Search Results (1,158)

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured. Full article

As generative AI technologies become increasingly embedded in educational cloud platforms, understanding their impact on teacher professional development is essential. Grounded in the stimulus–organism–response (S–O–R) framework, this study investigates how AI-driven stimuli—hedonicity, interactivity, and immersion—influence teachers’ self-regulation and resilience. Using structural equation modeling, data were collected from in-service teachers in Taiwan who actively utilize educational cloud platforms. The results reveal that all three AI-driven stimuli significantly enhance teachers’ self-regulation and resilience, which in turn are significantly associated with perceived value co-creation intentions. Specifically, self-regulation enables teachers to manage goals effectively, while resilience supports their recovery from technical setbacks. The findings indicate that self-regulation positively influences resilience, and both appear to mediate the relationship between perceived AI-driven stimuli and teachers’ value co-creation intentions. This study highlights the potential role of teachers’ psychological adaptability in AI-enhanced environments. Practical implications suggest that platform developers and administrators should prioritize AI features that foster self-directed learning and emotional engagement to promote collaborative willingness and professional alignment within modern educational ecosystems rather than implying proven macro-level transformation. Full article

Generative artificial intelligence (GenAI) is increasingly used in entrepreneurial practice, yet limited empirical evidence explains how sustainability-oriented entrepreneurs appropriate it in relation to Triple Bottom Line (TBL) performance. This study examines the strategic alignment paradox faced by entrepreneurs who pursue accountable sustainability outcomes while using fast-moving, probabilistic, and partly opaque technologies. It investigates whether Sustainability Orientation (SO) is associated with Sustainable Entrepreneurial Performance (SEP) through five theoretically distinct GenAI appropriation dimensions: Adoptive, Customized, Ethical, Integrative, and Interface Appropriation. In this study, Ethical Appropriation is interpreted as ethical-boundary permissiveness because the retained GAIA items capture acceptance of self-serving, improper, or beyond-intended GenAI use rather than broad responsible-AI governance. A cross-sectional survey of 360 GenAI-using entrepreneurs and SME owner-managers in Saudi Arabia was analyzed using PLS-SEM in SmartPLS 4, with a 10,000-resample bootstrapping procedure, MICOM, permutation-based multi-group analysis, and higher-order robustness tests. The empirical results show that SO is positively and significantly associated with Customized, Ethical, and Integrative Appropriation, but has no significant relationship with Adoptive or Interface Adoptive Appropriation or Interface Appropriation. The aggregate model explains 60.8% of the variance in SEP. Mediation analysis indicates that the observed indirect association between SO and aggregate SEP is statistically significant only through the Customized, Ethical, and Integrative pathways. The pillar-specific analysis further shows that the aggregate SEP construct masks important differences across TBL outcomes: Integrative Appropriation is consistently associated with all three pillars; Ethical Appropriation is selectively associated with economic sustainability and should be interpreted as a governance warning rather than as evidence of responsible-AI governance; and Customized Appropriation is associated only with environmental and social sustainability. Finally, multi-group analysis provides no statistically robust evidence of gender-based heterogeneity after Bonferroni correction, suggesting that the SO–GenAI appropriation–SEP relationships are broadly comparable across male and female respondents in this sample. Overall, the findings suggest that GenAI should not be treated as a monolithic adoption construct; its sustainability implications depend on how generative tools are customized for specific tasks, interpreted with attention to ethical-boundary permissiveness, and structurally integrated into business routines. Full article

The contemporary educational system places complex demands on educators, emphasizing the importance of pedagogical-didactical competencies and teacher self-efficacy alongside subject-matter expertise. The purpose of this study was to examine how future STEM educators assess their own perceived pedagogical-didactical capabilities for direct teaching and classroom management through the construct of teacher self-efficacy, examining variations by gender, student cohort, and academic major. The research was conducted in autumn 2025 using a near-census sample of graduate teaching track students at the Faculty of Science in Split, utilizing the Norwegian Teacher Self-Efficacy Scale. The descriptive and non-parametric results indicate that future teachers express higher baseline scores in the domains of instruction and coping with changes, whereas the lowest self-assessment was recorded regarding the adaptation of teaching to individual student needs. While gender and student cohort demonstrated no statistically significant impact on overall self-efficacy, a significant difference was determined in the domain of maintaining discipline with respect to the study major, with Informatics and Technology students exhibiting the greatest sense of confidence. In conclusion, the findings highlight that while students are well-prepared for content delivery, there remains a critical need to enhance initial teacher education programs by introducing practical tools for managing challenging student behaviors and working within heterogeneous classrooms. Full article

