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.

By bridging literature on resource partitioning and markets for technology, this article proposes that companies that pursue a broad (focused) product strategy buy more (less) technology in the market but sign fewer (more) deals as technology suppliers. Furthermore, an alignment between product and technology market strategies increases firms’ survival chances: Companies that pursue a broad (focused) product strategy are more likely to prosper when they buy (sell) technology in the market. To test these contentions, the authors consider a population of 736 firms that entered the security software industry between 1989 and 2002.

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.

This paper shows the evolution of traceability management from 1990-2017 using 124 articles. It lists 73 journals by name, classified in 12 fields of science. We find a dominance of food journals, with journals in supply chain management and IT in a stable second and third position. Articles are coded for nine possible topics and divided into two categories: theoretical and empirical. An increasing Berry index for the nine topics shows an increased dispersion across the various topics. Out of the 43 case studies, 36 relate to the food industry, although more recent publications also deal with other sectors. Various relevant theories are discussed and observed bottlenecks are discussed as well as future potential, like using systems thinking with big data analysis as an enabler.

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.