This study considers diversification effects and significant influences on tourist arrivals as a vital export direction. Different quantitative methods, namely a cointegrated-autoregressive model, panels, sentiment and sensitivity analysis, were used in this study. The time-series data for Croatia and Slovenia were isolated from several secondary sources. The variables examined in this approach are tourist arrivals, precipitations, sunny days, earthquakes, microbes and CO₂ emissions. The study results showed that there is a severe negative effect on tourist arrivals defined by viruses. Moreover, there is a significant decisive effect of weather conditions on tourist arrivals. Nevertheless, it is necessary to move past Covid-19 pandemic discussions to yield more accurate tourism supply forecasts, while demand is already somehow low since the beginning of 2020. The primary significance is to develop a broader thinking about the impacts of CO₂ emissions on the tourism escorted to official tourist websites.

With the emergence of the metaverse, countries’ digital efforts to create tourism opportunities have given rise to the possibility of capitalising on digital content which, along with physical tourism experiences, can generate further income and enhance a country’s reputation. Non-fungible tokens (NFTs), a unique application of blockchain technology, offer an enabling technology in several sectors, including tourism. Therefore, this study aims to explore the official tourism websites of Croatia and Slovenia and analyse current NFT applications in tourism economics. The methodology focuses explicitly on sentiment analysis, blockchain and machine learning. The paper introduces various applications currently in place, including Slovenia’s “I Feel Nft” project. The research shows that the main benefits of using NFT and sentiment analysis in the tourism economy are the promotion and presentation of major tourist destinations, exhibitions, works of art, and companies’ products in tokens, digital content and souvenirs. The adoption of sentiment analysis and NFTs in the tourism economy is still open to proposals for implementing public quantitative data metrics. Therefore, the scientific contribution of this research is essential in terms of operational recommendations and defining metrics for measuring the effectiveness of those methodologies and their applications in the tourism economy. On top of that, the practical contribution lies in monitoring the influx of tourists, and highlighting their increase over time and the significance of new technology in time series tourism research.

Non-fungible tokens (NFTs) represent a promising application of blockchain technology that can potentially disrupt various sectors, mainly tourism. While there have been conceptual discussions regarding the opportunities and challenges of utilizing NFTs for purposes such as digital souvenirs, ticketing, loyalty programs, and conservation initiatives, there remains a significant need for a robust methodological framework to assess the impact of real-world NFT implementations empirically. This paper presents the methodological foundation of ongoing research. It proposes a comprehensive approach to researching NFT initiatives within the tourism sector, which includes data collection methods, analytical techniques, and the design of a workbench for monitoring key performance indicators (KPIs). The proposed framework combines quantitative and qualitative measures to capture the complex nature of NFT adoption, including financial performance, visitor engagement, user experience, and operational efficiency. By establishing standardized protocols and metrics, the proposed methodology aims to enable cross-study comparisons and contribute to developing the best practices for leveraging NFTs in the tourism industry. The work highlights the potential of NFTs to enhance visitor experiences, generate new revenue streams, and promote destinations as tech-savvy hubs, while also addressing ethical and sustainability concerns. The conclusion emphasizes the importance of a structured approach to evaluating NFTs initiatives, which can provide valuable insights for tourism organizations seeking to innovate and remain competitive in a digital landscape. Future research should focus on validating the framework through real-world case studies, exploring additional applications of NFTs in tourism, and addressing challenges related to data availability, technological integration, and stakeholder collaboration.

This research aims to ensure methodological conformance and to test the validity of its empirical application. To do so, the study analysed differentiation of the development patterns of four time-series variables. The relationships between greenhouse gas (GHG) emissions, employment, inflation, and gross domestic product (GDP) at constant prices were analysed, comparing the European Union (EU-27) and two European Free Trade Association countries. The study period covers twelve years of monthly and quarterly data from the beginning of 2010 to mid-2021, where the highest frequency of data was 138 observations. The methodology used included unit root testing and the vector autoregressive model (VAR). The study’s main results show that GDP at constant prices significantly affected GHG emissions in the EU-27 countries. Meanwhile, the lag between inflation and employment did not have a considerable impact. This finding shows that inflation was not a stable variable and had a strong autocorrelation. Variable employment did not follow a normal distribution. It was necessary for this research to adopt a suitable model for the technical procedure.

Brain–computer interfaces (BCIs) have gained significant attention in recent years for various applications, including education and skill development: studies have shown that BCIs can boost memory, concentration, and even creativity and can improve learning and memory retention in healthy people. In our current study, we investigated the effectiveness of real-time feedback provided by a BCI system for improving performance on a specific task. A total of 20 participants completed a pre-training assessment, followed by a training period with the BCI system and a post-training assessment. The BCI system provided real-time feedback based on the participants’ level of accuracy, with positive feedback given for scores above 70%. Results showed a significant improvement in accuracy scores from pre- to post-training, with an average improvement of 15%. Participants also reported high levels of satisfaction with the feedback provided by the BCI system. These findings suggest that real-time feedback provided by a BCI system can be an effective tool for skill development and education, particularly when tailored to the specific needs of individual learners. Further research is needed to explore the potential of BCIs for a wide range of educational applications.

The present study aims to analyse and model the net occupancy rates of bed-places in the Croatian hotel industry in the period from January 2005 to August 2014. For these purposes three models of generating time-series forecasts were used: the seasonal Naive model, the Holt-Winters exponential model and the seasonal autoregressive integrated moving average model. The empirical results show that the used time-series models performed well in terms of MAPE and that the Holt-Winters model outperformed the seasonal naive and the seasonal ARIMA model.