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FUZZY ECONOMIC REVIEW

ISSN (print) 1136-0593 · ISSN (online) 2445-4192

HOTEL GUESTS´ SATISFACTION: A SEGMENTATION ANALYSIS BASED ON AGE AND GENDER USING TOPSIS FUZZY METHODOLOGY

J. C. Martín. Department of Applied Economic Analysis. Institute of Tourism and Sustainable Economic Development. University of Las Palmas de Gran Canaria, Spain

M. V. Sánchez-Rebull. Department of Business Management. University Rovira i Virgili, Spain

V. Rudchenko. Department of Management. National Research University Higher School of Economics, Russia

Guest’s satisfaction in the hotel industry cannot be easily measured because these constructs depend on multiple intangible attributes that can be evaluated very differently by distinct market segments. In this paper, the satisfaction experienced by different market segments based on age and gender is evaluated by the use of a hybrid method built from the fuzzy logic and optimal solutions. Fuzzy set theory has become a standard technique to resolve in part the uncertain information provided by guests. The results show that age and gender affect the satisfaction experienced by the hotel guests, and that not all the attributes are equally important when satisfaction is studied. The analysis of the elasticities show that the guest satisfaction is quite inelastic with respect to the 32 attributes included in the study, but the elasticity is higher for these four attributes: (1) welcome gifts in the room; (2) furniture/decoration in restaurants and bars; (3) furniture/decoration in public areas; and (4) welcome gifts in the bathroom.

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NON-ADDITIVE MULTIPLE CRITERIA APPROACH FOR THE EVALUATION OF INTERNATIONAL CLIMATE THINK TANKS

L. Farnia. Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna (Italy). Fondazione Eni Enrico Mattei and International Center for Climate Governance, Venice, Italy

S. Giove. Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna (Italy). Fondazione Eni Enrico Mattei and University Cà Foscari, Venice, Italy

M. Khoroshiltseva. University Cà Foscari, Venice, Italy

This paper outlines the application of non-additive measures and Choquet integral in the construction of a composite indicator to assess the performance of international climate think tanks and evaluate their influence in shaping climate policies and raising awareness among the general public. The composite index consists of 15 carefully selected indicators according to the feedback provided by Experts within the field and structured into three main pillars: Activities, Publications and Dissemination. In order compare Think Tanks of different size and hence to measure their efficiency, the standardized ranking is also computed dividing the Think Tank outcome in each criterion by the number of its researchers. The application of fuzzy measures and Choquet integral, allowing to take into account potential interactions existing among criteria, increases substantially the model capability both in eliciting effectively Experts’ preferences and in aggregating indicators. Moreover, we present a novel technique for the aggregation of Experts’ preferences where Decision Makers’ weights have been set proportionally to their consistency in evaluating the specific questionnaire.

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DEVELOPING A GENERALIZED FUZZY MULTI-CRITERIA DECISION MAKING FOR PERSONNEL SELECTION

Nguyen Anh Tuan. Hanoi University of Natural Resources and Environment, Hanoi

This paper proposes an extension of fuzzy multi-criteria decision making (MCDM) method for supporting the personnel selection process. In the proposed method, the ratings of various alternatives versus various subjective criteria and the weights of all criteria are assessed in linguistic terms represented by generalized fuzzy numbers. Then, the weighted fuzzy decision matrix is derived by arithmetic of generalized fuzzy numbers. To make the procedure easier and more practical, the weighted ratings are defuzzified into crisp values by using the most popular ranking approach based on centroid index. Finally, this study applies the proposed fuzzy MCDM method to solve a lecturer selection problem, demonstrating its applicability and computational process.

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UTILIZING INTERVALS OF VALUES IN MODELING DUE TO DIVERSITY OF MEASUREMENTS

Eli Shnaider. Peres Academic Center, Israel

Arthur Yosef. Tel Aviv-Yaffo Academic College, Israel

Model-building professionals are often facing a very difficult choice of selecting relevant variable/s from a set of several similar variables. All those variables are supposedly representing the same factor but are measured differently. They are based on different methodologies, baselines, conversion/comparability methods, etc., thus leading to substantial differences in numerical values for essentially the same things (from the perspective of the modeler).

In this study, we introduce a method for modeling, capable of utilizing ranges (intervals) of values and thus enabling to utilize inclusive approach, which includes all the relevant variables that represent the same factor. This approach has numerous advantages in terms of efficient data utilization, reliability of conclusions and simplification of process to summarize results (due to reduction in the number of necessary regression runs). We also introduce an interval reduction algorithm, designed to reduce excessive size of intervals, thus bringing them closer to their central tendency cluster. The algorithm also allows to identify exceptional cases where such reduction is not possible.

