One of the possible solutions of rural development is rural Digital Innovation Hubs (DIHs). Rural DIHs represent an efficient way of improving local environments in a more sustainable way, by affecting local businesses, people and local authorities. The purpose of this paper is to analyze the concept of a rural DIH by exploring the following elements: business model, digital technology and competences, and the policy instrument. We investigate the above-mentioned conceptual elements by conducting a literature review study and synthesizing the findings. Additionally, we provide a case study of the Divina Wine Hub Šmarje as an example of a rural DIH, whose activities are aimed at supporting rural businesses and individuals. The results show that a rural DIH does have a positive impact on local businesses, in particular regarding their sustainability aspect. The local DIH explained in the case study provides possibilities for local businesses to use innovative technological solutions, by supporting them with the right technological equipment and skilled people. From an economic point of view on sustainability, this resulted in business processes optimization, cost reduction, employment opportunities, as well as the strengthening of sustainable consumption and marketing for the winegrowers. Furthermore, it adds to environmental sustainability by adequately assessing the conditions in the vineyards to determine the optimal time and location of effective action, resulting in reducing the environmental footprint. All of this together also contributes to social sustainability by providing fairer distribution of social opportunities and digital inclusion. In this manner, we conclude that rural DIHs should be part of the Smart transformations of rural areas and included in rural development policies.

Highlighting the important role of rural development, this paper represents a review of rural policies. Data were generated, including a broad literature research and online survey on existing and future, post-2020 rural development policies. The survey was shared among project partners from six different EU Member States including eleven regions, all from the Alpine Space. The number of total policies covered in this review paper, together with policy projects, programs or actions, is 114. Based on these, key policy findings and future recommendations are provided, attributing to the future studies on this topic as well as for policymakers at the local, regional, national and EU levels.

The gap between environmental attitudes and behaviors has been on research agendas for a while. Despite the enormous efforts of all concerned parties to increase consumer engagement in environmental issues, the levels of individual environmental concern are still higher than actual green purchasing. Considering the shortcomings in theory and practice, the purpose of this paper is to examine the link between environmental concern and environmental buying behavior by introducing three mediating variables, namely consumer environmental knowledge, perceived consumer effectiveness, and perceived personal relevance. The hypotheses were tested on a sample of 319 consumers using structural equation modeling. The results show that environmental concern predicts environmental buying behavior. Environmental concern also influences consumer knowledge, perceived consumer effectiveness, and perceived personal relevance, while environmental buying behavior is affected by knowledge and effectiveness. The model testing confirmed a partially mediated model. The findings offer several avenues for public policy makers, academics, and socially responsible companies that find the environment important.

Accurate irrigation volume prediction is crucial for sustainable agriculture. This study enhances precision irrigation by integrating diverse datasets, including historical irrigation records, soil moisture, and climatic factors, collected from a small-scale commercial estate vineyard in southwestern Idaho, the United States of America (USA), over a period of three years (2017–2019). Focusing on long-term irrigation forecasting, addressing a critical gap in sustainable water management, we use machine learning (ML) methods to predict future irrigation needs, with improved accuracy. We designed, developed, and tested a Long Short-Term Memory (LSTM) model, which achieved a Mean Squared Error (MSE) of 0.37, and evaluated its performance against a simpler baseline linear regression (LinReg) model, which yielded a higher MSE of 1.29. We validate the results of the LSTM model using a cross-validation technique, wherein a mean MSE of 0.18 was achieved. The low value of the statistical analysis (p-value = 0.0009) of a paired t-test confirmed that the improvement is significant. This research shows the potential of Artificial Intelligence (AI) to optimize irrigation planning and advance sustainable precision agriculture (PA), by providing a practical tool for long-term forecasting and that supports data-driven decisions.

The demands and preferences of men are often prioritized in societal challenges, despite women comprising half of the world’s population. Gender-Responsive Research and Innovation (GRRI) offers an approach to address this bias, promoting comprehensive and equitable solutions to better-understood real-world problems. The successful implementation of GRRI depends on recognizing obstacles, staying informed about relevant initiatives, and using this knowledge to develop effective strategies, ultimately contributing to a fairer and more responsive society. This research, using a mixed-methods approach, identified 10 critical GRRI concerns, measures, and strategies to address them, as well as 18 noteworthy initiatives across research, industrial, and regulatory contextual fields. It also enhances the understanding of GRRI, laying the groundwork for an ontology of GRRI that can benefit policymakers, organizations, and researchers seeking more inclusive strategies and practices.

Digital transformation is increasingly recognized as an important enabler of sustainable development and competitiveness in small- and medium-sized enterprises (SMEs). However, evidence on how different dimensions of digital transformation relate to the achievement of Sustainable Development Goals (SDGs) remains limited. This study explores the relationship between digital transformation and selected Sustainable Development Goals (SDGs) using an explanatory sequential mixed-methods design, in which quantitative findings informed the subsequent qualitative exploration and interpretation of managerial perspectives. The quantitative phase combined data from Eurostat’s ICT Usage Survey (2020–2024), including 60 sustainability-related indicators, with an analysis of the relationship between the Digital Economy and Society Index (DESI) and selected SDG indicators across 27 EU member states using Spearman’s rank correlation. The quantitative analysis suggests that Slovenia performs close to the EU average in overall digitalization, while significant associations were identified between digitalization and SDG 9. The qualitative phase consisted of semi-structured interviews with managers from ten Slovenian SMEs from different sectors. The findings indicate that managers perceive digital technologies, process digitalization, data-driven decision-making, and employee digital competencies as important contributors to sustainability-related outcomes, particularly in relation to SDG 8, SDG 9, SDG 12, and SDG 13. The study contributes to the literature on sustainability-oriented digital transformation in SMEs by integrating quantitative benchmarking with managerial perspectives. The findings highlight the importance of organizational capabilities, digital competencies, and strategic alignment in translating digital transformation initiatives into sustainability-related outcomes. The results provide practical implications for SMEs and policymakers seeking to support sustainable and digitally enabled business development.