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    (Technická Univerzita v Liberci, ) Rapacz, Andrzej; Gryszel,Piotr; Walesiak, Marek; Dudek, Andrzej; Ekonomická fakulta
    The skills of acquiring and processing information as well as creating innovations remain the key factor responsible for the market success of enterprises, one of the most important factors in gaining a competitive advantage on the market. It is also true for the tourism market of which catering services make an essential part. Hotel industry has been the subject of intensive research in this area for over 2 decades. Much less scientific attention has been paid to innovation in the restaurant sector. Therefore, the intention of the authors of this study was to analyse the process of creating innovations in restaurants operating in the largest Polish cities. The study identifies the factors responsible for the innovative activity of restaurants, perceived from the perspective of their managers. For this purpose, a multivariate method, in the form of classification trees, was used. The research material was collected in the course of a survey carried out in 250 restaurants. As a result of the applied research methods, the innovation factors were specified and 4 segments of innovative restaurants in Poland were identified. These segments are made up of 75% of restaurants applying pro-innovative activities, which for them represent an important aspect of market success. Pro-innovative activities are implemented more often by chain restaurants as well as the ones operating in hotel facilities. Small, family-run restaurants use innovations on a smaller scale. They refer to selected restaurants operating in the 6 largest Polish cities, which limits the possibility of making generalizations regarding other forms of catering establishments functioning in other geographical locations. Future research should cover a wider group of catering establishments, in various locations.
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    (Technická Univerzita v Liberci, ) Choi, Jaewon; Lee, Hong Joo; Choeh, Joon Yeon; Ekonomická fakulta
    The automotive industry evaluates various success factors to achieve competitive advantage in selling products. Existing studies have predicted the success of newly launched automobiles based on an economic perspective. However, factors such as dynamic changes in consumer preferences and the emergence of numerous automobile brands pose difficulty in understanding product quality. This study proposes a method of understanding the automotive market using text mining techniques and online user opinions for newly launched cars. By analyzing customer experiences and expectations through their opinions, we can anticipate automobile demand in the market more easily. The proposed method is based on online reviews from an online portal for automobiles. Based on a literature review, this study presents a framework for analyzing input versus output word-of-mouth (WOM). It also integrates the success factors from existing automobile studies and derives functional categories and relevant keywords. The analysis identifies differences in consumer-interest factors that lead to short-term success or normal results in automobile sales. In addition, it confirms that the elements of WOM produces varying results depending on the timing these are employed in relation to the product launch (i.e., before or after a product’s launch). It revealed which dimensions of automobile characteristics are important factors in identifying sales volume and market share for specific types and brands of automobile models. The results of this study provide theoretical advantage in predicting market success in the automobile industry. In addition, the study derives practical insights into characteristics of classification information for market forecasts in the automotive industry. The paper provides empirical insights about how input WOM and output WOM which are analyzed differently can have predictive power in forecasting market share and sales volume for automobiles.
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    (Technická Univerzita v Liberci, ) Pompurová, Kristína; Marčeková, Radka; Ekonomická fakulta
    A model based on digital sharing has brought a new wind to the business world. Its growth was abruptly disrupted by the COVID-19 pandemic. As anti-epidemiological measures have most significantly affected the tourism sector, this paper attempts to outline the development perspective of platform tourism services. The aim of the paper is to examine the plans of the Slovak population related to the use of platform tourism services after the end of the COVID-19 pandemic. Based on the results of focus groups and questionnaire survey, the paper predicts, that the current crisis will not weaken the development of the tourism platform economy, while demand will be price-driven. As the economy of platform tourism services will be an integral part of our lives for many years to come, it is possible to assume a relentless interest not only of practice, but also of scholars. The research confirms that the accommodation and the transport are the most important paid platform tourism services. They are popular mainly because of the price, not because of the environmental friendliness which denote rather a positive externality of their use. Platform tourism services should be seen as a whole, not as fragments through the prism of selected platforms. The paper highlights information as a key segment and draws attention to the shortcomings of measuring platform services, especially transport ones. Platform tourism services will continue to transform the business. Therefore, it is necessary to better understand it and look for opportunities for its sustainable development. The uniqueness of the current study lies, among others, in the use of mixed methods which help to comprehensively understand the problem in depth and breadth.
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    (Technická Univerzita v Liberci, ) Cheraghalizadeh, Romina; Dědková, Jaroslava; Ekonomická fakulta
    Customer retention is a critical factor in companies’ survival, and both service quality and social media are important means of retaining customers. However, despite this importance of these antecedents, only a few studies considered the effects of these factors in the hotel industry literature. With the application of social exchange theory, the current research aims to evaluate the effect of service quality and social media marketing on customer retention in hotels, and evaluates how customer satisfaction, brand awareness and brand image mediate this association. After pre-testing the questionnaire, data of this research was collected from 4- and 5-star hotel customers in various cities of the Czech Republic. Before testing the hypotheses, data have been tested in term of checking reliability and validity. Moreover, the probability of common-method bias was also tested in order to ensure there is no variation caused by study method or instrument. Testing the hypotheses was done using correlation and regression analysis afterwards. Findings of this research indicated that service quality and social media marketing improve customer satisfaction, brand awareness, and brand image. Moreover, customer satisfaction, brand awareness, and brand image are also antecedents of customer retention. Findings also confirmed that these three factors (customer satisfaction, brand awareness, and brand image) play the mediation roles between Service quality and revisit intention, and also between social media marketing and revisit intention. Results of this research showed the importance of both service quality and social media marketing in order to achieve better customer outcomes and encourage clients to revisit the hotel. The findings can provide important managerial and practical implications to the hotel industry.
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    (Technická Univerzita v Liberci, ) Yin, Tao; Wang, Yiming; Ekonomická fakulta
    This paper mainly studies the market nonlinearity and the prediction model based on the intrinsic generation mechanism (chaos) of Bitcoin’s daily return’s volatility from June 27, 2013 to November 7, 2019 with an econophysics perspective, so as to avoid the forecasting model misspecification. Firstly, this paper studies the multifractal and chaotic nonlinear characteristics of Bitcoin volatility by using multifractal detrended fluctuation analysis (MFDFA) and largest Lyapunov exponent (LLE) methods. Then, from the perspective of nonlinearity, the measured values of multifractal and chaos show that the volatility of Bitcoin has short-term predictability. The study of chaos and multifractal dynamics in nonlinear systems is very important in terms of their predictability. The chaos signals may have short-term predictability, while multifractals and self-similarity can increase the likelihood of accurately predicting future sequences of these signals. Finally, we constructed a number of chaotic artificial neural network models to forecast the Bitcoin return’s volatility avoiding the model misspecification. The results show that chaotic artificial neural network models have good prediction effect by comparing these models with the existing Artificial Neural Network (ANN) models. This is because the chaotic artificial neural network models can extract hidden patterns and accurately model time series from potential signals, while the benchmark ANN models are based on Gaussian kernel local approximation of non-stationary signals, so they cannot approach the global model with chaotic characteristics. At the same time, the multifractal parameters are further mined to obtain more market information to guide financial practice. These above findings matter for investors (especially for investors in quantitative trading) as well as effective supervision of financial institutions by government.