Fakulta informatiky a managementu http://hdl.handle.net/20.500.12603/44 2026-04-05T15:31:55Z 2026-04-05T15:31:55Z Soukal, Ivan http://hdl.handle.net/20.500.12603/2512 2025-12-05T20:36:50Z 2021-01-01T00:00:00Z

THE DEFENSE OF PRICE DISCRIMINATION IN NETWORK AND INFORMATION GOODS MARKETS Soukal, Ivan It is not uncommon that articles focused on consumer-price interaction in the network and information goods market swiftly condemn price discrimination as an obfuscation, on-purpose price complexity, or market failure. The reason is a general neoclassical rule of an efficient market where prices are set at marginal cost with no price discrimination. However, the matter is more complicated. This review provides authors an overview of why, where, and which type of price discrimination should be viewed by different optics. Goods such as software, cell carrier services, electronic newspapers subscription, electric energy supply, payment accounts, books, copyrighted content streaming, etc, cannot be treated like manufactured goods. The reasons are specific conditions - substantial and/or repeated fixed/sunk cost, economies of scale, and demand heterogeneity. Recognized economist W. J. Baumol described marginal cost set prices under these conditions as an 'economic suicide'. Reviewed articles showed that firms are forced to adopt price discrimination in order to recover their costs and to serve more consumer segments. Reviewed authors provided facts to support the use of multipart tariffs, dynamic pricing, versioning, bundling, and Ramsey pricing. These conclusions are used for suggestions on how several studies of information and network goods should be modified. Modifications are related mostly to model assumptions and pricing conclusions. I argue that, in the case of information and network goods, there is justified price discrimination. Hence, there is a certain justified level of price complexity that has to be accepted and not taken as automated evidence of inefficiency, market power, and consumer exploitation.

2021-01-01T00:00:00Z Chalupa, Štěpán Petříček, Martin Ulrych, Zdeněk http://hdl.handle.net/20.500.12603/2509 2025-12-05T20:36:28Z 2021-01-01T00:00:00Z

The Use of Business Process Management in Hotel Direct Sales Improvement Chalupa, Štěpán; Petříček, Martin; Ulrych, Zdeněk This article deals with Business Process Modelling and Reengineering use in the hospitality industry, focusing on the improvement of direct telephone sales by application of the CRM system. After modelling the current state of the selected process using ARIS methodology, the same process was remodelled for the application of the CRM system. This application shortened the whole process (mainly the number of activities needed to prosecute) and allowed the front-office employee to be more clients oriented. The automation of labour-intensive processes can reduce the number of human-caused errors and improve the convertibility and reservation request and their overall value.

2021-01-01T00:00:00Z Chalupa, Štěpán Martin, Petříček Karel, Chadt http://hdl.handle.net/20.500.12603/2508 2025-12-05T20:36:21Z 2021-01-01T00:00:00Z

Improving service quality using text mining and sentiment analysis of online reviews Chalupa, Štěpán; Martin, Petříček; Karel, Chadt The article is focused on the use of text mining and sentiment analysis of online reviews for securing a sustainable level of service quality in hotel operations. Previous studies focused mainly only on the concept extraction and customer needs and want identification. This study is using text and sentiment analysis of 3671 reviews collected from various booking and review sites during 2019 and interviews with general and HR managers. The results show that the drivers of high service quality evaluation are mainly the employees' training and development and creation of a sustainable workplace. The fluctuation of employees or a high proportion of part-time employees with a temporal commitment to the employer and a high level of stress is causing a lower level of service quality perception from costumers. Based on the results, education providers should build close cooperation with hoteliers to provide updated information and needed skills to their students, and hoteliers should focus on the creation of sustainable workplace with reduction of employee fluctuation. The training and development should be planned more than once per year, and the employees must be trained not only for standard procedures but as well in various other fields like stress management. © 2021, SRAC - Romanian Society for Quality. All rights reserved.

2021-01-01T00:00:00Z Chiu, P.C. Selamat, Ali Bin Krejcar, Ondřej Kuok, K.K. Herrera-Viedma, E. Fenza, G. http://hdl.handle.net/20.500.12603/2507 2025-12-05T20:35:59Z 2021-01-01T00:00:00Z

Imputation of rainfall data using the sine cosine function fitting neural network Chiu, P.C.; Selamat, Ali Bin; Krejcar, Ondřej; Kuok, K.K.; Herrera-Viedma, E.; Fenza, G. Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel pre-processing mechanism for non-precipitation data by using principal component analysis (PCA). Before the imputation, PCA is used to extract the most relevant features from the meteorological data. The final output of the PCA is combined with the rainfall data from the nearest neighbor gauging stations and then used as the input to the neural network for missing data imputation. Second, a sine cosine algorithm is presented to optimize neural network for infilling the missing rainfall data. The proposed sine cosine function fitting neural network (SC-FITNET) was compared with the sine cosine feedforward neural network (SC-FFNN), feedforward neural network (FFNN) and long short-term memory (LSTM) approaches. The results showed that the proposed SC-FITNET outperformed LSTM, SC-FFNN and FFNN imputation in terms of mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R), with an average accuracy of 90.9%. This study revealed that as the percentage of missingness increased, the precision of the four imputation methods reduced. In addition, this study also revealed that PCA has potential in pre-processing meteorological data into an understandable format for the missing data imputation. © 2021, Universidad Internacional de la Rioja. All rights reserved.

2021-01-01T00:00:00Z