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## The Role of Nationality and Hotel Class on Guests’ Satisfaction. A Fuzzy-TOPSIS Approach Applied in Saint Petersburg

Although hotels usually have clients from dierent nationalities, the research analyses

the multicultural eects on hotel customers’ satisfaction is still scant. This paper aims to contribute

to the realm of hotel management by providing interesting managerial insights into how dierent

nationalities perceive hotel attributes dierently using two hotels located in Saint Petersburg as

a case study. To that end, a fuzzy hybrid method based on the technique of similarity to ideal

solution (FTOPSIS) is proposed. The results are based on a sample of 447 guests and show that:

(1) nationality influences the hotel guests’ satisfaction; (2) customers are, in general, more elastic in

three-star hotels than in four-star hotels; (3) welcome gifts in the room and in the bathroom are key

attributes in the clients’ satisfaction; and (4) Italian and Spanish guests are the least and the most

satisfied customers, respectively. The study oers a number of important managerial insights to

hotel managers and practitioners. The average figures obtained by general hotel satisfaction surveys

usually hinder important peculiarities that need to be addressed when managers develop strategic

satisfaction enhancement programmes. In particular, our results show that managers need to adapt

the programmes to the dierences observed by nationalities.

This abstract offers a method for ranking alternatives in a decison making problem. It determines importance of the criteria with help of factor analysis. Though the alternatives are evaluated by each of the criteria by a group of experts, the weights for the criteria are to be found with the help of factor analysis.

The algorithm of the method is as follows:

1. Under the constraint that the problem handles several evaluation criteria, several items to compare (alternatives) and several experts to give their evaluation.

2. Find the principal components that replace the input criteria implicitly.

3. To find the final mark for each of the alternatives the marks given by experts are multiplied with the regression coefficients, found in the step 2.

4. The final marks are represented in axes „crieria“ and „mark“ so that each alternative is described with a curve (trajectory). These curves represent the map of graded alternatives. Depending on the problem to be solved (min or max,) a record for each main criteria is to be found.

5. With help of special deviation measure procedures (Minkowski, Chebyshev e.t.s) a matrix of deviations from ideal solution is to be built.

6. The alternatives are to be rated in accordance to the deviation from the ideal trajectory.

To prove the effectiveness of the method it was applied to a problem for 5 alternatives, 3 experts and 38 evaluation criteria. The problem was also solved with the help of most popular method of Weighted Sum Model (WSM) and TOPSIS method. The problem was also being solved by finding the geometric mean for each alternative. The results for approaches were compared and the method, offered in this abstrat, proved itself as a feasible one.

The companies that are IT-industry leaders perform from several tens to several hundreds of projects simultaneously. The main problem is to decide whether the project is acceptable to the current strategic goals and resource limits of a company or not. This leads firms to an issue of a project portfolio formation; therefore, the challenge is to choose the subset of all projects which satisfy the strategic objectives of a company in the best way. In this present article we propose the multi-objective mathematical model of the project portfolio formation problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After analysis, we choose ant colony optimization method and SPEA II method, which is a modification of a genetic algorithm. We describe the implementation of these methods applied to the project portfolio formation problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modification of our SPEA II implementation were considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. The part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population more strongly than an initial population, which is generated completely by random. Comparing of ant colony optimization algorithm and three modifications of a genetic algorithm was performed. We use the following parameters: speed of execution and the C-metric between each pair of algorithms. Genetic algorithm with non-random initial population show better results than other methods. Thus, we propose using this algorithm for solving project portfolio formation problem.

This paper presents the results of volatility forecasting for indices of the Russian stock market using existing and developed by the authors fuzzy asymmetric GARCH-models. These models consider various switching functions which are taking into account the positive and negative shocks and are built using the tools of fuzzy numbers. Furthermore, in some models there are used switching functions that consider expert macroeconomic information. It was shown that fuzzy asymmetric GARCH-models provide a more accurate prediction of volatility than similar crisp models.

The companies that are IT-industry leaders perform from several tens to several hundreds of projects simultaneously. The main problem is to decide whether the project is acceptable to the current strategic goals and resource limits of a company or not. This leads firms to an issue of a project portfolio selection; therefore, the challenge is to choose the subset of all projects which satisfy the strategic objectives of a company in the best way. In this present article we propose the multi-objective mathematical model of the project portfolio selection problem, defined on the fuzzy trapezoidal numbers. We provide an overview of methods for solving this problem, which are a branch and bound approach, an adaptive parameter variation scheme based on the epsilon-constraint method, ant colony optimization method and genetic algorithm. After analysis, we choose ant colony optimization method and SPEA II method, which is a modification of a genetic algorithm. We describe the implementation of these methods applied to the project portfolio selection problem. The ant colony optimization is based on the max min ant system with one pheromone structure and one ant colony. Three modification of our SPEA II implementation were considered. The first adaptation uses the binary tournament selection, while the second requires the rank selection method. The last one is based on another variant of generating initial population. The part of the population is generated by a non-random manner on the basis of solving a one-criterion optimization problem. This fact makes the population more strongly than an initial population, which is generated completely by random.

There is no accurate answer for the question, which method of modeling of uncertainty is preferable: random or fuzzy. Today both of these approaches are highly popular. Fuzzy and probabilistic approaches are commonly used for modeling of uncertainty. Fuzzy numbers can be used for modeling vagueness of parameters, such as risk-free rate or volatility in option pricing. Under these assumptions, option value depends on believe degree and turns to fuzzy number. In this paper the Black – Scholes formula and it’s modification for American option arbitrage-free value are used. Fuzzy representations of underlying asset price, volatility of asset price and risk-free rate are used as parameters. There is set of papers regarding fuzzy approach for European option pricing. In this paper fuzzy approach is used for arbitrage-free American option pricing for the first time. The fuzzy American call value is compared with fuzzy European option value.

The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.

The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.