Search Results (2,478)

As artificial intelligence (AI) technology becomes increasingly widespread in organizations, its impact on individual employees’ psychology and behavior has garnered growing attention. Existing research primarily focuses on AI’s effects on organizational performance and job design, with limited exploration of its mechanisms influencing individual employees, particularly in the critical area of risk-taking behavior, which is essential to organizational innovation. This research develops a moderated mediation model grounded in social cognitive theory (SCT) to explore how AI usage affects the willingness to take risks. A three-wave longitudinal study collected and statistically analyzed data from 442 participants. The findings reveal that (1) AI usage significantly enhances employees’ willingness to take risks; (2) self-efficacy serves as a partial mediator in the connection between AI usage and the willingness to take risks; and (3) learning goal orientation moderates both the relationship between AI usage and self-efficacy, as well as the mediating effect. This research enhances our understanding of AI’s impact on organizational behavior and provides valuable insights for human resource management in the AI era. Full article

This study investigates the foundational causes of unsustainability that obstruct efforts to address global challenges such as climate change, environmental degradation, water crises, and public health deterioration. Using qualitative research with in-depth expert interviews from education, environmental studies, and business, it finds that these global challenges, while visible on the surface, are deeply rooted in worldviews that shape human behavior, societal structures, and policies. Building on this insight, the thematic analysis manifests three interrelated systemic paradigms as the fundamental drivers of unsustainability: a crisis of wholeness, reflected in fragmented identities and collective disorientation; a disconnection from nature, shaped by human-centered perspectives; and the influence of dominant political-economic systems which prioritize growth logics over ecological and social concerns. These paradigms underlie both structural and cognitive barriers to systemic transformation, which influence the design and implementation of education for sustainability. By clarifying a body of knowledge and systemic paradigms regarding unsustainability, this paper calls for transformative education that promotes a holistic, value-based approach, eco-empathy, and critical thinking, aiming to equip future generations with the tools to challenge and transform unsustainable systems. Full article

Pupillometry is commonly used to evaluate cognitive effort, attention, and facial expression response, offering valuable insights into human performance. The combination of eye tracking and facial expression data under the iMotions platform provides great opportunities for multimodal research. However, there is a lack of standardized pipelines for managing pupillometry data on a multimodal platform. Preprocessing pupil data in multimodal platforms poses challenges like timestamp misalignment, missing data, and inconsistencies across multiple data sources. To address these challenges, the authors introduced a systematic preprocessing pipeline for pupil diameter measurements collected using iMotions 10 (version 10.1.38911.4) during an endoscopy simulation task. The pipeline involves artifact removal, outlier detection using advanced methods such as the Median Absolute Deviation (MAD) and Moving Average (MA) algorithm filtering, interpolation of missing data using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and mean pupil diameter calculation through linear regression, as well as normalization of mean pupil diameter and integration of the pupil diameter dataset with facial expression data. By following these steps, the pipeline enhances data quality, reduces noise, and facilitates the seamless integration of pupillometry other multimodal datasets. In conclusion, this pipeline provides a detailed and organized preprocessing method that improves data reliability while preserving important information for further analysis. Full article

Background/Objective: Food insecurity refers to social or economic challenges that limit or create uncertainty around access to enough food. Among college students, food security status is usually determined with the USDA 10-item Food Security Survey Module, which has not been validated for this population. Nutrition security refers to consistent access to food and beverages that promote well-being, prevent disease, and emphasize equitable access to healthy, safe, and affordable foods. Currently, there is no standardized measure that assesses food and nutrition security tailored to the unique experiences of college students. This study aims to evaluate the validity and reliability of a newly developed College Student Food and Nutrition Security Survey Module (CS-FNSSM). Methods: A mixed-methods approach that combined an online survey with semi-structured cognitive interviews. Participants were students aged 18 and older from U.S. public universities. Quantitative data were analyzed using RStudio (version 4.4.1), and interview transcripts were thematically analyzed. Results: Survey responses were collected from 953 participants, including a subset of 69 participants for reliability testing and 30 participants for cognitive interviews. Rasch analysis showed good item performance and structural validity. The CS-FNSSM demonstrated strong sensitivity (89.09%), specificity (76.2%), moderate test–retest reliability (0.59), and good internal consistency (Cronbach’s alpha = 0.79). Qualitative findings confirmed participant understanding of the items. Conclusions: The CS-FNSSM effectively identifies food and nutrition insecurity, with nutrition security emerging as a key issue. Addressing both is crucial for promoting the overall health and well-being of college students. Full article

