Search Results (5,626)

Background/Objectives: Multiple chronic conditions (MCCs) pose global health and social challenges, with caregiving often relying on family members, especially in low- and middle-income countries (LMICs). However, limited evidence exists regarding the factors influencing caregiver contribution (CC) to patient self-care among older adults with MCCs in these settings. Aim: The aim of this study was to examine the associations between caregivers’ and patients’ socio-demographic characteristics and patients’ clinical variables and the CC to patient self-care behaviors in adults with MCCs in an LMIC context. Methods: This multicenter, cross-sectional study included patient–caregiver dyads recruited from outpatient and community settings across Albania, between August 2020 and April 2021. CC was assessed using the Caregiver Contribution to Self-Care of Chronic Illness Inventory scale (CC-SCCII). Three multivariable linear regression models were used to explore associations with the three dimensions of CC to self-care maintenance, monitoring, and management. Results: Caregivers were mostly female, children, or spouses with a high level of education and employed. Patients were primarily female and had low education. Hypertension and diabetes were the most prevalent. Older and employed caregivers contributed less to CC to self-care maintenance, while higher education and caregiving experience increased it. Living with the patient and being a spouse reduced CC to self-care monitoring, whereas more caregiving hours and experience improved it. CC to self-care management was negatively influenced by cohabitation, presence of a second caregiver, and being a spouse, but improved with more caregiving hours. Conclusions: Socio-demographic and caregiving factors differently influence CC to self-care dimensions in older adults with MCCs in an LMIC. Tailored caregiver support programs are essential to enhance caregiver involvement and improve MCC patient outcomes in LMICs. Full article

Environmental, Social, and Governance (ESG) communications have proliferated across Fortune 500 companies, yet no validated frameworks exist for systematically distinguishing authentic from superficial positioning. This study develops and validates the Dynamic Authenticity Evaluation Model (DAEM), measuring three interactive dimensions of ESG communication authenticity: operational alignment, temporal consistency, and communication specificity. Through dual-evaluator protocols applied to eight mega-cap companies, DAEM achieves excellent inter-rater reliability (ICC = 0.85; Krippendorff’s α = 0.83). An event study analysis across sixteen major ESG announcements reveals no significant correlation between communication authenticity and abnormal stock returns (r = 0.289; p = 0.491), with effects being bounded below ±0.30% cumulative abnormal returns through equivalence testing. Preliminary stakeholder analysis suggests differential authenticity sensitivity, with employee engagement showing a stronger association with DAEM scores (r = 0.423) than market reactions (r = 0.289). Results indicate that authentic ESG communications influence non-market stakeholders more than short-term stock prices, suggesting that market value creation requires operational rather than symbolic approaches, while authentic communication remains important for stakeholder relationship management. Full article

In order to understand how health beliefs map onto health behaviors, a national survey, administered in the wake of the COVID-19 campus closures, was conducted to explore college students’ mind-body health beliefs and their health behaviors (across dimensions of physical exercise, diet/nutrition, and socialization). To this end, the Mind-Body Health Screener (MBHS), a five-item, Likert scale, brief measure, was developed. The present study applied an online survey design administered nationally to U.S. undergraduate students during the initial lockdowns with the pandemic (n = 557). To examine the psychometric properties of the MBHS, exploratory and confirmatory factor analyses were run as well as measures of reliability. Furthermore, linear regressions and effect sizes were computed to understand the connection between mind-body health beliefs and behaviors. While initial data supported the psychometric properties of the Mind-Body Health Screener (MBHS) developed for this purpose, substantive results suggested that mind-body health beliefs did not relate to mind-body health behaviors (either before or after the campus closures), aligning with the Cognitive Dissonance Theory. Post hoc analysis did, however, suggest a significant change in health behaviors from pre-campus closures to during the closures, suggesting students engaged in more physical exercise, eating behaviors, and socializing before campus closed, observed with small to large effects. Taken together, the findings of the present study illustrate how the Cognitive Dissonance Theory is a relevant perspective to consider the relation between health beliefs and behaviors during a period of immense stress, such as the COVID-19 initial campus closures. Full article

