The global employment landscape will continue to change due to new technologies, in particular automation, online collaboration tools, and artificial intelligence. The shortage of skilled workers and the growing jobs of e-tourism employees are a challenge for maintaining day-to-day operations. It is crucial to develop a digital recruitment strategy and communicate a good employer brand, supported by targeted digital advertising. This study aims to identify the impact of social networks on the effectiveness of digital human resources recruitment strategies in tourism. On the basis of a sample of 620 respondents who had experience of the digital recruitment of human resources in the tourism sector, the collected data was analyzed using structural equation modeling. The results underline the high relevance of building these strategies, as professional communication in social networks is the key to successful work in sustainable human resources practices.

Background: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a strategic vision, a flexible organizational culture, and the ability to support decisions through artificial intelligence (AI)-based systems. Methods: This study proposes an extended conceptual model using structural equation modelling (SEM) to explore the relationships between five constructs: technological change, strategic and organizational readiness, transformation environment, AI-enabled decision configuration, and operational redesign. The model was validated based on a sample of 217 active logistics specialists, coming from sectors such as road transport, retail, 3PL logistics services, and manufacturing. The participants are involved in the digitization of processes, especially in activities related to operational decisions and sustainability. Results: The findings reveal that the analysis confirms statistically significant relationships between organizational readiness, transformation environment, AI-based decision processes, and operational redesign. Conclusions: The study highlights the importance of an integrated approach in which technology, organizational culture, and advanced decision support collectively contribute to the transition to digital and circular logistics chains.

Growing concerns about sustainability highlight the need to understand not only rational but also neurocognitive mechanisms that shape consumer decisions. This study examines how discrete emotions—such as empathy, moral satisfaction, and responsibility—interact with reflective cognitive control to influence green purchase intention, with neural loyalty functioning as a mediating mechanism. Grounded in dual-process theory, the proposed model is empirically tested through PLS-SEM using data from 276 consumers in Romania, Poland, and the Czech Republic, actively engaged with ecological products. The results demonstrate that both emotional and cognitive dimensions significantly predict purchase intention, while neural loyalty partially mediates these relationships, transforming temporary reactions into stable behavioral patterns. These findings suggest that bridging the intention–behaviour gap in sustainable consumption requires the integration of affective engagement, rational evaluation, and loyalty consolidation. The study contributes to sustainable marketing literature by positioning neurocognitive drivers as key antecedents of pro-environmental behaviour and by offering practical insights for designing interventions that effectively convert green intentions into consistent actions. All structural relationships were statistically significant (p < 0.05), confirming the robustness of the proposed model.

In the context of tightening sustainability regulations and rising demands for transparent and responsible capital allocation, understanding how digital financial innovations influence market efficiency has become increasingly important. This study examines the impact of Financial Technology (FinTech) solutions and crowdfunding platforms on sustainable market efficiency, volatility dynamics, and risk structures in the United Kingdom. Using weekly data for the Financial Times Stock Exchange 100 (FTSE 100) index from January 2010 to June 2025, the analysis applies the Lo–MacKinlay variance ratio test to assess compliance with the Random Walk Hypothesis as a proxy for informational efficiency. Firm-level proxies for FinTech and crowdfunding activity are constructed using the Nomenclature of Economic Activities (NACE) and Standard Industrial Classification (SIC) systems. The empirical results indicate substantial deviations from random-walk behavior in crowdfunding-related market segments, where persistent positive autocorrelation and elevated volatility reflect liquidity constraints and informational frictions. By contrast, FinTech-dominated segments display milder inefficiencies and faster information absorption, pointing to more stable price-adjustment mechanisms. After controlling for structural distortions through heteroskedasticity-consistent corrections and volatility adjustments, variance ratios converge toward unity, suggesting a restoration of informational efficiency. The results provide relevant insights for investors, regulators, and policymakers seeking to align financial innovation with the objectives of sustainable financial systems.