative values of inputs/outputs of the alternatives in the set. It considers the change in efficiency scores of the alternatives while we delete 0,…,D_k number of alternatives from the set. The method avoids the negative effect of outliers and crowding in certain areas. It favors DMUs that manage to improve their efficiency scores quickly as we delete units from the set, and also alternatives that maintain high levels of efficiency scores as we delete units. We propose inclusion of weight restrictions into AES to incorporate decision maker preferences into the analysis. We apply our approach to ranking MBA programs. We provide rank lists of MBA programs by both AES and another DEA-based method for comparison. We also use AES scores to place programs in a small number of classes that are preference ordered from the best to the worst.

In this paper, we propose that information technology (IT) managers make investment decisions about new IT initiatives based on a modified rational expectation model. Unlike traditional rational expectation models, we emphasize the relevance of market uncertainty and its impact on the return of new IT investment. This results in information acquisition decisions by managers that can cause information asymmetry. This information asymmetry is endogenous and so the IT manager can become well informed if and only if it is beneficial to do so. We also capture different levels of IT investment across managers by introducing heterogeneity across managers in terms of different levels of initial capital. Based on a simulation analysis to validate our theoretical model, we find that it is the IT manager with larger initial capital outlay who is particularly interested in acquiring information about their IT investments in order to reduce any asymmetry with competitors. Furthermore, we find that holding other things constant, fewer IT investors are informed when information cost increases and in consequence the difference of investment level between the informed and uninformed investors is more pronounced.

This paper considers the evaluation step in a decision-making process that follows decision-making goals setting, feasible alternatives and attributes or criteria that characterize them determination steps. Evaluation step must establish a model or algorithm to evaluate alternatives taking into account their performances with regard to criteria as well as decision makers or stakeholders preferences. Though this problem is rather a classic one, researches related to evaluation model construction continue to be active to find models that cope with more realities or that fit well how human beings behave in group and proceed when facing the problem of choosing, ranking or sorting alternatives or options. The purpose of this paper is to construct an evaluation model that integrate the performances of alternatives with regard to attributes or criteria and decision makers or agents opinions with regard to the importance to assign to each criterion in order to obtain a value function. As any decision problem is almost always a matter of tradeoff, among attributes characterizing alternatives there will be those acting toward the achievement of decision makers goal (benefit) and those that decision makers would like to reduce as much as possible (cost); we will designate the first ones as positive attributes and the later ones as negative attributes. The process of dividing attributes into positive attributes and negative attributes is beyond the scope of this paper and this partition will be considered as a part of the problem specification. The model is constructed in two steps: firstly, satisfiability (selectability and rejectability) measures or functions are obtained for each alternative using attributes values (positive attributes will contribute to selectability measure whereas negative ones are used in the derivation of rejectability measure) and agents opinions in the framework of satisficing game theory and secondly a value function is built on that measures. Agents opinions with regard to attributes will be expressed locally by weighting them by category (positive/negative).

In a bilevel decision problem, both the leader and the follower may have multiple objectives, and the coefficients involved in these objective functions or constraints may be described by some uncertain values. To express such a situation, a fuzzy multi-objective bilevel (FMOBL) programming model and related solution methods are introduced. This research develops a FMOBL decision support system through implementing the proposed FMOBL methods.

Most group decision making (GDM) problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts' opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e. using term sets that are not uniformly and symmetrically distributed. The aim of this paper is to present a consensus model for GDM problems with unbalanced fuzzy linguistic information. This consensus model is based on both a fuzzy linguistic methodology to deal with unbalanced linguistic term sets and two consensus criteria, consensus degrees, and proximity measures. To do so, we use a new fuzzy linguistic methodology improving another approach to manage unbalanced fuzzy linguistic information,¹ (Int. J. Intell. Syst.22(11) (2007) 1197–1214), which uses the linguistic 2-tuple model as representation base of unbalanced fuzzy linguistic information. In addition, the consensus model presents a feedback mechanism to help experts for reaching the highest degree of consensus possible. There are two main advantages provided by this consensus model. First, its ability to cope with GDM problems with unbalanced fuzzy linguistic information overcoming the problem of finding different discrimination levels in linguistic term sets. Second, it supports the consensus process automatically, avoiding the possible subjectivity that the moderator can introduce in this phase.

This paper proposes a risk scoring model to assess the performance of 27 US companies listed online by applying Data Envelopment Analysis (DEA) and comparing with the traditional financial measure Return on Equity (ROE). The DEA evaluation process involves two processes: (1) computation of operating efficiency and effectiveness to measure a company's operating performance, and (2) measurement of the return level per unit of risk to provide guidance for their investors. The risk scoring model is useful for both investors and company managers. For investors, it yields a new stock selecting strategy. For managers, it provides a risk-adjusted performance evaluation process. Empirical results show that for the Internet industry, the effectiveness of a company is more important than operating efficiency. Investors investing in efficient online companies yield higher returns.

This work involves the novel application of a traditional technique for multivariate decision making to a complex problem in software selection. In particular, we formalize the problem statement "how does one select an appropriate commercial real-time operating system for a specific application" and we review two previous solutions to this decision making problem. Along the way, we will consider various aspects of the theoretical argument, broaden its application, and address the deficiencies of the two other solution strategies. Then, we present an improved solution to the problem using the well-developed Analytical Hierarchy Process (AHP), which is not traditionally used by software engineers. We then demonstrate the application of this method using the same criteria used in the previous methods. By explicitly representing preference, by providing tools that allow users to set and inspect their judgments, and by affording users with systematic evaluation procedure, the contribution is to help the decision maker to better identify an appropriate real-time operating systems solution without the need for intensive performance testing.

There have been few studies on the Key Success Factors (KSF) of TV shopping industry in both China and Taiwan. Moreover, the market growth potential and size across the Formosa Strait are significant. Using the Analytical Hierarchy Process (AHP) in Export Choice (EC) software, we find that the top ten KSF in the TV shopping industry across the Formosa Strait are somewhat different in order of importance and in the content. Managers in Taiwan consider watching habit, income, age, occupation, product positioning strategy, channel releasing policy, education, differential pricing, target product strategy and strategic sensitivity as the top ten KSF in order of importance. Meanwhile, managers in China consider domestic economic development, income, average domestic income, business philosophy, channel releasing policy, watching habit, product positioning strategy, related broadcasting laws, age, and occupation as the top ten KSF in order of importance.