This paper deals with the factors affecting an adoption of information technology (IT) in micro, small and medium enterprises (SMEs) within the transition economies of the Czech–Polish region. The basic determinants of IT adoption which were identified include data sources, the use of function modules in information systems, the required IT properties and the way IT operations are implemented. The results of the questionnaire survey carried out in 2012 are evaluated by the asymmetric dependence testing and estimation of ordinal regression models. The paper describes the impact of selected determinants on IT adoption. The analysis results show a different effect of determinants in relation to the size of an economic entity (individual SME segments). When we are aware of the importance of these indicators, it can help us to improve the understanding, monitoring and support of the further development of IT adoption in SMEs as an important condition for the successful economy transformation.Web of Science21344442

COBIT is a process-oriented IT governance framework. In 2019 version, the framework offers a significantly redesigned approach to prioritize processes and set process target capability levels. The design phase is very important to achieve gove...

Virtual reality environments (VRE) allow users to visualize both real-life and imaginary activities. For this reason, they make appropriate training fields at universities, too. However, the positive or negative effects of VRE are still a subject of research. There is a need to verify methods of their deployment, student responses and the impact of VRE implementation. Science and medicine courses are frequently exploiting VRE, while their exploitation in humanities is much less frequent. In our paper, we describe and evaluate their application in finance and marketing courses. Both courses were designed and developed as part of a larger, potentially university-wide project. The courses were enriched by mazes including 3-D rooms with course content elements. Students could explore them and communicate with their lecturers and classmates. To allow anytime/anywhere access, the VRE does not require using any special interface. The finance course was organized as a pedagogical experiment with test and control groups. Due to organizational and scheduling reasons, the VRE in marketing served just as enrichment. At the end of the term, all students using VRE were given a questionnaire assessing their satisfaction. The majority expressed satisfaction. In the finance course, positive opinion was also supported by students’ improved grades. In total, 87.5% of students agreed that the application of VRE contributed to gaining knowledge. Based on the positive experience and outcomes, the university plans to expand and to intensify its VRE-supported education.

The purpose of the study is to evaluate the use of quantitative methods in companies and determine the reasons for the non-use of quantitative methods. First, the context of dependence on individual characteristics of the company, such as size, amount of foreign share, and business sector, is analysed. Subsequently, the methods companies use, and those for which companies require scientific support and research are identified. The study is carried out in companies in the Czech Republic and is based on an electronic survey. Methods of statistical testing of dependencies are used for evaluation. The inputs showed that the size of the company is the most dependent on the use of quantitative methods in companies. From the results of the question on the reasons for not using quantitative methods in business practice, the most significant number of respondents answered that the methods are too academic and often their use in business practice is unrealistic due to their complexity. It is the trend of academic institutions and scientific societies to develop quantitative evaluation software with simple input and interpretation. They identified the forecasting of business variables as the method with the greatest scientific potential. The following are simulation methods and logistics management methods. The article may provide a central theme for future science and research development in business.

Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will continue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting. Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural networks. Models: In this study, hybrid models will be used, namely the Theta model and the new forecastHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre-pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators. Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.