Since the end of Apartheid in 1994, South Africa has striven to address past discrimination against members of marginalized groups such as Africans, women and LGBTQ+ individuals. Sophisticated media legislation and a vibrant civil society forged in the struggle against Apartheid ensure limited discrimination in traditional media and relatively fringe online forums. However, subtle forms of undermining signal the persistent legacy of a colonial and patriarchal past. While incidents of online racism and sexism are relatively well documented, ableism deserves more attention. Despite growing scholarship on digital discrimination, a significant research gap remains in understanding how ableist microaggressions manifest online, particularly when intersecting with race and gender. As a result of established media tropes, microaggressions against people with disabilities are somewhat naturalized and reproduced on social media, yet their intersectional dimensions—especially targeting Black women with disabilities—remain underexplored. This paper addresses this gap through a focused case study of jokes targeting Black women with disabilities in one TikTok video and the approximately 700 comments. Considering (dis)ability’s intersections with race, gender, and socio-economic status, these media texts are subjected to a critical thematic analysis. The study also problematizes the methodological challenges associated with finding, identifying, and purposively selecting such content. The analysis reveals a set of historically and contextually rooted microaggressions expressed through humor, which, as a cultural expression, is inherently covert and thus hard to detect and regulate. This research contributes to understanding how intersectional ableism operates digitally and highlights the need for nuanced approaches to identifying subtle forms of discrimination in online spaces. Full article

This study investigates the relationship between artificial intelligence (AI) adoption and environmental, social, and governance (ESG) performance among 100 listed Saudi Arabian firms over the period 2015–2024. Drawing on panel data regression techniques, including fixed effects models with Driscoll–Kraay standard errors, pooled OLS with industry and year controls, and dynamic panel estimations using system GMM, the analysis reveals a significant and positive association between AI implementation and overall ESG scores. Disaggregated analysis shows that AI adoption is particularly associated with improvements in the environmental and social dimensions, with a more moderate relationship to governance practices. To address potential issues of cross-sectional dependence and heterogeneity, the study applies the Common Correlated Effects Mean Group (CCEMG) and Mean Group (MG) estimators as robustness checks, which confirm the consistency of the main findings. In addition, the Dumitrescu–Hurlin panel Granger causality test indicates that AI adoption Granger-causes ESG performance—especially in the environmental and social dimensions—while no reverse causality is observed. The results suggest that AI technologies are positively linked to firms’ sustainability strategies and performance, supporting the integration of digital transformation into national and corporate ESG agendas, particularly in emerging markets like Saudi Arabia. Full article

Digital Twin (DT) technology is increasingly recognized as a key enabler for optimizing design, operation, and management across the built environment. While several reviews have addressed DTs in either building- or city-scale contexts, a comprehensive integration of these two perspectives remains limited. This paper provides an updated overview of DT developments from Building Digital Twins (BDTs) to Urban Digital Twins (UDTs), aiming to identify convergences, divergences, and future directions. The analysis is conducted through a review of recent literature and selected case studies, considering technical, environmental, economic, and social dimensions. Findings reveal that although BDTs and UDTs share common conceptual and technological foundations, scaling from single assets to complex urban systems introduces new challenges in terms of interoperability, governance, and data management. Furthermore, while environmental and economic benefits are relatively well-documented, social implications, such as citizen engagement, inclusivity, and behavioral modeling, remain underexplored. This review highlights the novelty of adopting a cross-scale perspective, emphasizing the importance of integrating technical and social aspects to fully exploit the potential of DTs for sustainable and resilient transitions. The study concludes by outlining research gaps and recommending strategies for developing more integrated, socially aware DT frameworks in both building and urban contexts. Full article

