The paper presents a novel analytical framework in order to follow the diffusion of sectoral productivity shocks in European economies during both digital and green transitions. We then isolate six transmission channels by using improved structural decomposition of input-output networks, which include direct, internal, feedback, supplier spillovers, customer spillovers, and a new dual-transition spillover. Dynamic unbalanced panel regression includes 24 sectors in 27 EU member states between 2000-2023 and shows that there is a very large heterogeneity shock propagation in transition periods. Key results: dual transition spillovers enhance traditional network effects by 42-47 percent and digital-intensive industries by increasing upstream propagation and sustainability-oriented industries by increasing downstream transmission. The contemporary two-way transition coefficient is up to 2.34 percent, that is, much higher than traditional spillovers. Multipliers determine information services and energy as key drivers of transformation and provide spillovers that are 2.8 times greater than traditional manufacturing linkages. The temporal analysis indicates accelerating spillover effects after 2020 in line with the Recovery and Resilience Facility and the increased policies of twin-transition. Such findings offer evidence to policymakers in Europe on the dynamics of cross-sectors, highlighting the need to have integrated digital-green transition strategies in order to drive the growth in productivity.
The "Green Agenda" as a new global political and economic framework includes the gradual phase-out of coal by 2050 and a shift toward environmentally clean energy sources. This process, known as the energy transition, is driven by three key pillars: tackling climate change, protecting the environment, implementing energy efficiency measures, and integrating renewable energy sources (RES). Since Serbia has adopted a new National Energy Strategy and Energy and Climate Plan for 2040, this strategic framework has been incorporated into technical studies based on the country's existing renewable energy potential. Every country must preserve the stability of its electricity system and energy security, while also maintaining economic and financial sustainability. This study presents a methodology for predicting the share of RES in total energy consumption using a multi-criteria mathematical model for ranking the most influential factors. A prediction model for the year 2030 is proposed, and the ranking of influencing factors reveals the limitations of individual variables and their interactions. Ranking factors affecting the prediction of the RES share involves a sequence of mathematical steps that describe the behavior of input variables such as meteorological conditions, types of days, electricity consumption, energy prices, and other characteristics. The model output is a ranked set of alternatives showing the expected RES share for the forecast period, based on projected consumption, climate variability, energy prices, technology development level, and global investment trends. The multi-criteria ELECTRE method demonstrated that the selected mathematical approach incorporates all variables, corrective coefficients, and weighting factors required to improve prediction reliability and accuracy. Such models have become mandatory components of energy development strategies worldwide for the period up to 2050.
This study aims to investigate the impact of service robots on the well-being of hospitality employees, with a particular focus on key burnout dimensions: Emotional Exhaustion, Depersonalization, and Personal Accomplishment. Empirical data were collected through an online questionnaire, which was completed by 1,234 employees from the hospitality industry across four Southeastern European countries: Croatia, Serbia, Montenegro, and Bosnia and Herzegovina. The results of the Mann-Whitney U-test revealed significant findings. Employees who worked with service robots reported lower levels of Emotional Exhaustion and Depersonalization compared to those who did not. However, they also experienced lower levels of Personal Accomplishment, suggesting a mixed impact of robot integration on employee well-being. These findings highlight the complex relationship between service robot adoption and employee burnout. While robots may reduce emotional strain and detachment, they may also lead to diminished feelings of professional achievement. The results underscore the importance of adopting a balanced approach to robot integration in hospitality settings, ensuring that technological advancements contribute to both operational efficiency and employee well-being. This research provides valuable insights into the broader implications of service robot use in the hospitality industry.
This study aims to identify the impact of macroeconomic and market variables on the Bosnian and Herzegovinian stock market. Two stock market indices on the capital market of Bosnia and Herzegovina are considered: the SASX-30 and BIRS. At the same time, macroeconomic and market variables include turnover (TRN), market capitalization (MC), exports (EXP), imports (IMP), gross domestic product (GDP), consumer price index (CPI), and foreign direct investment (FDI). The study adopts panel regression analysis for the period from 2013 to 2022. The results of the panel regression analysis showed that export and import values have the strongest influence on the movement of returns for the SASX-30 and BIRS stock market indices in the capital market of Bosnia and Herzegovina. On the other hand, the variables achieved the lowest significance: turnover, market capitalization, foreign direct investments, and consumer price index.
This study examines the levels and determinants of financial literacy (FL) and digital financial literacy (DFL) among micro-scale enterprise owners in Yogyakarta. Using survey data from micro enterprises across multiple sectors, the analysis assesses literacy across knowledge, attitudes, and behavioural dimensions. The findings reveal that both FL and DFL remain relatively low, particularly in knowledge and behaviour, despite generally positive attitudes. Demographic such as gender, age, and education, significantly influence FL and DFL, with male, younger, and more educated owners exhibiting higher literacy. Socioeconomic factors also shape outcomes: permanent income employment decreases DFL, family income support enhances digital financial attitudes, while a larger number of dependents and reliance on community-based financial support reduce digital financial engagement. Business characteristics such as turnover and type, along with technology-related factors including internet access and digital payment tools, further affect literacy levels; however, access alone does not guarantee responsible digital financial behaviour. Mediation analysis confirms that FL plays a critical role in strengthening DFL, underscoring the importance of foundational financial knowledge and behaviour in digital financial adoption. The study offers insights for policies aimed at improving digital financial inclusion among micro enterprises in emerging economies.
