A new fuzzy linguistic approach to qualitative Cross Impact Analysis

Villacorta, Pablo J.; Masegosa, Antonio D.; Castellanos, Dagoberto; Lamata, María Teresa

doi:10.1016/j.asoc.2014.06.025

Cited by 36 publications

(22 citation statements)

References 36 publications

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“…• encoded by means of the re-scaled to [0, 10] usual numerical encoding (i.e., A1 ≡ 0, A2 ≡ 10/3, A3 ≡ 20/3 and A4 ≡ 10), for short NELikert, and • encoded by means of the re-scaled to [0, 10] set of four terms with its usual semantics (see Figure 4), for short FLELikert. Other balanced and unbalanced fuzzy linguistic encodings have been considered, like those in Calcagnì and Lombardi [8], Villacorta et al [62], and Wang et al [63]. However, we have validated different encodings through the percentage of discrete responses matching with the closest one in ρ 2 's sense, and the one in Figure 4 has been the one yielding over 80% of matches.…”

Section: Descriptive Data Analysis Of the Real-life Example: A Comparmentioning

confidence: 71%

“…• the fuzzy linguistic scales, which are frequently considered for different goals as an a posteriori tool to encode data from a discrete (often a Likert) scale by means of fuzzy numbers (see, for instance, Zadeh [67], Tong and Bonissone [59], Pedrycz [42], Herrera et al [27,26], Lalla et al [32], and also Li [33], Akdag et al [1], Estrella et al [18], Massanet et al [39], Tejeda-Lorente et al [57,58], Villacorta et al [62], Wang et al [63], García-Galán et al [19], Liu et al [34] and Tavana [56], about some very recent developments and applications in connection with perceived quality, satisfaction, etc. ); • the fuzzy rating scale, which is considered as an a priori tool to directly assess fuzzy values and integrating the continuous nature and free assessment of the visual analogue scales with the ability to cope with imprecision of the fuzzy linguistic ones; this scale has been introduced by Hesketh et al [30] (see also, among others, Hesketh and Hesketh [29], Matsui and Takeya [40], Takemura [53][54][55], Yamashita [65], Hesketh et al [28] and De la Rosa de Sáa et al [14] for some developments and applications).…”

Section: Introductionmentioning

confidence: 99%

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Descriptive analysis of responses to items in questionnaires. Why not using a fuzzy rating scale?

Lubiano

Sáa

Montenegro

et al. 2016

Information Sciences

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In evaluating aspects like quality perception, satisfaction or attitude which are intrinsically imprecise, the fuzzy rating scale has been introduced as a psychometric tool that allows evaluators to give flexible and quite accurate, albeit non numerical, ratings. The fuzzy rating scale integrates the skills associated with the visual analogue scale, because of the total freedom in assessing ratings, with the ability of fuzzy linguistic variables to capture the natural imprecision in evaluating such aspects. Thanks to a recent methodology, the descriptive analysis of the responses to a fuzzy rating scale-based questionnaire can be now carried out. This paper aims to illustrate such an analysis through a real-life example, as well as to show that statistical conclusions can often be rather different from the conclusions one could get from either Likert scale-based responses or their fuzzy linguistic encoding. This difference encourages the use of the fuzzy rating scale when statistical conclusions are important, similarly to the use of exact real-valued data instead of grouping them.

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Section: Descriptive Data Analysis Of the Real-life Example: A Comparmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Descriptive analysis of responses to items in questionnaires. Why not using a fuzzy rating scale?

Lubiano

Sáa

Montenegro

et al. 2016

Information Sciences

View full text Add to dashboard Cite

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“…Nowadays, the need for future studies to make long-term planning and strategies for proper management has become necessary. This necessity has emerged, from the social, cultural, economic, environmental and technological opportunities and threats in most developing countries such as China, Brazil or India (Villacorta et al, 2014). The study of the future of tourism system is necessary for its planning and development.…”

Section: Introductionmentioning

confidence: 99%

Structural analysis of the tourism impacts in the form of future study in developing countries (case study: Iran)

Nematpour

Faraji

2019

JTF

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Purpose The purpose of this paper is to identify and prioritize the positive and negative impacts of tourism on the process of tourism growth at a national scale in Iran, by taking into account the reviews of previous studies, views of experts and structural analysis. Design/methodology/approach In this investigation, structural analysis technique has been used to identify the correlation between variables by using mix method data analysis. By using cross-impact analysis (N × N integer matrix) in the form of the Micmac method, the economic, sociocultural and environmental factors have been evaluated. Findings The results of the distribution of factors in the coordinate axes and the graphs between them indicate their features, and for reaching a sustainable system of tourism development, at first, priority should be given to the negative influential factors, especially the environmental fields, and then the focus should be on the decrease of the dual and risk variables as they cannot be anticipated. Originality/value For the rapid growth of tourism in many countries, governments ensure that policies have been heeded in designing and preparing general plans of the country to understand how the development trend is moving on. In this respect, arisen impacts of tourism system are one of the important issues during the development path and in the field of tourism future. Because of the complexity and broadness of tourism activities, these impacts have also many interconnected dimensions that should also be considered while studying tourism impacts.

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“…Research on CW has been very active, and many results have been achieved in the theoretical and application aspects. As a basic methodology, CW has extensively been applied to various fields such as text categorization, information processing, decision making, control, and scenario planning ever since its introduction. In this paper, we will investigate the theoretical aspects of CW.…”

Section: Related Workmentioning

confidence: 99%

A general type-2 fuzzy model for computing with words

Jiang

2017

Int. J. Intell. Syst.

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As a methodology, computing with words (CW) allows the use of words, instead of numbers or symbols, in the process of computing and reasoning and thus conforms more to humans’ inference when it is used to describe real‐world problems. In the line of developing a computational theory for CW, in this paper we develop a formal general type‐2 fuzzy model of CW by exploiting general type‐2 fuzzy sets (GT2 FSs) since GT2 FSs bear greater potential to model the linguistic uncertainty. On the one hand, we generalize the interval type‐2 fuzzy sets (IT2 FSs)‐based formal model of CW into general type‐2 fuzzy environments. Concretely, we present two kinds of general type‐2 fuzzy automata (i.e., general type‐2 fuzzy finite automata and general type‐2 fuzzy pushdown automata) as computational models of CW. On the other hand, we also give a somewhat universally general type‐2 fuzzy model of computing with (some special) words and establish a retraction principle from computing with words to computing with values for handling crisp inputs in general type‐2 fuzzy setting and a generalized extension principle from computing with words to computing with all words for handling general type‐2 fuzzy inputs.

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A new fuzzy linguistic approach to qualitative Cross Impact Analysis

Cited by 36 publications

References 36 publications

Descriptive analysis of responses to items in questionnaires. Why not using a fuzzy rating scale?

Descriptive analysis of responses to items in questionnaires. Why not using a fuzzy rating scale?

Structural analysis of the tourism impacts in the form of future study in developing countries (case study: Iran)

A general type-2 fuzzy model for computing with words

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