International Journal of Information Technology & Decision Making

This paper describes the design and proof of concept of a web-based e-business decision-making support system (EBDMSS), to deliver a balanced scorecard (BSC)-based modeling and analysis in support of e-business strategic management. EBDMSS is designed, built and validated through a conceptual design research method. Its design is theoretically underpinned in three DMSS design schemes: an e-business BSC (EBBSC) framework for decision-making process (DMP), a DMSS architecture, and a DMSS design-evaluation framework. The EBBSC scheme aims to support e-business managers during the business strategy making process in a comprehensive, integrated, and continuous manner. The DMSS architecture and the DMSS design-evaluation frameworks help to deliver the expected design capabilities of an integrated DMSS. The paper reports related literature review, conceptual design methodology and principles, EBDMSS design and prototype implementation, and illustrates how practitioners can use this system to deliver a valuable range of embodied e-business strategy expertise in support of real-time decision making. The paper concludes with limitations of design, implementation and validation, recommendations for further research, and conclusions.

The objective of this paper is to provide a more comprehensive efficiency measure to estimate the performance of OECD and non-OECD countries. A Russell directional distance function that appropriately credits the decision-making unit not only for increase in desirable outputs but also for the decrease of undesirable outputs is derived from the proposed weighted Russell directional distance model. The method was applied to a panel of 116 countries from 1992 to 2010. This framework also decomposes the comprehensive efficiency measure into individual input/output components' inefficiency scores that are useful for policy making. The results reveal that the OECD countries perform better than the non-OECD countries in overall, goods, labor and capital efficiencies, but worse in bad and energy efficiencies.

Deterministic record linkage (RL) is frequently regarded as a rival to more sophisticated strategies like probabilistic RL. We investigate the effect of combining deterministic linkage with other linkage techniques. For this task, we use a simple deterministic linkage strategy as a preceding filter: a data pair is classified as ‘match' if all values of attributes considered agree exactly, otherwise as ‘nonmatch'. This strategy is separately combined with two probabilistic RL methods based on the Fellegi–Sunter model and with two classification tree methods (CART and Bagging). An empirical comparison was conducted on two real data sets. We used four different partitions into training data and test data to increase the validity of the results. In almost all cases, application of deterministic linkage as a preceding filter leads to better results compared to the omission of such a pre-filter, and overall classification trees exhibited best results. On all data sets, probabilistic RL only profited from deterministic linkage when the underlying probabilities were estimated before applying deterministic linkage. When using a pre-filter for subtracting definite cases, the underlying population of data pairs changes. It is crucial to take this into account for model-based probabilistic RL.

This paper provides a systematic study of the technologies and algorithms associated with the implementation of multiobjective evolutionary algorithms (MOEAs) for the solution of the portfolio optimization problem. Based on the examination of the state-of-the art we provide the best practices for dealing with the complexities of the constrained portfolio optimization problem (CPOP). In particular, rigorous algorithmic and technical treatment is provided for the efficient incorporation of a wide range of real-world constraints into the MOEAs. Moreover, we address special configuration issues related to the application of MOEAs for solving the CPOP. Finally, by examining the state-of-the-art we identify the most appropriate performance metrics for the evaluation of the relevant results from the implementation of the MOEAs to the solution of the CPOP.

In this paper, we perform the analysis of temporalized structure of bodies of evidence to construct more precise decisions based on the mathematical model of experts’ evaluations. The relation of information precision is defined on a monotone sequence of the bodies of evidence. For determining of a body of evidence the maximum principles of nonspecificity measure, the Shannon and Shapley entropies are applied. Corresponding mathematical programming problems are constructed. A new approach for the numerical solution of these problems is developed. The temporalized structure of bodies of evidence is used for precising the decision in the well-known Kaufmann’s theory of expertons. A measure of increase of decision precision is introduced, which takes into account all steps of temporalization. The temporalized method of expertons is applied to the problem of decision risk management, where the investment fund expert commission provides evaluation of competition results. In our specially created decision-making model, the goal of the expert technology is to aggregate and refine subjective evaluations provided by the expert commission members. The model performs as an adviser that assists the expert commission in selecting of decision with a minimum risks. The results of developed method are then compared with other well-known methods and aggregation operators such as: mean, median, ordered weighed averaging (OWA) and method of expertons.

