This study explores how agency in later life is enacted within everyday sociomaterial arrangements. Drawing on Actor-Network Theory (ANT) and the International Classification of Functioning, Disability and Health (ICF), the analysis is based on open-ended survey responses from older adults and healthcare professionals. The findings suggest that people, objects, spaces, and temporal rhythms collectively shape the conditions under which agency becomes enabled or constrained in daily life. Rather than framing challenges as individual deficits, the study highlights how agency emerges through the alignment, or misalignment, of diverse human and non-human elements. Agency is found to be fragile, dynamic, and context-dependent, shaped by bodily rhythms, spatial accessibility, technological mediation, and temporal coordination. Suggestions from professionals are interpreted as proposed interventions into these everyday sociomaterial arrangements. By integrating ANT and ICF, the study offers a relational understanding of functioning that challenges individualistic models and contributes to critical ageing research by showing how agency in later life can be examined as a situated and sociomaterial achievement.
GovTech has become a key driver of societal transformation, yet its impact on human well-being and social progress remains complex and potentially non-linear. This study aims to examine how GovTech development, as a "Technological - Institutional - Human" system, shapes social progress by identifying its mechanisms, non-linear dynamics, and structural conditions. The analysis is based on a panel dataset of 1,964 observations for 2003-2024, employing two-way fixed-effects models with Driscoll-Kraay standard errors and non-linear specifications. The results show that the aggregate EGDI has a positive and significant effect on social progress, even after controlling for time effects (beta = 3.37, p = 0.036). A non-linear relationship is identified, with a turning point at approximately 0.61, indicating diminishing returns at higher levels of digital development. Component-level analysis reveals that telecommunications infrastructure has the strongest impact (beta = 14.09, p < 0.001), followed by online services (beta = 4.16, p = 0.002), while human capital exhibits a threshold effect (turning point approximate to 0.26). Additionally, social progress increased steadily until 2020 (beta approximate to 6.10) but declined in 2022 (beta = 3.04), reflecting global shocks.
Technological transformation and digital mimicry characterise the present day. Trends in digitisation, computerisation and robotisation have become firmly established in societies. This article is part of this trend, emphasising the role of digital transformation in primary and secondary education. The aim of the article is to evaluate the digital transformation of the educational process (K-12) using Chromebook laptops and the Google Workspace for Education platform to develop the practical digital skills and competences of students and their teachers. Achieving this goal required research of secondary sources using monographic methods with content analysis techniques and primary sources using mixed qualitative and quantitative methods. The analyses conducted have identified the benefits of introducing Chromebooks and Google Workspace for Education cloud technology, which are changing the learning and teaching process in Polish primary and secondary schools. The results obtained indicate a significant improvement in the effectiveness of both teachers and students. Particularly noteworthy is the shift towards increasing the teacher's focus on individual students on a 1:1 basis, with the possibility of personalising learning anywhere and anytime, and strengthening student collaboration in the process of learning from each other. These results are in line with the broader implications of pedagogy supported by new technologies in the context of international initiatives in the field of digital transformation of the education process.
Artificial intelligence (AI) is rapidly reshaping innovation management, moving from a peripheral technology to a potentially transformative force within organisational innovation systems. This paper examines AI’s role within an Innovation Management System (IMS) using the ISO 56002 framework. Adopting a conceptual and integrative literature review approach, the study brings together insights from innovation management research, emerging evidence on AI applications, and prior studies of technological diffusion and socio-technical change. The analysis identifies two broad and complementary AI capabilities, analytical AI and generative AI, whose application across the innovation process highlights substantial opportunities in search, selection, implementation, and value capture. Findings indicate that current AI adoption is largely limited to substitution, improving existing tasks, while its transformative potential lies in augmentation and human-AI collaboration. The paper argues for deeper socio-technical integration of AI as a complementary partner within innovation management systems.
