This article delves into the transformative impact of blockchain technology on enhancing transaction quality and efficiency. Since the emergence of blockchain alongside Bitcoin in 2008, its decentralised and transparent nature has significantly improved transaction speed, security, and cost efficiency. These advancements have solidified blockchain as a foundational innovation in financial services. The paper examines critical milestones in blockchain, including Bitcoin, Ethereum, and Binance Coin (BNB), and their role in reshaping global finance by automating processes and reducing reliance on intermediaries. Additionally, the study evaluates blockchain’s impact on quality management, particularly emphasising how its immutable ledger system enhances the reliability and transparency of financial transactions. Despite challenges such as scalability, energy consumption, and regulatory hurdles, the potential for blockchain to redefine transaction quality in financial services is evident. This research contributes to the growing body of literature by integrating blockchain technology and traditional quality management systems, providing a comprehensive perspective on how the two domains influence one another. The findings underscore blockchain’s ability to drive innovation in financial services while addressing security, efficiency, and operational quality concerns.

The present article aims to analyse wage-labour productivity causalities in Croatia and Slovenia using cointegration methods based on monthly time series data of variables for labour productivity and real gross wages in tourism industry during the period December 1999–January 2020. The data vector is integrated by chain indices with the constant base January 2000 = 100. A stochastic trend and shocks are covered in the analysis. Shocks are linked to the European Union accession, and economic crisis following with overwhelmed tourist arrivals. The contribution of the research is two-fold. First, the equations for at most normal distributed variables of labour productivity and real wages in tourism are exposed. Three spatial cointegration relations confirm labour productivity integrity of the regional tourism market. Second, pair-wise causalities indicate one cointegrated vector for labour productivity, which drives real gross wages in tourism sub-industries. These results suggest that for a higher non-seasonal assessment of real gross wage, the labour productivity should rise, i.e. less workers, more robotization or more tourist arrivals with better quality solutions. These findings are at most important to be implemented after the COVID-19 infection crisis with expected restructurings and digital transformation in the tourism industry.

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.

This article expounds one of the first attempts to explore the relationship between tourist arrivals and gross domestic product (GDP) in Montenegro and Slovenia. Both countries are newcomers on the tourist destinations map, derived from what was previously Yugoslav republics existing as a singular emerging tourist destination. Data vector for empirical analysis covers quarterly change of GDP at constant prices and monthly data for tourist arrivals during January 2010 – December 2019 as an endogenous variable. The cointegration is used in the modelling structure. The empirical results confirm research hypothesis of uni-causal relationship of economic growth-led domestic tourism growth in Montenegro and one cointegrated vector. No cointegration vector was confirmed for Slovenia. These results are important for research, policymakers and tourism practice. Considering that, tourism and economic growth have a different distribution of causalities during the expansion period with a boost of tourist arrivals and vice versa during the economic downturn.

Purpose: This paper aims to provide a reliable statistical model for time-series prices of short-stay accommodation and overnight stays in a eurozone country. Design/methodology/approach: Exploiting the unit root feature, the cointegrated vector autoregressive model solves the problem of misspecification. Subsequently, variables are modelled for a long-run equilibrium with included deterministic variables. Findings: The empirical results confirmed that overnight stays for foreign tourists were positively associated with the prices of short-stay accommodation. Research limitations/implications: The major limitation lies in the data vector and its time horizon; its extension could provide a more specific view. Practical implications: Findings can assist practitioners and hotel executives by providing the information and rationale for adopting seasonal volatility pricing. Structural breaks in price time-series have practical implications for setting seasonal-pricing schemes. Tourists could benefit either from greater price stability or from differentiated seasonal prices, which are important in the promotion of the price attractiveness of the tourist destination. Originality/value: The originality of the paper lies in the applied unit root econometrics for tourism price time-series modelling and the prediction of short-stay accommodation prices.