International Journal of Information Technology & Decision Making

We consider estimation of one variable’s dependence against another one in a new measure called a coefficient of structural association (CSA). It is based on the distribution of one variable along the segments of another one, and yields a gauge similar to the correlation ratio in the nonlinear regression modeling. This index can be constructed as a quotient of the observed and maximum possible variances. The CSA relations to other measures of dependence are described too, particularly, for binary variables CSA reduces to the Loevinger’s coefficient of association. Numerical simulations show that CSA presents a powerful tool for data analysis where traditional measures fail. This method can enrich both theoretical and practical estimations for identifying hidden patterns in the data and help managers and researchers in taking appropriate decisions.

Efficient inventory classification is a vital activity for electronics firms that work with a large amount of inventory items. Although one of the most widely used techniques in inventory classification is ABC analysis, this technique considers only a single criterion as the annual sales volume of each item. In practice, inhomogeneity and the differences among the inventory items necessitate considering multiple criteria to obtain a reliable classification. In this study, an integrated process of the analytic hierarchy process (AHP)-The technique for order preference by similarity to the ideal solution (TOPSIS)-ABC approach is proposed and performed to solve the multiple criteria inventory classification problem in an electronics firm. The steps of the interactive approach are programmed using MATLAB. The results of the integrated interactive approach and of the traditional ABC analysis are presented. The proposed approach gives effective and implementable results for the firm.

This paper presents an integrated model based on a compromised solution method to solve fuzzy belief multi-objective large-scale nonlinear programming (FBMOLSNLP) problem with block angular structure. A new method is proposed to transfer each belief decision-making problem into some fuzzy problems. Furthermore, we propose a new compromise method of decision-making as one of the most efficient methods based on the particular measure of closeness to the ideal solution to aggregate multi-objective decision-making (MODM) problems into a single problem. The decomposition algorithm based on Dantzig–Wolfe is utilized to reduce the large-dimensional objective space into a two-dimensional space. Then, Zimmerman method is applied to transfer each bi-objective to a single-objective. Moreover, TOPSIS and VIKOR are utilized as two independent solution methods to aggregate each multi-objective sub-problem. Finally, a new single-objective nonlinear programming problem is solved to find the final solution. To justify the proposed model, two illustrative examples are provided, and the results of three decision methods are compromised.

Traditional project scheduling methods inherently assume that the decision makers (DMs) are a unique entity whose acts are based on group rationality. However, in practice, DMs’ reliance on individual rationality and the wish to optimize their own objectives skew negotiations towards their preferred solutions. This makes conventional project scheduling solutions unrealistic. Here, a new two-step method is proposed that seeks to increase the overall efficiency of project schedules without violating individual rationality criteria, to find scheduling solutions that are acceptable to all DMs. First, a genetic algorithm is combined with evidential reasoning (ER) to obtain near optimal project schedule alternatives with respect to the priorities of each DM, separately. Second, the fallback bargaining method is used to help the DMs reach a consensus on an alternative with the highest group satisfaction. The proposed model is tested on a benchmark project scheduling problem with over 3.6 billion possible project scheduling alternatives. The results show that the model helps DMs when appointing their preferences using a well-organized procedure to provide a transparent view of each project schedule performance solution. Furthermore, the model is able to absorb the maximum support from the DMs, not necessarily a unique entity, by collecting all the self-optimizing DMs’ preferences and fairly allocating the benefits.

The success of a customer relationship management (CRM) strategy depends on the adequate use of technology, including CRM software. This paper offers empirical evidence regarding critical success factors for CRM software adoption, namely, implementation project management and services provided by information technology consultants. Data analyses involving 208 business customers of an international CRM software provider show that (1) successful management of the implementation project is a fundamental prerequisite for the firm to take full advantage of CRM software; (2) perceptions of tangible offers, reliability, responsiveness, assurance, empathy, and training services provided by CRM consultants affect CRM success; and (3) the successful management of an implementation project mediates the effects of consulting service quality on successful CRM software adoption. These findings point to the relevance of service in the CRM software industry from the perspectives of both the professionals involved and the potential adopters of CRM software.

