This work presents a data-fusion mathematical object that incorporates the optimism level of a decision-making agent. The new fusion object is constructed by extending the ordered weighted averaging (OWA) operator in the process of creating an experton. The main advantage of this approach is that it can represent the attitudinal character of the decision maker in the construction of the experton. Therefore, this approach represents a new method for addressing multiperson problems by using optimistic and pessimistic perspectives. The work presents different practical examples based on the absolute hierarchical relationships of the "minimum of the bottom end of the intervals," "minimum of the top end of the intervals," and "minimum size of the interval." The work also considers a wide range of particular cases of the OWA-experton, including the minimum experton, the maximum experton, the average experton, and the olympic experton. In addition, the study presents software for the calculation of OWA-expertons. Finally, the paper ends with an application in business decision-making regarding the calculation of expected benefits.
This article presents an empirical comparative assessment of the measurement quality of two instruments commonly used to measure fuzzy characteristics in computer-assisted questionnaires: a graphic scale (a line production scale using a slider bar) and an endecanary scale (a 0–10 rating scale using radio buttons). Data are analyzed by means of multitrait–multimethod models estimated as structural equation models with a mean and covariance structure. For the first time in such research, the results include bias, valid variance, method variance, and random error variance. The data are taken from a program that assesses entrepreneurial competences in undergraduate Economics and Business students by means of questionnaires administered on desktop computers. Neither of the measurement instruments was found to be biased with respect to the other, meaning that their scores are comparable. While both instruments achieve valid and reliable measurements, the reliability and validity are higher for the endecanary scale. This study contributes to the still scarce literature on fuzzy measurement instruments and on the comparability and relative merits of graphic and discrete rating scales on computer-assisted questionnaires.
Purpose -The aim of this study is to show in detail the theoretical and practical foundations of a new feasibility technique for cash flow forecasting (CFF) based on triangular fuzzy numbers. Design/methodology/approach -One of the most complicated problems business people face is determining if they have enough cash to be able to meet all future payments of a specific period. The uncertainty of forecasting the data to solve the problem suggests that a model based on fuzzy logic tools may provide a good way to obtain new techniques to ensure the feasibility of cash flow management.Findings -This study shows how a specific company can obtain a quantitative idea of the risk of not being able to meet payments in a specific period. This idea can be put into practice with the usual computer tools. Research limitations/implications -This work presents a technique to predict the feasibility of CFF using triangular fuzzy numbers. There are other fuzzy numbers with which we can model the study problem and that offer certain advantages over to triangular ones. Practical implications -A qualitative procedure is currently used to calculate the feasibility of a CFF. This work represents a step forward since it shows how to model quantitative feasibility. Originality/value -The originality and value of this contribution consists of providing a complete model for a feasibility technique of CFF, as well as several proposals to mechanize the calculations and make the results more intuitive by means of spreadsheet graphs.
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