This research aims at identifying the role of social media in evaluating the attractiveness of a tourism destination, with special emphasis on the health safety of the destination. Consistent with this objective, a survey has been carried out on a sample of 675 Romanian social media users. The research results led to the development of a model based on structural equation modeling. The model includes nine latent variables that were structured taking into account different behavioral aspects related to the role social media has in travel planning, as well as for evaluating the health safety of a tourism destination. The main findings suggest that the trust in social media for tourism information made people become more interested in communicating through this means and to consider it more useful throughout the travel planning process. When choosing a travel destination, the more involved a tourist is in the decision making process, the greater the attention they pay to social media. The perceived usefulness of social media in travel planning has a significant influence on intentions to choose a tourism destination. As the importance assigned to the health safety of tourism destination increases, social media plays a more active role in travel by creating trust in this means in order to obtain sanitary safety information. People that intend to use social media for finding information on the health safety of a tourism destination are more likely to choose that destination for their vacation. The managerial implications of this paper regard the communication strategies adopted by tourism services suppliers or by some public authorities aimed at stimulating an efficient usage of social media so as to increase the buying intentions for tourism destinations.

The rapid integration of emerging technologies into organizational processes has fundamentally redefined the role of strategic human resource management (SHRM). This paper explores how digital innovations—such as artificial intelligence (AI), robotic process automation (RPA), blockchain, and immersive technologies—are reshaping the workforce and transforming the way organizations attract, develop, and retain talent. In the context of the digital era, human capital is no longer a passive input but a strategic enabler of sustainable competitive advantage. The purpose of the study is to analyze how SHRM practices must evolve to align with technology-driven organizational models, combining insights from a systematic literature review, institutional reports, and illustrative corporate cases. Findings indicate that agility, continuous reskilling, ethical AI governance, and employee well-being are critical levers for sustainable advantage. Comparative tables highlight differences between traditional HRM and SHRM in the digital era, while case studies (IBM, Walmart, Unilever, and UiPath) demonstrate the strategic value of predictive analytics, diversity and inclusion programs, virtual training, and people analytics. By proposing a conceptual model that links emerging technologies, SHRM, and competitiveness, the paper contributes to current debates on the transformation of work and organizational resilience. The study offers practical implications for HR leaders, policymakers, and academics navigating digital transformation while reinforcing human-centric performance and sustainability.

The European waste policy is focused on reducing the negative impact of waste, eliminating the unhealthy use of resources, and on better waste management. The aim of this paper is to analyse Romania’s waste management practices in the EU context. The research methodology is based on a quantitative and qualitative analysis. The descriptive data analysis reveals significant differences among EU countries in terms of the municipal waste generated and the recycling rate. The municipal waste generated registers decrease, whereas there are significant increases in terms of the recycling rates in the EU countries. The qualitative research is based on the analysis of structured interviews among stakeholders, relevant for the waste management in Romania. The information obtained based on interviews was processed using NVivo and the main findings are focused on recommendations for improving the waste management system. We have identified a lack of adequate public support in the waste field due to insufficient awareness programs, lack of operator’s obligation for separate waste sanitation, people's discouragement to separate their household waste, and lack of bins to separate collection. The correction of identified weaknesses could lead to an improved waste management system, feasible only through an active involvement of stakeholders

The research aim was to identify the training needs of entrepreneurs and employees within small and medium enterprises (SMEs) from the Suceava, Chernivtsi, and Bălți regions, to analyze the specific training practices in the cross-border area, and to identify the common features or the disparities. The research contains an exploratory survey, based on a semi-structured questionnaire that investigates the training needs in the SMEs and specific training practices with a comparative approach. The results relate to the challenges faced by transformation economies and by the specificities of SMEs from these remote areas. Additionally, the research connects the factors involved in planning and delivering training programs for employees in SMEs in this EU peripheral area with the weaknesses of the companies in facing the market competitive economy. The results of the survey disclosed some common features and specificities related to training needs, training responsibilities, and interests in the SMEs from this cross-border area. The discussions are relevant for different categories of stakeholders, at the micro-level, for the management of the companies, but also on a larger scale, in planning the new development programs for the labor market in the targeted areas.

The article explores the characteristics and approaches of climate-neutral business development in the European Union based on digital transformation. The focus is on small and medium-sized enterprises (SMEs), which currently generate a significant share of global economic emissions and need to accelerate the integration of effective decarbonization models. The data indicates that the proportion of emissions generated by SMEs in the business sector and their energy consumption represent the most crucial indicators for assessing the climate neutrality of a country’s development. These indicators exhibit a considerable degree of variation across EU countries. Concurrently, the research indicates a notable disparity between SMEs and large corporations in terms of their preparedness to implement various climate-oriented initiatives and their capacity to integrate digital solutions. The article presents a systematic analysis of the potential for digitalization to reduce the SMEs’ carbon footprint across various emission areas. These include direct emissions from industrial processes, indirect emissions from energy consumption, and key links in the value chain. Five clusters of digital solutions were identified as the most effective and particularly supported in the European model of climate-neutral development. Systemic directions and recommendations for their strengthening are identified.

Cryptocurrency has emerged as a well-known significant component with both economic and financial potential in recent years. Unfortunately, Bitcoin acquisition is not simple, due to uneven business and significant rate fluctuations. Traditional approaches to price forecasting have proven incapable of proving adequate data and solutions because prices can now be forecast in real time. We recommended a machine learning-based alternative for a mortgage lender based on highlighted problems in forecasting the price of Bitcoin. The proposed system included a reinforcement learning algorithm for price estimation and forecasting, as well as a blockchain framework for an efficient and secure environment. The proposed prediction, compared to other state-of-the-art strategies in this sector, demonstrated better performance. In this system, the proposed prediction reached improved consistency, in comparison to other systems, with respect to Monero (XMR), Litecoin (LTC), Oryen (ORY), and Bitcoin (BTC).