Climate change mitigation for the built environment has become a subject of greatest urgency, as buildings account for nearly 40% of total energy consumption and nearly one-third of total CO₂ emissions. While environmental, social, and governance (ESG) indicators are increasingly used to monitor sustainability performance, their collective role in impacting building-related emissions is yet largely under-investigated. The current research closes that gap through an examination of the ESG dimension–CO₂ emissions intersection of 180 nations from 2000 to 2022, in the hope of illuminating how environmental, social, and governance elements interact to facilitate decarbonization. The research is guided by a multi-method design, including econometric examination, cluster modeling, and machine learning techniques, which provide causal evidence and predictive analysis, respectively. The findings reveal that the deployment of renewable energy significantly reduces emissions, while per capita energy use and PM_2.5 air pollution exacerbate this effect. The social indicators show mixed results: learning, women’s parliamentary representation, and women’s workforce representation reduce emissions, while food production and growth among the lowest-income individuals demonstrate higher emissions. Governance demonstrates mixed results as well, with good regulation reducing emissions under specific conditions yet primarily supporting high-income countries with superior infrastructure. The examination of clusters reveals that ESG-balanced performance is retained by countries in the low-emission clusters, whereas decentralized ESG pillars are associated with higher emissions. Machine learning confirms the existence of non-linear effects and identifies PM_2.5 exposure and renewable energy deployment as the strongest predictors of the relationship. In summary, the findings suggest that successful policies for decarbonizing the built environment are constructed upon the consistency of environmental, social, and governance plans, rather than single steps. Full article

Sustainability reporting has emerged as a pivotal tool for corporate accountability, integrating environmental, social, and economic performance into transparent disclosures that align with global frameworks such as the Global Reporting Initiative (GRI) Standards and the United Nations Sustainable Development Goals (SDGs). This study evaluates the environmental sustainability performance of Turkey’s automotive manufacturing sector by analyzing the extent and depth of GRI-based disclosures and their alignment with SDG targets. A mixed-method approach, combining quantitative Key Performance Indicator (KPI) coverage analysis with qualitative content assessment, was applied to sustainability reports from 12 major manufacturers. By identifying the most frequently reported indicators, assessing their coverage of economic, environmental, and social dimensions, and evaluating their direct relevance to specific SDGs, this research fills a critical gap and provides actionable insights for policymakers, industry leaders, and sustainability practitioners. The results indicate that while social indicators achieve the highest average disclosure rate (77.3%), environmental themes dominate narrative emphasis, reflecting sectoral materiality and regulatory pressures rather than proportional (KPI) coverage. Key gaps include underreporting of governance-related SDGs (e.g., SDG 5, SDG 8, SDG 16), limited target-level mapping, and a lack of measurable, outcome-based indicators. The study proposes a structured methodology for linking GRI metrics to SDG targets, enabling more consistent benchmarking and highlighting opportunities for balanced integration across all sustainability pillars. The findings contribute to both academic discourse and industry practice by demonstrating the need to bridge the gap between quantitative breadth and qualitative depth in sustainability reporting, ensuring more robust alignment with the 2030 Agenda. Full article

In the context of public space research, numerous studies highlight its vital role in fostering public life and social interaction. With urbanization on the rise and most people living in cities, acknowledging public spaces, and especially public squares, as key components of the urban realm is more important than ever. The success of space is frequently determined by its capacity to meet human needs, a condition that, in turn, is largely contingent upon specific design qualities. Literature identifies key qualities such as inclusiveness, accessibility and connectivity, sociability, vitality, perceptual and esthetic satisfaction, and participatory characteristics. While many studies explore these factors, little attention has been given to whether users and designers assign equal importance to them. This research addresses the question: To what extent do experts’ and users’ perceptions converge regarding the variables that determine the success of public spaces? To explore this, the study applies MICMAC method structural analysis that prioritizes variables based on their interdependence and dependence. The method is used with both design experts and public space users. Findings reveal convergence in perceptions regarding key parameters; specifically, strong convergence is observed in the qualities of participation and vitality, followed by sociability and perceptual and esthetic satisfaction. Moreover, the expert group prioritizes parameters related to sociability, accessibility and connectivity, and inclusiveness, reflecting contemporary design principles aimed at creating equitable, easily accessible, and inclusive spaces. In contrast, the user group focuses more on the experiential and esthetic dimension of space, adding variables related to perceptual and esthetic satisfaction and vitality. The study aims to inform more user-responsive public space design by bridging gaps between expert and user perspectives. Full article

