This paper explores ethno-villages in Bosnia and Herzegovina as an important element of rural and cultural tourism. The attractiveness of natural and cultural heritage is very important for sustainable rural tourism development. In order to improve the process of decision making to enable the sustainable development of ethno-villages, a multi-criteria assessment model has been developed. The methodology is based on qualitative modeling using a multi-criteria analysis via the DEXi software. The model is based on hierarchical relations consisting of three main criteria that are the basis of sustainable tourism development: economic, social, and environmental criteria. The ultimate goal of the model in this study was to evaluate ethno-villages, namely six ethno-villages in Bosnia and Herzegovina. The results of the study show how ethno-villages contribute to sustainable development.

Purpose: This paper aims to analyse the main variables and causal relationships in the system structure of the diversification of non-agricultural activities on agricultural holdings using system dynamics (SD) modelling. The SD model aims to simulate depictions of the behaviour of the real system while testing the effects of alternative decisions over time.Design/methodology/approach: An SD methodology was chosen to model diversification in farm tourism.Findings: A system approach increases the authors’ understanding of the transition of agricultural holdings to farm tourism. The results indicate that the transition to farm tourism depends on the level of tourism development in a certain area. The system is influenced by subsidies allocated by authorities to expand primary agricultural activities. The model describes a situation in which the tourism and agricultural industries have been affected by the COVID-19 pandemic.Research limitations/implications: The research is limited by the small set of available data due to the limited number of farms in Slovenia. One major problem is the difference in statistical data on the same activity collected from different institutions in Slovenia.Practical implications: The paper includes implications for understanding the transition process to farm tourism, allowing policymakers to experiment with subsidies and promotion to explore the efficacy and efficiency of proposed policies.Originality/value: This study provides a structured, systemic view of the diversification of non-agricultural activities on agricultural holdings, where the simulation results are a reliable reflection of the behaviour of the actual system being modelled.

Background: Besides visiting the main tourist attractions in Slovenia, many tourists want to spend their free time in the countryside as well, but the number of farming establishments in Slovenia diminished distinctly in the last years. Objectives: This paper aims to develop a system dynamics model, with the goal to analyse dynamics of the diversification of agricultural holdings into farm tourism activities in Slovenia. Methods/Approach: A system dynamics methodology was chosen to model the diversification in farm tourism. First, we present a basic concept of a system dynamics model with a causal loop diagram. Further, a system dynamics model with different scenarios is presented. Results: The main feedback loops were identified, and the simulation model was used to analyse different simulation scenarios of the transition of farming establishments into farm tourism facilities. Conclusions: The model provides the answers to the strategic questions about the dynamics of transfer into tourist farms, using several simulation scenarios. The transition mainly relies on subsidies, promotion of diversification and the growth of rural tourism, which provides a relevant direction for the development of future incentives.

This paper examines the efficiency of tourist farms in Slovenia by adopting an approach using a framework of non-parametric programming—Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP), combining the two because the DEA analysis by itself does not take into account all attributes, especially qualitative ones. The beforementioned two methods rank the farm tourism units with respect to their efficiency. By using the DEA method, an input- and output-oriented BCC and CCR model were introduced to upgrade the criteria by including the additional non-numerical criteria of the AHP. The results of the models showed that there are possible improvements on all levels of efficiency, as well as on the criteria of the additional offer of tourist farms, which were analyzed in the AHP model with additional criteria. According to the estimated efficiency, the ranking of tourist farms differed according to the two methods. Within the group of farms assessed as efficient by DEA, the AHP model allowed a more accurate ranking.

Background: Integrated Communications Management (ICM) has emerged as a critical strategy in the evolving economy of organisational dynamics. This paper delves into its evolution, challenges, and thematic axes, addressing theoretical controversies and practical applications. Objective: This paper aims to conduct a comprehensive bibliometric analysis of ICM literature from 1989 to June 2023, scrutinising 971 publications from Elsevier Scopus. The study seeks to unveil patterns in productivity, authorship, collaboration networks, international contributions, and thematic structures. Methods/Approach: Employing systematic bibliometric methodology, this study analyses selected publications from the Scopus database. It integrates quantitative productivity metrics, citation analysis, qualitative mapping, and network exploration. Results: The findings highlight a significant surge in ICM studies since 2009, delineating thematic axes such as strategic communication, integrated marketing communications, social media, and public relations. Philip J. Kitchen is the most prolific author, while the most productive source, the Journal of Communication Management, plays a significant role in shaping the field of ICM. Remarkable collaboration networks are led by P. J. Kitchen, A. Siano, and A. Moreno. Conclusions: The analysis underscores the growing interest in ICM, emphasising ongoing interdisciplinary research and theoretical refinement. It advocates for further exploration to enhance both theoretical understanding and practical applications.

Climate change poses significant challenges for viticulture, particularly in regions known for producing high-quality wines. Wine quality results from a complex interaction between climatic factors, regional characteristics, and viticultural practices. Methods: This study integrates statistical analysis, machine learning (ML) algorithms, and systems thinking to assess the extent to which wine quality can be predicted using monthly weather data and regional classification. The dataset includes average wine scores, monthly temperatures and precipitation, and categorical region data for Slovenia between 2011 and 2021. Predictive models tested include Random Forest, Support Vector Machine, Decision Tree, and linear regression. In addition, Causal Loop Diagrams (CLDs) were constructed to explore feedback mechanisms and systemic dynamics. Results: The Random Forest model showed the highest prediction accuracy (R² = 0.779). Regional classification emerged as the most influential variable, followed by temperatures in September and April. Precipitation did not have a statistically significant effect on wine ratings. CLD models revealed time delays in the effects of adaptation measures and highlighted the role of perceptual lags in growers’ responses to climate signals. Conclusions: The combined use of ML, statistical methods, and CLDs enhances understanding of how climate variability influences wine quality. This integrated approach offers practical insights for winegrowers, policymakers, and regional planners aiming to develop climate-resilient viticultural strategies. Future research should include phenological phase modeling and dynamic simulation to further improve predictive accuracy and system-level understanding.