Big data and data mining methods in the tourism industry in the Czech republic

This paper was created with the subsidy of the project SGS-2021-022 Financial (stock) markets, modeling and prediction of behavior carried out with the support of the University of West Bohemia.The increasing volume of data is also becoming a new challenge for the tourism industry and research. Research on the use of big data in tourism and its analysis using advanced analytical methods such as data mining, machine learning, or artificial intelligence is gaining importance worldwide. This paper aims to analyze the current research in the field of big data in tourism and tourism data processing, especially using data mining and machine learning methods, in the Czech Republic. Another aim is to compare the level of knowledge in this area in the Czech Republic and the world. The research is based on analyzing found articles dealing with this topic. These articles were analyzed in terms of the type and source of data, the analytical methods used, and the focus of the research. The results showed a slight increase in research on big data or data mining methods in tourism in the Czech Republic in recent years, but this topic is still neglected compared to the rest of the world. Researchers mostly use user-generated data, such as online hotel reviews, for sentiment analysis, fake review detection, or for classifying positive and negative reviews. A significant gap in the current research is that only a few researchers deal directly with applications in the Czech tourism environment

The impact of artificial intelligence in the finance sector and its chances

This contribution was made within the project SGS-2021-022 Financial (stock) markets, modeling and prediction of behavior, University of West Bohemia in Pilsen.Since Artificial Intelligence has entered almost all parts of the industry, the finance sector is highly influenced by Big data and Artificial Intelligence. For the article, the following research question has been formulated: “What are the key success factors to implement AI for the finance industry?” The aim of this paper is to research the factors for success to implement AI in the finance industry, how the key success factors have developed so far and which limitations can be expected. Those companies that regularly invest in Artificial Intelligence will likely have competitive advantages compared to their contestants. One significant impact of Artificial Intelligence is the topic of cost reduction and also the optimization of processes. To maximize their profitability, banks rely on the optimization of their capital. Artificial Intelligence algorithms can be applied to handle large quantities of data to increase mathematical calculations ́ efficiency, accuracy, and speed. Banks also use AI algorithms for back-testing to assess the overall risk models. Regarding credit scoring, historically, most financial institutions based their credit ratings on the lender’s payment history. Increasingly, banks are looking towards additional data sources, including mobile phone activity and social media usage, to capture a more accurate creditworthiness assessment and improve loan profitability. Many developments might impact the future adoption of a broad range of AI and machine learning financial applications. This includes a growing number of data repositories, data quality, increasing processing power, but also new regulations and laws

Employer branding: exploring attractiveness dimensions in a multicultural context

Attracting and retaining talented employees and gaining competitive advantage are important for organizations around the world. This study identifies and operationalizes the components of employer attractiveness from the perspective of potential employees. The study tests the employer attractiveness scale (EmpAt) by identifying the attractiveness dimensions of an employer brand among business students in the Czech Republic through exploratory factor analysis. We also search for similarities and differences among employer attractiveness dimensions through a cross-cultural comparison based on the results of previous studies. Businesses in today’s globalised world need to attract potential employees globally and determine whether it would be better to use one corporate strategy or to customize their employer brand according to the cultural differences between countries. National, cultural, and gender differences are also investigated. The findings show factors that business students give the highest importance to when searching for an employer and that the factor’s importance is influenced by gender. The findings of this study can be used to track the perceptions of current job applicants about the company and to appeal to “suitable target audiences” – potential employees. The results can be used by HR experts and practitioners in formulating and executing their communication and recruitment strategies. First published online 10 April 201

Perception of the municipality in the context of a historical heritage using the example of the cheb trusess

The paper deals with the perception and image of the town Cheb in the context of historical heritage. Since 2017, the town has been running a tour of historic trusses. The aim of the research was to determine the influence of this activity on the image of the town, or whether the historic roofs already form part of the image of the town at least for some Czech residents and citizens. So far, the image of the city has not been systematically investigated. Two surveys have been carried out, one among the city residents and one among the citizens of the Czech Republic. It was found that the city of Cheb is associated with historical heritage in the minds of the respondents. However, outside the town of Cheb the historical trusses are not yet sufficiently associated with the perception of it. The article also presents a semantic differential with citizens' perception of the town of Cheb

Srovnání různých přístupů k procesu výuky pomocí analýzy dat z Moodle

The paper builds on previous work that dealt with the automatic generator of parameterized tasks and tests. In these works, the author focused mainly on the issue of automatic generation of parameterized tasks in the field of data science. A key problem in creating tasks of this type is the generation of source data and especially their storage and subsequent access in various systems and formats. This contribution is an innovation of previously used procedures and an extension of the possibilities of working with synthetic data files. The innovative data storage system uses cloud storage for its work and thus simplifies the work of the generator user when generating tasks that also contain the statistics data stored in the data file. No knowledge of cloud technologies is required to generate these tasks. In this solution we can work with the statistics tasks containing data files not only in format of selected LMS (for example LMS Moodle), but we can work with these tasks published in PDF format, where the data is represented by a link to the cloud storage. The contribution is more technical and contains the procedures and codes needed to work with cloud storage in both directions – data storage and data retrieval. These procedures also include instructions that allow you to create and use cloud storage. An integral part of the solution is also the design of the administration system of stored data and their periodic cleaning