The fuzzy systems based on the universal triple I method are investigated, and then their response functions are analyzed. First, the conclusions show that 100 fuzzy systems via the universal triple I method are approximately interpolation functions, which can be used in practical systems, and that 90 ones are approximately fitted functions, which may be usable. Second, as its special cases, the Compositional Rule of Inference (CRI) method and the triple I method are discussed, with the results that 19 fuzzy systems via the CRI method and 2 ones via the triple I method are practicable. Therefore, the universal triple I method has larger effective choosing space, which can obtain more usable fuzzy systems than the others. Lastly, it is found that the first implication and second implication, respectively, embody the function of rule base and reasoning mechanism, further demonstrating the reasonability of the universal triple I method.

Marginalization studies of a population are tools that enable the Mexican government to understand and compare the socio-demographic situation of different regions of the country. The goal is to implement effectively various programs of social or economic development whose aims are to fight against the population’s lag, which has affected the quality of life of Mexican citizens. In this paper, a multi-criteria approach for ranking the municipalities of the states of Mexico by their levels of marginalization is proposed, and the case of Jalisco, Mexico, is presented. The approach uses the ELECTRE III method to construct a medium-sized valued outranking relation and then employs a new multi-objective evolutionary algorithm (MOEA) based on the nondominated sorting genetic algorithm (NSGA) II to exploit the relation to obtain a recommendation. The results of this application can be useful for policymakers, planners, academics, investors, and business leaders. This study also contributes to an important, yet relatively new, body of application-based literature that investigates multi-criteria approaches to decision-making that use fuzzy theory and evolutionary multi-objective optimization methods. A comparison of the ranking obtained with the proposed methodology and the stratification created by the National Population Council of Mexico shows that the methodology presented is consistent and yields reliable results for this problem.

Adaptive Neuro Fuzzy Inference System (ANFIS) used to be applied to finance, engineering, material design, and decision-making management in past research, but seldom to predict educational learning performance. In recent research, gamification learning material design is often applied to reinforce learning performance, while the prediction of gamification learning performance is seldom discussed. This study therefore applies Rough set theory to extract Core Set and generating rule, ANFIS for learning achievement predication. In order to evaluate the performance of proposed model, the VCCSEGLS dataset are collected as experimental dataset and compared with other models. The results show that the proposed method outperforms the listing models in accuracy. The three key factors are extract, (G7) Time spent on game-based learning, (L1) Examination, normal drugs and treatment, and (L2) Integration ability (time scoring, stability scoring, strain capacity, completeness scoring).The proposed model also can offer accurate predictions and provide some simple decision rules, which can be accurately used by decision-makers and game designers.

In a fiercely competitive business environment, understanding target customers’ product preferences and demands has become the basis for improving competitive advantage. In the past, an enterprise would understand its consumers’ preferences and demands through interactions between salespersons and consumers or questionnaire surveys. As Internet technology and the popularity of virtual communities have grown, more consumers are commenting about products on the Internet, enabling enterprises to understand more objectively consumers’ preferences and demands. Therefore, the extraction and analysis of valuable decision-supporting information from extensive target customers’ Chinese electronic word-of-mouth (eWOM) is critical to improving an enterprise’s competitiveness. This work develops a mechanism for identifying desirable product specifications from target customers’ Chinese eWOM to provide enterprises with reference specifications in product planning, and thereby reduce the time to market and improve the target customers’ satisfaction. This goal can be achieved by performing the following tasks: (i) designing a process for identifying desirable product specifications from target customers’ Chinese eWOM, (ii) developing techniques related to desirable product specification identification from target customers’ Chinese eWOM, and (iii) implementing a mechanism for identifying desirable product specifications from target customers’ Chinese eWOM. Developing techniques associated with identifying desirable product specifications from target customers’ Chinese eWOM involves the selection and analysis of the target customers’ Chinese eWOM and the evaluation of the desirable product specifications.

Stock investors are not fully rational in trading and many behavioral biases that affect them. However, most of the literature on behavioral finance has put efforts only to explain empirical phenomena observed in financial markets; little attention has been paid to how individual investors’ trading performance is affected by behavioral biases. As against the common perception that behavioral biases are always detrimental to investment performance, we conjecture that these biases can sometimes yield better trading outcomes. Focusing on representativeness bias, conservatism and disposition effect, we construct a mathematical model in which the representative trend investor follows a Bayesian trading strategy based on an underlying Markov chain, switching beliefs between trending and mean-reversion. By this model, scenario analysis is undertaken to track investor behavior and performance under different patterns of market movements. Simulation results show the effect of biases on investor performance can sometimes be positive. Further, we investigate how manipulators could take advantage of investor biases to profit. The model’s potential for manipulation detection is demonstrated by real data of well-known manipulation cases.