This study investigates behavioural patterns in identifying disinformation within news environments, where algorithmically curated information flows shape human-technology interaction. Using data from the 2025 Eurobarometer survey (n=26,114), the research employs statistical analysis to compare users' attitudes towards news in traditional and social media. Findings indicate a significant disparity: while 65.9 % of respondents express high confidence in recognising fake news, 34.1% remain unconfident. Notably, high engagement with social media correlates with a greater exposure to disinformation. Women tend to have slightly higher self-reported exposure to disinformation (23.8%) than men (21.1%). Results demonstrate that demographic factors, particularly age and years of education, significantly shape information-checking behaviours. By adopting a human-oriented perspective, the study highlights how digitally mediated environments structure users’ interaction with information and condition their capacity to critically assess its reliability.
The rapid diffusion of generative artificial intelligence (GenAI) is reshaping higher education by challenging traditional roles of teaching, trust, and academic integrity. This study aims to explore how university staff cognitively and ethically evaluate GenAI by analysing perceptions of its pedagogical replacement potential, practical feasibility, academic integrity risks, and perceived reliability across national contexts. The analysis is based on an anonymous cross-sectional survey of 637 respondents conducted between May and September 2025, using descriptive statistics, correlations, regression models, and exploratory factor analysis. The findings show that perceived replacement potential is low (M = 2.47), with over 51% of respondents rejecting the idea ofAI replacing teachers. Academic integrity concerns are the strongest dimension (M = 3.51), while trust in AI accuracy remains low (M = 1.99), indicating widespread scepticism. Perceived cost and complexity do not significantly influence beliefs about replacement (R2 = 0.007; p = 0.117), suggesting a weak relationship between feasibility and perceived impact. Finally, moderate positive correlation (rho = 0.34) and low reliability (alpha = 0.50; a = 0.45) provide evidence that perceptions of GenAI are fragmented and multidimensional rather than internally consistent.
This study empirically examines the impact of digitalization dynamics and remote work technologies on the social integration processes of highly skilled migrants displaced to Eastern Europe after the Russia-Ukraine war. Panel dataset for the period methods. The theoretical framework is based on the synthesis of the Technology findings reveal that the Digital Economy and Society Index (DESI) has a strong, positive impact on the professional integration of highly skilled migrants. The unexpected negative mediator effect of digital competence indicates that excessive reliance on remote work carries the risk of "digital isolation". The regulatory role of digital infrastructure was found to be statistically insignificant. The findings introduce the 'Digitally Mediated Acculturation Model' to the literature and offer concrete suggestions for hybrid integration policies. Given data constraints, this study employs the employment rate of tertiaryeducated non-EU nationals as a proxy for professional integration of highly skilled migrants. The findings introduce the "Digitally Mediated Acculturation Model" to the literature and offer concrete suggestions for hybrid integration policies.
This study examines the use of artificial intelligence (AI), delegation preferences, and perceptions of the future of work among Generation Z representatives, shedding light on emerging patterns of human-AI interaction and delegation. The analysis is based on a cross-sectional survey. The results indicate widespread and frequent engagement with AI, particularly for learning-related tasks and chatbot interaction, as well as high levels of mobile-based use. Despite this intensive adoption, the willingness to delegate tasks to AI systems remains limited. Most respondents reject delegation across contexts such as financial decisions, monitoring, and communication, and, where accepted, delegation is typically conditional on human oversight rather than full autonomy. Perceptions of the future of work emphasise transformation rather than displacement. Respondents most commonly expect hybrid models in which human work is supported by AI tools, alongside a strong expectation of the need for retraining. Overall, the findings highlight a tension between high AI usage and constrained trust in autonomous decision-making, suggesting that Generation Z engages with AI as a supportive resource while maintaining strong preferences for human control. This contributes to ongoing discussions on human-technology relationship in which Gen Z engages AI primarily as a supportive tool while safeguarding human control, thereby contributing to understandings of trust, agency, and socio-technical change in future work practices.
In this Editorial, we discuss young people’s digital well-being and the recent debates on algorithmic systems and temptations to regulate children’s and young people’s online activities and thus their digital agency.