In-house Prediction Markets and their Extension in Czech Republic

Electronic virtual markets can serve as an alternative tool for gathering information that is spread among numerous experts. Information is basic element of decision support system as part of enterprise information system. These in-house prediction markets can be important part of business management. The contribution presents the results of research on the use of predication markets in enterprises and awareness of their potential in Czech Republic. These results are compared with the results of the global survey by McKinsey. The survey within the Czech Republic was carried out twice with a period of 4 years and the results of these surveys were compared

Srovnání různých přístupů k procesu výuky pomocí analýzy dat z Moodle

The paper builds on previous work that dealt with the automatic generator of parameterized tasks and tests. In these works, the author focused mainly on the issue of automatic generation of parameterized tasks in the field of data science. A key problem in creating tasks of this type is the generation of source data and especially their storage and subsequent access in various systems and formats. This contribution is an innovation of previously used procedures and an extension of the possibilities of working with synthetic data files. The innovative data storage system uses cloud storage for its work and thus simplifies the work of the generator user when generating tasks that also contain the statistics data stored in the data file. No knowledge of cloud technologies is required to generate these tasks. In this solution we can work with the statistics tasks containing data files not only in format of selected LMS (for example LMS Moodle), but we can work with these tasks published in PDF format, where the data is represented by a link to the cloud storage. The contribution is more technical and contains the procedures and codes needed to work with cloud storage in both directions – data storage and data retrieval. These procedures also include instructions that allow you to create and use cloud storage. An integral part of the solution is also the design of the administration system of stored data and their periodic cleaning

A use of data mining methods in the Czech republic and in the world

The data mining or a knowledge discovery from data becomes more significant these days. Our world faces an enormous amount of data which are produced every day. It is important to use clever softwares to help companies sort the information and use them in a right way. Regarding the world areas it describes which methods are used and briefly describes the most important of them. Overall it contains the overview of 33 methods. Regarding the Czech Republic it is based on revisions of 42 articles which are focused on the application of data mining methods. The result from the world revision of data mining methods are decision tree, including C4.5 decision tree and classification and regression tree, genetic algorithm, k-nearest neighbor, multilayer perceptron neural network, Naïve Bayes, support vector machine, association rule, expectation maximization and k-means. For the Czech Republic mix of methods were found and they went through the whole spectrum of all areas from business, environment to healhtcare. Between main methods which are used most often are decision tree and its variations, classification and regression trees, genetic algorithm, neural network or logistic regression. The result of the comparison of all the data mining methods used in the Czech Republic and in the world is satisfactory – the same main methods are used in the Czech Republic as well as in the rest of the world. The purpose of this article is to provide the overview of commonly used methods in data mining in different areas in the Czech Republic and in the world. Another goal of this article is to provide an overview of the various areas where data mining is used with regard to the amount of resources dealing with the use of data mining methods in the Czech Republic and in the world. The conducted research made by authors shows a trend in a growth of new published articles in the Czech Republic. The research also shows that the most popular spheres where data mining methods could be used are business, environment and educational sectors

Tools for Consumer Rights Protection in the Prediction of Electronic Virtual Market and Technological Changes

Electronic virtual markets can serve as an alternative tool for collecting information that is spread among numerous experts. This is the principal market functionality from the operators’ point of view. On the other hand it is profits that are the main interest of the market participants. What they expect from the market is liquidity as high as possible and the opportunity for unrestricted trading. Both the operator and the electronic market participant can be considered consumers of this particular market with reference to the requirements for the accuracy of its outputs but also for the market liquidity. Both the above mentioned groups of consumers (the operators and the participants themselves) expect protection of their specific consumer rights, i.e. securing the two above mentioned functionalities of the market. These functionalities of the electronic market are, however, influenced by many factors, among others by participants’ activity. The article deals with the motivation tools that may improve the quality of the prediction market. In the prediction electronic virtual market there may be situations in which the commonly used tools for increasing business activities described in the published literature are not significantly effective. For such situations we suggest a new type of motivation incentive consisting in penalizing the individual market participants whose funds are not placed in the market. The functionality of the proposed motivation incentive is presented on the example of the existing data gained from the electronic virtual prediction market which is actively operated

The impact of artificial intelligence in the finance sector and its chances

This contribution was made within the project SGS-2021-022 Financial (stock) markets, modeling and prediction of behavior, University of West Bohemia in Pilsen.Since Artificial Intelligence has entered almost all parts of the industry, the finance sector is highly influenced by Big data and Artificial Intelligence. For the article, the following research question has been formulated: “What are the key success factors to implement AI for the finance industry?” The aim of this paper is to research the factors for success to implement AI in the finance industry, how the key success factors have developed so far and which limitations can be expected. Those companies that regularly invest in Artificial Intelligence will likely have competitive advantages compared to their contestants. One significant impact of Artificial Intelligence is the topic of cost reduction and also the optimization of processes. To maximize their profitability, banks rely on the optimization of their capital. Artificial Intelligence algorithms can be applied to handle large quantities of data to increase mathematical calculations ́ efficiency, accuracy, and speed. Banks also use AI algorithms for back-testing to assess the overall risk models. Regarding credit scoring, historically, most financial institutions based their credit ratings on the lender’s payment history. Increasingly, banks are looking towards additional data sources, including mobile phone activity and social media usage, to capture a more accurate creditworthiness assessment and improve loan profitability. Many developments might impact the future adoption of a broad range of AI and machine learning financial applications. This includes a growing number of data repositories, data quality, increasing processing power, but also new regulations and laws