The coronavirus outbreak at the end of 2019 can be cited as a trigger for several drastic consequences. In those years and since, the pandemic brought many changes for which the world was not prepared. The spread and increased prevalence of home-based work is one such consequence. The study of the relationship between working from home and well-being at work is one of the most researched areas in the field of economic psychology today. The main objective of this study was to investigate well-being at work in a Slovak sample and to explore its relationship with working from home. The research methodology was implemented in the form of an online questionnaire at the end of 2023. A total of 772 people participated in the survey. According to the research hypothesis, there is a significant correlation between the form of working from home and a higher average level of well-being at work. The results obtained confirmed the hypothesis that working from home has a positive impact on well-being at work. Furthermore, the results highlight the main risks of work from home in terms of 'home comfort' and 'work-life balance'. In conclusion, the research has shown both the advantages and disadvantages of working from home, and managers should strive to ensure that the well-being of employees is maximized in order to work effectively.

This study systematically maps the thematic evolution of human–technology research from 2020 to 2025 using Structural Topic Modelling (STM) applied to over 2,000 abstracts from the Web of Science Core Collection. The analysis identifies six dominant themes: Industry, AI and Sustainability; Education and Human-Centred Design; Public Health, Community and Equity; Robotics, HRI and Ergonomics; Philosophy and Ethics of Technology; and Clinical mHealth and Usability. The results reveal a structural realignment from pandemic-driven experimentation to institutionalised, interdisciplinary research embedded in industrial, clinical, and community systems. Thematic inequality declined while diversity stabilised, indicating a mature and balanced research ecosystem. Methodologically, the study introduces a reproducible STM-based workflow integrating Gini and Shannon indices. Empirically, it provides a data-driven map of cross-disciplinary convergence. Conceptually, it demonstrates that human–technology inquiry increasingly operationalises ethics and sustainability through design, governance, and applied practice.

This study explores how social media platforms (for example, Instagram) and Reels-sponsored ads influence what makes consumers stop, watch and decide to buy. Using Consumer Engagement Theory (CET) as a lens, it looks at how people emotionally, cognitively and behaviourally respond to short-form video content. Four key factors were examined: engaging content, scenario-based experiences, user participation and perceived usefulness. Data from 393 active social media platform users in India’s National Capital Region revealed that all four elements positively shaped consumer attitudes, significantly influencing purchase intentions. Notably, relatable and emotionally engaging content had the most substantial impact. Attitude played a central role, bridging how consumers feel about a Reel and what they choose to do next. For marketers, the takeaway is clear: Reels that are visually appealing, useful and invite interaction are more likely to turn engagement into action. The study offers timely insights into how brands can connect meaningfully through short-form video.

Artificial intelligence (AI) is increasingly transforming public administration, yet empirical evidence from developing countries remains limited. This study explores the current use, key challenges, and enabling conditions of AI adoption in Kazakhstan’s public administration system. The study employs an exploratory qualitative design based on semi-structured interviews with 20 experts from government, academia, and related professional domains. The data were analyzed using thematic analysis in ATLAS.ti to identify key themes. The findings show that AI adoption is in a transitional stage, supported by strong government initiatives and shifting from digitalization to its use in decision support and predictive analytics for more proactive public services. While a number of pilot projects and practical applications are already in place, broader adoption remains constrained by interrelated barriers, including data limitations, skills gaps, infrastructural constraints, and regulatory uncertainty. The results also identify a corresponding set of enabling conditions, such as institutional support, human capital development, data governance improvements, and cross-sector collaboration, which can facilitate further progress. By linking systemic barriers with corresponding enabling conditions, the study clarifies how AI adoption unfolds in practice and identifies actionable directions for policy and implementation.