In the context of Industry 4.0, user demands for industrial robots have shifted toward diversification and experience-orientation. Effectively integrating users’ affective imagery requirements into industrial-robot form design remains a critical challenge. Traditional methods rely heavily on designers’ subjective judgments and lack objective data on user cognition. To address these limitations, this study develops a comprehensive methodology grounded in Kansei engineering that combines Extenics-based semantic analysis, eye-tracking experiments, and user imagery evaluation. First, we used web crawlers to harvest user-generated descriptors for industrial floor-cleaning robots and applied Extenics theory to quantify and filter key perceptual imagery features. Second, eye-tracking experiments captured users’ visual-attention patterns during robot observation, allowing us to identify pivotal design elements and assemble a sample repository. Finally, the semantic differential method collected users’ evaluations of these design elements, and correlation analysis mapped emotional needs onto stylistic features. Our findings reveal strong positive correlations between four core imagery preferences—“dignified,” “technological,” “agile,” and “minimalist”—and their corresponding styling elements. By integrating qualitative semantic data with quantitative eye-tracking metrics, this research provides a scientific foundation and novel insights for emotion-driven design in industrial floor-cleaning robots. Full article

With the advancement of immersive technologies, metaverse museum exhibitions have become an increasingly important medium through which audiences access cultural content and experience artistic works. This study aims to identify the key factors influencing visitors’ processual experiences in metaverse museum exhibitions and to explore how these factors collectively contribute to the formation of satisfaction with the visiting experience. Adopting an interdisciplinary theoretical perspective, the study integrates the Experience Economy theory with the Stimulus–Organism–Response (SOR) model to construct a systematic theoretical framework. This framework reveals how exhibition-related stimuli affect visitors’ behavioral intentions through psychological response pathways. Specifically, perceived educational appeal, interactive entertainment, escapist experience, and perceived visual aesthetics are defined as stimulus variables, while psychological immersion, emotional trigger, and cognitive engagement are introduced as organismic variables to explain their effects on satisfaction with the visiting experience and social sharing intention as response variables. Based on 507 valid responses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for empirical analysis. The results indicate that interactive entertainment and escapist experience have significant positive effects on psychological responses, serving as key drivers of deep visitor engagement. Emotional Trigger acts as a significant mediator between exhibition stimuli and satisfaction with the visiting experience, which in turn significantly predicts social sharing intention. In contrast, perceived educational appeal and perceived visual aesthetics exhibit weaker impacts at the cognitive and behavioral levels. This study not only identifies these weakened pathways but also proposes optimization strategies grounded in experiential construction and cognitive synergy, offering guidance for enhancing the educational function and deep experiential design of metaverse exhibitions. The findings validate the applicability of the Experience Economy theory and the SOR model in metaverse cultural contexts and deepen our understanding of the psychological mechanisms underlying immersive cultural experiences. This study further provides a pathway for shifting exhibition design from a “content-oriented” to an “experience-driven” approach, offering theoretical and practical insights into enhancing audience engagement and cultural communication effectiveness in metaverse museums. Full article

Background: Emerging evidence indicates that some individuals recovering from COVID-19 develop persistent symptoms, including fatigue, pain, cognitive difficulties, and psychological distress, commonly known as Long COVID. These symptoms often overlap with those seen in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis (CFS/ME), underscoring the need for integrative, non-pharmacological interventions. This Phase II controlled trial aimed to evaluate the feasibility and preliminary efficacy of Heart Rate Variability Biofeedback (HRV-BF) in individuals with Long COVID who meet the diagnostic criteria for CFS/ME. Specific objectives included assessing feasibility indicators (drop-out rates, side effects, participant satisfaction) and changes in fatigue, depression, anxiety, pain, and health-related quality of life. Methods: Participants were assigned alternately and consecutively to the HRV-BF intervention or Treatment-as-usual (TAU), in a predefined 1:1 sequence (quasirandom assignment). The intervention consisted of 10 HRV-BF sessions, held twice weekly over 5 weeks, with each session including a 10 min respiratory preparation and 40 min of active training. Results: The overall drop-out rate was low (5.56%), and participants reported a generally high level of satisfaction. Regarding side effects, the mean total Simulator Sickness Questionnaire score was 24.31 (SD = 35.42), decreasing to 12.82 (SD = 15.24) after excluding an outlier. A significantly greater improvement in severe fatigue was observed in the experimental group (H = 4.083, p = 0.043). When considering all outcomes collectively, a tendency toward improvement was detected in the experimental group (binomial test, p < 0.0001). Conclusions: HRV-BF appears feasible and well tolerated. Findings support the need for Phase III trials to confirm its potential in mitigating fatigue in Long COVID. Full article