The modeling method used in this study is Soft Regression (based on Fuzzy Logic).

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NEW INVESTMENT DECISION-MAKING TOOL THAT COMBINES A FUZZY INFERENCE SYSTEM WITH REAL OPTION ANALYSIS

Mariia Kozlova. School of Business and Management, Lappeenranta University of Technology

Mikael Collan. School of Business and Management, Lappeenranta University of Technology

Pasi Luukka. School of Business and Management, Lappeenranta University of Technology

This paper proposes a new procedure for enriching investment and real option analysis performed with the fuzzy pay-off method by decomposing the pay-off distribution into multiple sub-distributions that correspond to different investment scenarios. This creates more information about the importance of the effect of selected factors to investment profitability. Furthermore, based on the proposed procedure, we show how a fuzzy inference system to support investment decision-making can be constructed. The proposed new procedure and the application of a fuzzy inference system are illustrated with a numerical case analysis of a power generation investment. The results show that the proposed new procedure reveals actionable information about the analyzed investment that may otherwise remain uncovered and enhances the decision-making ability of investment managers. The application of a fuzzy inference system to investment decision-support and real option analysis is a rather new approach. The obtained results highlight how the construct of a fuzzy inference system must be adapted to the perspective of the application for which it is used.

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ANOMALY INTERACTIONS AND THE CROSS-SECTION OF STOCK RETURNS

Ville Karell. Lappeenranta University of Technology, School of Business and Management

Julian S. Yeomans. Schulich School of Business, York University

This study provides new evidence on anomaly interactions, as well as on the cross-section of returns in all-but-microcap universe of U.S. stocks over the 42-year sample period from 1971 to 2013. The five anomalies being examined are size, value, profitability, investment/asset growth, and momentum. We form 5x5 conditional double-sort portfolios for each pair of anomaly variables, resulting in 20 different 5x5 sorts when using each variable in the first-stage sorting and the remaining four in the second-stage sorting. The interrelation between each pair of anomaly variables is evaluated on the basis of the monotonic relation (MR) test of Patton and Timmermann (2010) for portfolio raw returns, and in addition, by means of the Sharpe ratio comparisons. Moreover, we run Fama-MacBeth (1973) cross-sectional regressions to compare the relative explanatory power of each variable in the presence of the others. The results show that investment/asset growth and momentum dimensions capture the cross-sectional return patterns better than size, value, or profitability. The relative efficacy of momentum is higher in all-but-microcap universe than previously documented for the corresponding unlimited market-cap samples of U.S. stocks.

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VALUATION OF REAL OPTIONS IN INCOMPLETE MODELS – AN IMPLIED YIELD APPROACH

Fredrik Armerin. Department of Real Estate and Construction Management, School of Architecture and the Built Environment, KTH Royal Institute of Technology

Han-Suck Song. Department of Real Estate and Construction Management, School of Architecture and the Built Environment, KTH Royal Institute of Technology

In many applications of real options there is an assumption of complete capital markets. For the perpetual timing option this means that if the underlying asset does not pay out any cash flows, then there is no finite optimal time at which the investment should be undertaken. In contrast, when the market is incomplete there is a possibility of having a finite optimal stopping time even in the cases when the underlying asset does not pay out any cash flows. We discuss the incomplete case in models driven by both Brownian motion(s) and a Poisson process and connect it with the concept of an implied yield. The implied yield will in these models extend the concept of a monetary yield (i.e. a yield that represents the fraction of the value of an asset paid out as a cash flow). Several examples of incomplete market models where there could be a finite optimal time to invest are given.

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MULTIPLE CRITERIA MULTIPLE PEER-ASSESSMENT FOR AN ELEARNING ENVIRONMENT USING A LINGUISTIC SCORECARD FOR ON-LINE PEER-ASSESSMENT

Pasi Luukka. School of Business and Management, Lappeenranta University of Technology

Mikael Collan. School of Business and Management, Lappeenranta University of Technology

Peer-assessment between students is a way to include evaluation as a part of the learning process and to simultaneously cut teacher load in grading of assignments. eLearning systems can be used to automate peer-assessment to a large extent. Peer-assessment benefits from multiple-peer-assessment as it is likely to even-out outliers. Linguistic scorecards that use linguistic scales can be used to simplify assessment. Linguistic terms used can be mapped to fuzzy number scales and the resulting fuzzy scores can then be weighted and aggregated to derive an overall assessment. Sometimes ranking of the overall assessments is performed. This paper presents the foundations of a system that uses linguistic scorecards in peer-assessment and weights for peer-assessment is gained by self-evaluation. After this our system computes the overall score and ranks the assessments. A numerical example is used to illustrate the system

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