Digital systems are increasingly embedded in everyday life, shaping access to services, institutional interactions, and ultimately human well-being. This study aims to examine how digital government development influences quality of life and to identify the human-centred mechanisms (online services, digital infrastructure, and human capital) through which these effects occur. The analysis is based on a cross-country panel dataset of 76 countries over 2012-2024 (446 observations), using two-way fixed effects models with Driscoll-Kraay standard errors, mechanism decomposition, and a Mundlak approach. The results show that e-government development has a strong positive effect on quality of life in the contemporaneous specification (beta = 106.45; p < 0.001), while the lagged effect is weaker (beta = 25.96; p = 0.075), indicating predominantly immediate impacts. A 0.1 increase in EGDI is associated with an increase of approximately 10.6 points in the Quality of Life Index. Mechanism analysis reveals that online services (beta approximate to 31-38; p < 0.001) and digital infrastructure (beta approximate to 60-67; p < 0.01) are the primary drivers, whereas human capital is not statistically significant. The Mundlak decomposition confirms that both within-country improvements (beta = 122.68; p < 0.05) and between-country differences (beta = 62.74; p < 0.001) significantly contribute to quality of life.
This study examines how different information processing tendencies are associated with economic decision-making in consumer contexts. Using eye-tracking technology, we analyze the relationship between visual attention patterns and task-specific decision accuracy in simulated online purchasing tasks. Participants (N = 100) evaluated mobile phone plans. Cluster analysis revealed two visual-attentional profiles: perfectionists and impulsive decision-makers. Results show that detailed attention, self-control, and systematic information processing are associated with higher decision accuracy. Age and conscientiousness positively correlate with deeper information processing and more accurate task performance. Rather than treating eye-tracking as a direct measure of decision quality, the study interprets gaze behavior as an indicator of how consumers inspect, compare, and revisit information in a digitally structured choice environment. The findings offer implications for consumer research, digital marketing, and interface design.
This study investigates the explanatory potential of cultural dimensions on ChatGPT usage rates at the societal level across 21 countries. The research uses country-level regression models (n = 21) to examine the relationship between Hofstede’s cultural dimensions and ChatGPT usage reported in a late 2023 survey. Results reveal a significant negative relationship between one dimension, Individualism/Collectivism, and ChatGPT usage, with collectivist societies such as Kenya, Pakistan, and India exhibiting the highest usage rates. This suggests that generative artificial intelligence adoption in these cultures was driven by imitation rather than innovation. The paper also discusses the issues of digital and data colonialism in the Global South, where ChatGPT usage reflects both technological diffusion and economic exploitation. The study provides insights into the intersection of culture, generative artificial intelligence, and global power dynamics.
This study examines research trends in digital technologies supporting predictive healthcare, with particular attention to the role of digital twins. A structured bibliometric analysis combined with qualitative thematic analysis was conducted using publications indexed in the Scopus and Web of Science databases from 2015 to 2025. The results indicate a clear shift towards integrated, data-driven healthcare solutions, in which digital twins function as central frameworks linking artificial intelligence, machine learning and Internet of Medical Things technologies. Three emerging thematic areas were identified: integrated patient data ecosystems, predictive and preventive digital twins, and digital twin–based treatment planning and patient response simulation. The findings highlight increasing interest in personalised, predictive and simulation-oriented healthcare models. At the same time, the analysis reveals a gap between technological development and routine clinical implementation. The study contributes to a clearer understanding of the evolving structure of this research field and outlines directions for future research and application in predictive healthcare.
This research explores the neurosociological dimensions of video games, focusing on their evolution, social impact, and neurological effects. Using a narrative review, we first examine their progression from playful activities to digital and connected experiences. We assess potential risks such as youth vulnerability, excessive screen time, lack of physical presence, gaming disorder, and aggressiveness. Conversely, we highlight positive aspects, including self-expansion, social play, flow states, excitement and relaxation, neurophysiological and psychological effects, and mental health applications. We further analyze gaming’s role in socialization, high-stimulation environments, disembodied interactions, and as a source of fun in modern society. Finally, we synthesize these themes through three key perspectives: the allure of screen flow among youth; the balance between digital and physical play for healthy development; and the importance of understanding both the risks and benefits of video games. This study frames video games as a cultural and cognitive technological force shaping human development.