As large language models (LLMs) and vision–language models (VLMs) become increasingly used in robotics area, a crucial question arises: to what extent do these models replicate human-like cognitive processes, particularly within socially interactive contexts? Whilst these models demonstrate impressive multimodal reasoning and perception capabilities, their cognitive plausibility remains underexplored. In this study, we address this gap by using human visual attention as a behavioural proxy for cognition in a naturalistic human-robot interaction (HRI) scenario. Eye-tracking data were previously collected from participants engaging in social human-human interactions, providing frame-level gaze fixations as a human attentional ground truth. We then prompted a state-of-the-art VLM (LLaVA) to generate scene descriptions, which were processed by four LLMs (DeepSeek-R1-Distill-Qwen-7B, Qwen1.5-7B-Chat, LLaMA-3.1-8b-instruct, and Gemma-7b-it) to infer saliency points. Critically, we evaluated each model in both stateless and memory-augmented (short-term memory, STM) modes to assess the influence of temporal context on saliency prediction. Our results presented that whilst stateless LLaVA most closely replicates human gaze patterns, STM confers measurable benefits only for DeepSeek, whose lexical anchoring mirrors human rehearsal mechanisms. Other models exhibited degraded performance with memory due to prompt interference or limited contextual integration. This work introduces a novel, empirically grounded framework for assessing cognitive plausibility in generative models and underscores the role of short-term memory in shaping human-like visual attention in robotic systems. Full article

Employee creativity is essential for navigating digital disruption and maintaining organizational competitiveness; however, the mechanisms through which digital leadership fosters creativity remain underexplored. This study investigates the psychological and social processes through which digital leadership influences workplace creativity. Grounded in social cognitive and social exchange theories, the proposed model incorporates innovation self-efficacy and knowledge sharing as mediators and technology readiness as a moderator. Data were collected using a three-wave, time-lagged, multi-source survey design from 234 matched respondents, including employees and supervisors, across 20 business units in seven regional branches of a large Chinese organization undergoing digital transformation. The findings indicate that digital leadership significantly enhances employee creativity through the partial mediation of both innovation self-efficacy and knowledge sharing. Notably, the indirect effect through knowledge sharing was stronger, underscoring the critical role of collaborative processes in driving creativity. Furthermore, technology readiness positively moderates the effects of digital leadership on both mediators and amplifies the indirect effects on creativity. These findings provide valuable insights into how organizations can leverage digital leadership more effectively by aligning leadership strategies with employees’ psychological readiness and fostering a digitally supportive work environment. Full article

Background: Comprehensive Geriatric Assessment (CGA) is essential for evaluating older cancer patients, but significant gaps persist in both research and clinical practice. This study aimed (I) to identify the CGA elements that most influence anti-cancer treatment decisions in older patients and (II) to explore the predictive value of CGA components for mortality. Methods: This observational study included older patients with newly diagnosed, histologically confirmed solid or hematological cancers, recruited consecutively from 2003 to 2023. Participants were followed for four years. The data collected included CGA measures of functional (Activities of Daily Living-ADL), cognitive (Mini-Mental State Examination-MMSE), and emotional (Geriatric Depression Scale-GDS) domains. Patients were categorized into frail, vulnerable, or fit groups based on Balducci’s criteria. Statistical analyses included decision tree modeling and Cox regression to identify predictors of mortality. Results: A total of 7022 patients (3222 females) were included, with a mean age of 78.3 ± 12.9 years. The key CGA factors influencing treatment decisions were ADL (first step), cohabitation status (second step), and age (last step). After four years, 21.9% patients had died. Higher GDS scores (OR 1.04, 95% CI 1.01–1.07, p = 0.04) were independently associated with survival in men and living with family members (OR 1.67, 95% CI 1.35–2.07, p < 0.001) in women. Younger patients (<77 years) showed both MMSE and GDS as significant risk factors for mortality. Conclusions: Functional capacity, cohabitation status, and GDS scores are crucial for guiding treatment decisions and predicting mortality in older cancer patients, emphasizing the need for a multidimensional geriatric assessment. Full article