In service-intensive, compliance-driven settings such as banking, identifying how internal corporate social responsibility (ICSR) fosters employees’ vitality and learning is crucial for sustainable organisational performance. Amid growing interest in employee thriving, this study explores how perceived internal corporate social responsibility (PICSR) and moral meaningfulness (MM) shape thriving at work (TaW) through organisational embeddedness (OE). Rooted in self-determination theory, the findings reveal OE as a key mediator between PICSR and TaW, shedding light on how ICSR initiatives influence employee dynamics. The study also reveals that MM alone does not significantly predict TaW directly, but does so indirectly through OE, highlighting the importance of contextual mechanisms. Additionally, it identifies a surprising negative moderating effect of risk-taking, one dimension of intrapreneurial behaviour (IB), on the relationship between OE and TaW, while innovativeness, another dimension, shows no such effect. Theoretical and practical implications underscore the importance of aligning ICSR practices with employees’ psychological needs, supporting moral alignment, and tailoring support for intrapreneurs. Organisations must achieve a balance between autonomy and security to sustain engagement and innovation, advancing human and organisational sustainability, ultimately leading to thriving. Full article

Traffic crashes remain a critical public safety issue and are among the leading causes of mortality worldwide. Understanding, analyzing, and forecasting crash trends are essential for implementing effective countermeasures and reducing injury severity. In response to the growing number of crashes and their associated economic and social costs, this study presents a geospatial analytical framework for prioritizing and classifying roadway segments based on crash trends. The framework focuses on a major freeway corridor in the United States, covering a four-year period across 20 counties. This methodology employs spatiotemporal analysis, which integrates both spatial (geographic) and temporal (time-based) dimensions to better understand how crash patterns evolve over time and space. A central component of the analysis is Space–Time Cube (STC) modeling, a three-dimensional GIS-based visualization, and an analytical approach that organizes data into spatial locations (x and y) across a sequence of temporal bins (z-axis) to reveal patterns that may not be evident in a two-dimensional analysis. Additionally, emerging pattern analysis, specifically Emerging Hotspot Analysis (EHA), is used to identify statistically significant trends in crash frequency over time. The results indicate a significant spatial clustering of crashes, with high-risk segments predominantly located in densely populated urban areas with high traffic volumes. Crash hotspots were classified into five distinct categories: persistent, intensifying, new, sporadic, and diminishing, enabling transportation agencies to tailor interventions based on temporal dynamics. The proposed geospatial framework enhances decision making for roadway safety improvements and can be adapted for use in other regional corridors to support infrastructure investment and advance public safety. Full article

Smart cities are an emerging technology that is receiving new ethical attention due to recent advancements in artificial intelligence. This paper provides an overview of smart city ethics while simultaneously performing novel theorization about the definition of smart cities and the complicated relationship between (smart) cities, ethics, and politics. We respond to these ethical issues by providing an innovative representation of the agent-deed-consequence (ADC) model in symbolic terms through deontic logic. The ADC model operationalizes human moral intuitions underpinning virtue ethics, deontology, and utilitarianism. With the ADC model made symbolically representable, human moral intuitions can be built into the algorithms that govern autonomous vehicles, social robots in healthcare settings, and smart city projects. Once the paper has introduced the ADC model and its symbolic representation through deontic logic, it demonstrates the ADC model’s promise for algorithmic ethical decision-making in four dimensions of smart city ethics, using examples relating to public safety and waste management. We particularly emphasize ADC-enhanced ethical decision-making in (economic and social) sustainability by advancing an understanding of smart cities and human-AI teams (HAIT) as group agents. The ADC model has significant merit in algorithmic ethical decision-making, especially through its elucidation in deontic logic. Algorithmic ethical decision-making, if structured by the ADC model, successfully addresses a significant portion of the perennial questions in smart city ethics, and smart cities built with the ADC model may in fact be a significant step toward resolving important social dilemmas of our time. Full article