How to obtain valuable knowledge more effectively from historical cases and satisfy the requirements of supporting diagnosis or management decision making is one of the important and challenging issues in the research field of modern historical information management and intelligent decision-making science. In this study, we develop a novel case-based reasoning (CBR) method which is based on information entropy and improved gray systems theory for knowledge acquisition of historical diagnosis decision-making cases. Specially, information entropy for weight determination is introduced into the CBR, as well as a gray system theory combined to support the diagnosis decision making of breast cancer. Based on two different real-world data sets, we conduct experimental studies to compare the performance of the Delphi method and information entropy. We also investigate which combination is best among different weight determination methods and retrieval algorithms. The results suggest that: generally, information entropy is a better approach to weight derivation and better matching effect can be obtained if it is integrated into the retrieval algorithm based on gray system theory rather than Euclidean distance algorithm. Our study can provide a novel approach to obtain weight values of cases, as well as an effective tool to mine valuable decision knowledge from historical cases in public hospitals.

Hesitant fuzzy set (HFS), which allows the membership degree of an element to a set represented by several possible values, is considered as a powerful tool to express uncertain information in the process of multi-criteria decision making (MCDM) problems. In this paper, we develop a hesitant fuzzy ELECTRE I (HF-ELECTRE I) method and apply it to solve the MCDM problem under hesitant fuzzy environments. The new method is formulated using the concepts of hesitant fuzzy concordance and hesitant fuzzy discordance which are based on the given score function and deviation function, and employed to determine the preferable alternative. Numerical examples are provided to demonstrate the application of the proposed method, and the influence of different numbers of alternatives on outranking relations is analyzed based on a derived sensitive parameter interval in which a change in the parameters has no effects on the set of the nonoutranked alternatives. The randomly generated numerical cases are also investigated in the framework of the HF-ELECTRE I method. Furthermore, the outranking relations obtained in the HF-ELECTRE I method with those derived from the aggregation operator-based approach and the ELECTRE III and ELECTRE IV methods are discussed.

Many qualitative group decisions in professional fields such as law, engineering, economics, psychology, and medicine that appear to be crisp and certain are in reality shrouded in fuzziness as a result of uncertain environments and the nature of human cognition within which the group decisions are made. In this paper, we introduce an innovative approach to group decision making in uncertain situations by using fuzzy theory and a mean-variance neural approach. The key idea of this proposed approach is to defuzzify the fuzziness of the evaluation values from a group, compute the excluded-mean of individual evaluations and weight it by applying a variance influence function (VIF); this process of weighting the excluded-mean by VIF provides an improved result in the group decision making. In this paper, a case study with the proposed fuzzy-neural approach is also presented. The results of this case study indicate that this proposed approach can improve the effectiveness of qualitative decision making by providing the decision maker with a new cognitive tool to assist in the reasoning process.

Corporations would utilize advanced information technologies for generating corporate social responsibility (CSR) reports and communicating with their stakeholders. However, corporations often could not determine whether their CSR websites are capable of effective communication with the stakeholders. The purpose of this study would be to analyze websites of benchmark companies for establishing an evaluation model to be a reference for CSR website design. Information from expert interviews carried out in this study underwent DEMATEL method for analyzing the mutual relationships between the quality criteria and dimensions of CSR websites. DANP was then used to calculate the weight of each criterion. Finally, we would make use VIKOR method to prioritize the performance CSR website satisfaction. The following provides the recommended improvement priorities according to results of the expert interviews: service quality (C) followed by information quality (B) followed by technical quality (A).