This paper addresses the phenomenon of overtourism in Budapest from multiple perspectives, starting with an overview that uses information collected from news, media, and academic tourism literature. Further, the phenomenon of overtourism is addressed quantitatively using different indicators, including tourism density and intensity. According to these indicators, the center of Budapest (formed by districts I, V, VI, VII, VIII, and IX) has been strongly affected by the presence of tourists, while districts physically far from the center have been less affected. This fact suggests the heterogeneity of the city in terms of overtourism. The number one catalyst of the negative impacts of foreign visitors’ behavior is party tourism (‘ruin pub’ tourism), which involves an unconventional use of the Hungarian capital. Finally, using an unconventional optimization method called fuzzy linear programming, we attempt to explore the challenging problem of identifying the optimal number of tourists for the city. The results of the study have important theoretical, methodological, and practical implications. On the theoretical side, we offer a comprehensive understanding of the phenomenon of overtourism in Budapest. Methodologically, the integrated approach in terms of data gathering and unconventional analytical methodologies (comprised of a case study analysis, the assessment of effective indicators for measuring the discussed phenomenon, and the demonstration of the sustainable number of visitors) represents a novel perspective about the extent of overtourism in Budapest. On the practical side, our findings provide valuable guidance for policymakers to help mitigate the problem of overtourism in the city. With regard to future research, we suggest extending and updating the results presented in this study to develop more sustainable tourism strategies.

Purpose: Despite the growing adoption of AI in finance, a critical gap persists between algorithmic prediction and actionable, ethical interpretation for investors. This study addresses this gap by designing and evaluating a novel hybrid AI-driven financial advisory system that seamlessly integrates predictive modeling (Gradient Boosting Machines [GBM], generative AI (GPT-4) and human oversight (Advisor-in-the-Loop) to deliver personalized, interpretable and ethically sound investment recommendations. Design/methodology/approach: Guided by a design science research methodology, we developed an artifact that clusters 1,542 investors using K-means, predicts optimal investment strategies via GBM and translates these predictions into natural language advice using GPT-4. A key innovation is the embedded AITL framework, where financial experts validate all AI-generated outputs to ensure ethical and contextual relevance. Findings: The hybrid system demonstrates high predictive accuracy (R2 = 0.85) and successfully segments investors into distinct risk profiles. More importantly, it bridges the interpretability chasm by converting complex model outputs into plain-language narratives. The AITL mechanism ensures the recommendations are not only accurate but also trustworthy and aligned with individual investor contexts, thereby promoting financial inclusion. Research limitations/implications: The system has not yet deployed in real-world settings. Future work should pilot-test the system across institutions and explore cross-market adaptability, dynamic data handling and ethical considerations such as AI fairness and explainability. Practical implications: Although the system utilizes real-world data, it has not yet implemented in practical, real-world settings. Future research should focus on piloting the system across different financial institutions and evaluating its adaptability across diverse markets, its ability to manage dynamic data and ethical considerations such as fairness and the explainability of Generative AI (GAI). Social implications: The system provides personalized and plain-language guidance that enables informed decision-making for novices and everyday investors. It contributes to the advancement of financial decision-making in the context of investment. Originality/value: This paper is among the first to integrate GBM and GPT-4 within a Design Science framework for financial decision support. Its primary contribution is the novel AITL model, which offers a blueprint for responsible, human-centered AI in management by effectively closing the loop between algorithmic prediction and managerial interpretation.

Objective: The objective of the article is to explore the impact of the new technology adoption on the performance of small and medium-sized enterprises (SMEs) at the country level. Research Design & Methods: The authors modelled the effect of technology adoption (TA) on SMEs’ market and sustainability performance by using the dynamic ordinary least squares regression technique. The analysis used a sample of 12 EU countries from 2008 to 2021. Regional specificities of the Baltic and Central European countries were introduced. We obtained a novel database from the European Union’s SME Performance Review indicators. Findings: The results show that TA positively affects both market and sustainability performance in European SMEs at the country level. This impact is larger for market performance than for sustainability performance. Moreover, the long-run equilibrium relationships between TA and market performance demonstrate a positive effect in Central European countries and a negative effect in Baltic countries. Moreover, the impact of TA on sustainability performance proves negative for the joint group of new member countries consisting of Baltic and Central European states unlike for old member countries. Implications & Recommendations: The findings suggest the adoption of a more strategic perspective among SMEs regarding TA. Furthermore, the study offers policy recommendations aimed at facilitating the green transformation of new member countries. Contribution & Value Added: The effects of TA on market and sustainability performance have not yet been examined by applying an econometrically sophisticated analytical sequence on a panel dataset of countries’ SMEs. For policymakers, the findings demonstrate that environmentally friendly technologies, through enhancing sustainability performance, can be a solid pillar that undergirds a widespread green economic transition.

As the return of overtourism can be predicted with the dying down of the COVID-19 pandemic, the purpose of this study to explore the factors of its emergence. To achieve the research objective, we draw on the attention-based view for the first time in urban tourism research. This paper argues that the attention of city decision-makers can be severely diverted from problems such as overtourism by strong organizational players. Using a comprehensive research model we show how central governmental economic policy prioritized and sustained city hall’s attention to the growth of the tourism sector to the detriment of handling overtourism in Budapest. The outcomes imply that some governmental players, using a toolkit of rhetoric and discourse, managed to influence the attentional focus of city decision makers. This article describes a means by which urban tourism scholars can model the social interactions that may affect decision-making in a city.

The study seeks to explore how blockchain technology enables the creation of new ideas for ventures and to examine the activities of founders and entrepreneurial teams in shaping those ideas. We adopted several theoretical frameworks – external enablers theory, dynamic capabilities (DCs), and dynamic managerial capabilities (DMCs) – to explain the interaction of the actor-independent and actor-dependent factors in the process of new firm formation. We analysed four Hungarian blockchain start-ups that operate across financial services, cryptocurrency trading, crypto asset management, energy, information technology, and identity industries and create high value-added and cross-industrial offerings for Hungarian and foreign markets. Using qualitative study research results, the study develops the model of external enablers, founders' and firm capabilities and new venture creation. We identify three interconnected external enablers – namely, market volatility associated with the growing popularity of cryptocurrencies and the underlying blockchain technology, the properties of blockchain, and the ideology behind the technology – and discuss the role of entrepreneurs' DMCs and sensing and seizing activities in discovering and shaping these enablers into profitable business ideas.