Background/Objectives: Given the heterogeneous nature of long COVID, its treatment and management remain challenging. This study aimed to investigate whether poor pre-pandemic sleep quality, its deterioration during the peak of the pandemic, and circadian preference increase the risk of long COVID symptoms. Methods: An online survey was conducted between 9 October and 12 December 2022, with 384 participants who had recovered from COVID-19 at least three months prior to data collection. Participants were categorized based on the presence of at least one long COVID symptom. Logistic regression models assessed associations between sleep-related variables and long COVID symptoms. Results: Participants with long COVID symptoms reported significantly poorer sleep quality, higher perceived stress, greater somatic and cognitive pre-sleep arousal, and elevated levels of post-traumatic stress symptoms, anxiety, depression, and aggression. Fatigue (39.8%) and memory problems (37.0%) were the most common long COVID symptoms. Sleep deterioration during the pandemic peak was reported by 34.6% of respondents. Pre-pandemic poor sleep quality, its deterioration during the pandemic, and poor sleep at the time of the survey were all significantly associated with long COVID. An extreme morning chronotype consistently predicted long COVID symptoms across all models, while an extreme evening chronotype was predictive only when accounting for sleep quality changes during the pandemic. COVID-19 frequency, severity, financial impact, and somatic pre-sleep arousal were significant predictors in all models. Conclusions: Poor sleep quality before the pandemic and its worsening during the pandemic peak are associated with a higher likelihood of long COVID symptoms. These findings underscore the need to monitor sleep health during pandemics and similar global events to help identify at-risk individuals and mitigate long-term health consequences, with important clinical and societal implications. Full article

Background: Accelerated demographic aging in Hungary and across Europe presents significant public health and socioeconomic challenges, particularly in preserving cognitive function and preventing neurodegenerative diseases. Modifiable lifestyle factors—especially dietary habits—play a critical role in brain aging and cognitive decline. Objective: This narrative review explores the mechanisms by which Western dietary patterns contribute to cognitive impairment and neurovascular aging, with specific attention to their relevance in the Hungarian context. It also outlines the rationale and design of the Semmelweis Study and its workplace-based health promotion program targeting lifestyle-related risk factors. Methods: A review of peer-reviewed literature was conducted focusing on Western diet, cognitive decline, cerebrovascular health, and dietary interventions. Emphasis was placed on mechanistic pathways involving systemic inflammation, oxidative stress, endothelial dysfunction, and decreased neurotrophic support. Key findings: Western dietary patterns—characterized by high intakes of saturated fats, refined sugars, ultra-processed foods, and linoleic acid—are associated with elevated levels of 4-hydroxynonenal (4-HNE), a lipid peroxidation product linked to neuronal injury and accelerated cognitive aging. In contrast, adherence to Mediterranean dietary patterns—particularly those rich in polyphenols from extra virgin olive oil and moderate red wine consumption—supports neurovascular integrity and promotes brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) activity. The concept of “cognitive frailty” is introduced as a modifiable, intermediate state between healthy aging and dementia. Application: The Semmelweis Study is a prospective cohort study involving employees of Semmelweis University aged ≥25 years, collecting longitudinal data on dietary, psychosocial, and metabolic determinants of aging. The Semmelweis–EUniWell Workplace Health Promotion Model translates these findings into practical interventions targeting diet, physical activity, and cardiovascular risk factors in the workplace setting. Conclusions: Improving our understanding of the diet–brain health relationship through population-specific longitudinal research is crucial for developing culturally tailored preventive strategies. The Semmelweis Study offers a scalable, evidence-based model for reducing cognitive decline and supporting healthy aging across diverse populations. Full article