Contemporary office building standards require the installation of advanced HVAC systems, which, alongside industrial processes, are significantly responsible for energy consumption. While the literature describes numerous multi-parameter, deep artificial neural networks that are highly complex but poorly explainable (black box), commercial control systems must be based on simple-to-implement, easily adaptable intelligent predictive models whose reliability stems from their monitorability and explainability. The study verified several edge-oriented approaches using machine learning techniques for short- and medium-term predictions (1 day) with relatively high granularity (15 minutes). High granularity of time intervals, combined with high prediction reliability, not only provide a practical opportunity to optimize energy consumption, but also to increase the efficiency of renewable energy sources exploitation. Operational, high granularity predictions can also be used to detect anomalies and attacks, as required by modern cybersecurity rules.
The video game industry is a thriving sector where the effects derived from its consumption are studied, among other aspects. This research analyzes the possible incidence of commercial video games on the mindfulness trait of players. To this end, a survey was carried out on a sample, mostly Spanish, of 225 people aged 17 to 66 years. The measure used to assess the mindfulness trait is the Five Facets of Mindfulness Questionnaire (FFMQ). Among the main results obtained, the length of time spent playing video games is slightly and directly associated with a higher mindfulness trait among non-meditating players. On the other hand, a positive association is also detected between the greater experience with video games, except for meditating players, with the FFMQ dimensions: not-judging inner experience dimension (particularly strong correlation in ADHD players), describing and non-reactivity to inner experience dimension.
The widespread diffusion of mobile devices has made mobile applications an integral part of everyday life across age groups. Although prior research has consistently identified social influence (SI) as an important determinant of technology acceptance, its role across different generations remains insufficiently examined in the context of mobile applications. This study investigates the impact of social influence on behavioural intention to use mobile applications among four generational cohorts: Baby Boomers, Generation X, Generation Y, and Generation Z. The analysis is based on data from a nationwide CAWI survey conducted in Poland (N = 2,400; 600 respondents per generation). Reliability analysis, exploratory factor analysis, linear regression, and comparative statistical tests were applied. The results show that social influence is a significant predictor of behavioural intention in all generational groups. However, no statistically significant differences are observed in the strength of the SI-BI relationship across generations. At the same time, significant differences emerge in perceived levels of social influence, with Baby Boomers reporting higher mean values than younger cohorts. The findings highlight generational differences in the perceived relevance of social influence rather than in its behavioural impact, contributing to research on human-technology interaction.
Purpose: This study examined the validity of serious game-based assessments (SGAs) for measuring Finnish primary school children's (Grades 1-4; n = 735) reading, spelling, and related cognitive skills. Methods: Performance in the digital SGAs was compared with corresponding paper-and-pencil tasks assessing word and pseudoword reading, sentence reading fluency, spelling, and underlying skills including phonological processing, rapid automatized naming, short-term memory, receptive vocabulary, and associative learning. Results: The SGAs showed good concurrent and construct validity for reading fluency, reading accuracy, spelling, rapid automatized naming, and vocabulary. The SGA tasks explained 72-80% of the variance in traditional reading fluency measures and 51-66% in reading accuracy. Conclusions: The findings indicate that serious game-based assessments provide a valid and engaging alternative to traditional literacy assessment tools. Implications for digital assessment and intervention in reading development are discussed.
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.
Reliable, low-carbon power systems depend on high-granularity, short-horizon forecasting, especially within islandable microgrids where consumers and prosumers shape real-time balance. Ultra-short-term (15-minute) forecasts support storage scheduling, demand response, and secure operation amid renewable intermittency and growing cyber risk. Forecasting approaches are classified into black-box, gray-box, and white-box methods, highlighting trade-offs in accuracy, explainability, and deployability. Operational use-cases are aligned with forecasting time scales, and current literature shows notable gaps: limited 15-minute multi-step studies, inconsistent evaluation protocols, insufficient attention to explainability, and restricted access to representative datasets. Design principles are outlined for deployable forecasting and control: standardised metrics and horizons, privacy-preserving data pipelines, explainability-first modelling, and transferable domain-specific hyperparameters. Addressing these gaps can increase renewable penetration, lower imbalance costs, strengthen cybersecurity compliance, and enhance resilience, delivering cleaner energy at reduced cost with higher quality of service. Researchers who want to support effective energy management technologies with their research should be aware of the current challenges.