This research investigates the impact of Environmental, Social, and Governance (ESG) practices on the financial performance of logistics firms, with a focus on the mediating role of stakeholder pressure. Utilizing survey data collected from Korean logistics firms (N = 256 valid responses) and employing structural equation modeling, the findings indicate that social practices exert a significant direct effect on financial outcomes. Conversely, environmental and governance practices impact financial performance indirectly, through stakeholder pressure. These findings suggest that ESG activities impact financial performance via distinct mechanisms, contingent upon the specific ESG dimension and the level of stakeholder engagement. The study advances ESG literature by providing a disaggregated analysis of ESG effectiveness and empirically confirming stakeholder pressure as a critical pathway. Practically, the results underscore the need for logistics firms to align their ESG strategies with stakeholder expectations and institutional pressures, thereby optimizing both sustainability and financial performance. Full article

When a city decides to undertake a certain urban project, is it modifying just the physical environment or the social fabric that dwells within? This work investigates the relationship between the geometric configuration of urban space (geometry–city) and the topology of the networks of encounters of its inhabitants (network–city) that form through daily interactions. The research departs from the hypothesis that changes in geometry–city would not significantly alter the topology of the network–city, testing this proposition conceptually through abstract computational simulations developed specifically for this study. In this simulator, abstract maps with buildings distributed over different primary geometries are generated and have activities (use: home or work) and a population assigned. Encounters of the “inhabitants” are registered while daily commute routines, enough to achieve differentiation and stability, are run. The initial results revealed that the geometry description was not enough, and definitions regarding activity attribution were also necessary. Thus, we could not confirm nor reject the original hypothesis exactly, but it had to be complemented, including the idea of an activity–city dimension. We found that despite the geometry–city per se not determining the structure of the network–city, the spatial (geometric) distribution of activities directly impacts the resulting topology. Urban geometry influences networks–city only insofar as it conforms to activity–city, defining areas for activities or restricting routing between them. But it is the geometry of localization of the activities that has a direct impact on the topology of the network–city. This conceptual discovery can have significant implications for urban planning if corroborated in real-world situations. It could suggest that land use policies may be more effective for intervening in network-based characteristics, like social cohesion and resilience, than purely morphological interventions. Full article

Trust plays a pivotal role in individuals’ interactions with technological systems, and those incorporating artificial intelligence present significantly greater challenges than traditional systems. The current landscape of higher education is increasingly shaped by the integration of AI assistants into students’ classroom experiences. Their appropriate use is closely tied to the level of trust placed in these tools, as well as the strategies adopted to critically assess the accuracy of AI-generated content. However, scholarly attention to this dimension remains limited. To explore these dynamics, this study applied the POTDAI evaluation framework to a sample of 132 engineering and social sciences students at Camilo José Cela University in Madrid, Spain. The findings reveal a general lack of trust in AI assistants despite their extensive use, common reliance on inadequate verification methods, and a notable skepticism regarding professors’ ability to detect AI-related errors. Additionally, students demonstrated a concerning misperception of the capabilities of different AI models, often favoring less advanced or less appropriate tools. These results underscore the urgent need to establish a reliable verification protocol accessible to both students and faculty, and to further investigate the reasons why students opt for limited tools over the more powerful alternatives made available to them. Full article