Cybersecurity threats increasingly originate from human actions within organizations, emphasizing the need to understand behavioral factors behind non-compliance with information security policies (ISPs). Despite the presence of formal security policies, insider threats—whether accidental or intentional—remain a major vulnerability. This study addresses the gap in behavioral cybersecurity research by developing an integrated conceptual model that draws upon Operant Conditioning Theory (OCT), Protection Motivation Theory (PMT), and the Theory of Planned Behavior (TPB) to explore ISP compliance. The research aims to identify key cognitive, motivational, and behavioral factors that shape employees’ intentions and actual compliance with ISPs. The model examines seven independent variables of perceived severity: perceived vulnerability, rewards, punishment, attitude toward the behavior, subjective norms, and perceived behavioral control, with intention serving as a mediating variable and actual ISP compliance as the outcome. A quantitative approach was used, collecting data via an online survey from 302 employees across the public and private sectors. Structural Equation Modeling (SEM) with SmartPLS software (v.4.1.1.2) analyzed the complex relationships among variables, testing the proposed model. The findings reveal that perceived severity, punishment, attitude toward behavior, and perceived behavioral control, significantly and positively, influence employees’ intentions to comply with information security policies. Conversely, perceived vulnerability, rewards, and subjective norms do not show a significant effect on compliance intentions. Moreover, the intention to comply strongly predicts actual compliance behavior, thus confirming its key role as a mediator linking cognitive, motivational, and behavioral factors to real security practices. This study offers an original contribution by uniting three well-established theories into a single explanatory model and provides actionable insights for designing effective, psychologically informed interventions to enhance ISP adherence and reduce insider risks. Full article

Background: Parents of children with type 1 diabetes play a key role in managing their child’s self-management, which can be stressful and burdensome. High involvement can lead to reactions such as emotional, cognitive, and physical exhaustion in parents. Understanding parents’ psychosocial impact due to their child’s disease is crucial for the family’s overall well-being. The purpose of this study was to describe stress and burden experienced by parents in families with children living with type 1 diabetes. Methods: This study utilized a qualitative approach, analyzing interviews with 16 parents of children aged 10 to 17 years living with T1D through qualitative content analysis. The data collection occurred between January and February 2025. Results: Managing a child’s Type 1 diabetes can be tough on family relationships, affecting how partners interact, intimacy, and sibling relationships. The constant stress and worry might leave parents feeling exhausted, unable to sleep, and struggling to think clearly, on top of the pain of losing a normal everyday life. The delicate balance between allowing a child with type 1 diabetes to be independent and maintaining control over their self-management renders these challenges even more demanding for the parents. Conclusions: Parents’ experiences highlight the need for robust support systems, including dependable school environments, trustworthy technical devices, reliable family and friends, and accessible healthcare guidance. These elements are essential not only for the child’s health and well-being but also for alleviating the emotional and practical burdens parents face. Full article

Background: Artificial intelligence (AI) is being increasingly promoted as a means to enhance diagnostic accuracy, to streamline workflows, and to improve overall care quality in primary care. However, empirical evidence on how primary care physicians (PCPs) perceive, engage with, and emotionally respond to AI technologies in everyday clinical settings remains limited. Concerns persist regarding AI’s usability, transparency, and potential impact on professional identity, workload, and the physician–patient relationship. Methods: This qualitative study investigated the lived experiences and perceptions of 28 PCPs practicing in diverse outpatient settings across Germany. Participants were purposively sampled to ensure variation in age, practice characteristics, and digital proficiency. Data were collected through in-depth, semi-structured interviews, which were audio-recorded, transcribed verbatim, and subjected to rigorous thematic analysis employing Mayring’s qualitative content analysis framework. Results: Participants demonstrated a fundamentally ambivalent stance toward AI integration in primary care. Perceived advantages included enhanced diagnostic support, relief from administrative burdens, and facilitation of preventive care. Conversely, physicians reported concerns about workflow disruption due to excessive system prompts, lack of algorithmic transparency, increased cognitive and emotional strain, and perceived threats to clinical autonomy and accountability. The implications for the physician–patient relationship were seen as double-edged: while some believed AI could foster trust through transparent use, others feared depersonalization of care. Crucial prerequisites for successful implementation included transparent and explainable systems, structured training opportunities, clinician involvement in design processes, and seamless integration into clinical routines. Conclusions: Primary care physicians’ engagement with AI is marked by cautious optimism, shaped by both perceived utility and significant concerns. Effective and ethically sound implementation requires co-design approaches that embed clinical expertise, ensure algorithmic transparency, and align AI applications with the realities of primary care workflows. Moreover, foundational AI literacy should be incorporated into undergraduate health professional curricula to equip future clinicians with the competencies necessary for responsible and confident use. These strategies are essential to safeguard professional integrity, support clinician well-being, and maintain the humanistic core of primary care. Full article