Against the backdrop of the environmental crisis, access to water has become a critical challenge at both global and local scales. In Slovenia, dispersed settlement patterns mean that a significant share of the population relies on small-scale, self-managed, or insufficiently monitored water supply systems. This paper presents an exploratory practice-based case study and develops a design framework that integrates natural processes, vernacular knowledge, and new technology for water-related challenges in unmonitored catchment areas. The framework is articulated through the development of a site-responsive prototype for the Lipnik Spring in Triglav National Park, where elevated E. coli levels have been detected. The fabricated component is a 3D-printed ceramic gyroid filtration module, while the proposed system-level concept integrates an Archimedes’ screw mechanism to investigate how the kinetic energy of flowing water could support operation without external electricity. The study documents how ecological processes, local material knowledge, and digital fabrication were combined within a sustainability-oriented design process. Contextual iterations in Georgia and France apply the framework’s decision-making logic to different environmental conditions and locally available materials. The paper contributes to design practice research and sustainability-oriented prototype development by articulating a structured approach to designing context-specific water-access concepts with low operational energy demand for remote settings. Full article

This article develops a conceptual account of semantic displacement in conversational AI. The central question concerns how agency is affected when systems do more than automate information retrieval and begin to supply the descriptions, classifications and normative cues through which users understand what they are doing. Drawing on philosophy of action, philosophy of language, hermeneutics, philosophy of technology and critical accounts of algorithmic mediation, this article reconstructs the relation between meaning and action as a condition of agency. Its methodological approach is conceptual and diagnostic, oriented toward clarifying a problem that becomes visible when established theories are brought together in relation to contemporary conversational systems. The article interprets these systems as operational semantic infrastructures that organize context-sensitive linguistic uptake within practical environments such as health, work, education, administration and everyday self-management. It then introduces semantic displacement as the condition in which action-relevant meanings become increasingly organized, prioritized and consolidated outside the agent’s own participatory interpretation. The argument contributes a vocabulary for distinguishing agency-enhancing semantic support from forms of semantic substitution that weaken interpretive participation. It concludes by proposing semantic sovereignty and interpretive contestability as normative ideals for human agency in AI-mediated environments. The argument specifies action as intentional conduct understood under socially available descriptions and cognition as situated interpretive sense-making rather than purely internal computation. It also clarifies three conditions under which semantic support becomes displacement: opaque semantic generation, practical stabilization and reduced interpretive contestability. Full article

Background: As mobile health (mHealth) technologies become increasingly integrated into chronic disease management, understanding how mHealth literacy is associated with hypertension-related knowledge, attitudes, and practices (KAPs) is important. However, the psychosocial factors involved in this association remain unclear. This study aimed to examine the association between mHealth literacy and hypertension-related KAP among older adults with hypertension and to explore whether health empowerment and patient activation were involved in statistically significant indirect pathways. Methods: A cross-sectional survey was conducted among 1500 community-dwelling older adults with hypertension in Hebei Province, China, between December 2024 and June 2025. Data were collected using the Problem-Based mHealth Literacy Scale, the Health Empowerment Scale for Elderly Patients with Chronic Disease, the Patient Activation Measure, and the Community Elderly Hypertension Patients Health Science Popularization Cognitive Scale. Partial correlation analyses were performed after controlling for sociodemographic covariates. An adjusted structural equation model was used to examine the hypothesized indirect pathway structure, and indirect effects were tested using bootstrapping with 5000 resamples. Results: mHealth literacy was positively associated with hypertension-related KAP. The total effect of mHealth literacy on hypertension-related KAP was 0.399 (95% CI: 0.347–0.499), including a direct effect of 0.290 (95% CI: 0.238–0.341) and a total indirect effect of 0.108 (95% CI: 0.082–0.137). The indirect pathway through health empowerment was statistically significant and accounted for the largest proportion of the total effect (indirect effect = 0.079, 95% CI: 0.059–0.103). The indirect pathway through patient activation was also statistically significant (indirect effect = 0.019, 95% CI: 0.003–0.035), as was the sequential indirect pathway through health empowerment and patient activation (indirect effect = 0.010, 95% CI: 0.006–0.016). Conclusions: In this cross-sectional sample of older adults with hypertension, mHealth literacy was positively associated with hypertension-related KAP. The data were consistent with indirect associations involving health empowerment and patient activation, with the largest indirect association observed through health empowerment. These findings suggest that mHealth literacy and psychosocial self-management resources may both be relevant to hypertension-related KAP. Further longitudinal or intervention studies are needed to clarify the temporal and causal nature of these associations. Full article

The integration of artificial intelligence (AI) tools in higher education raises important questions about how students engage with these technologies and what shapes that engagement. This study examined how AI literacy, academic self-efficacy, and self-regulated resource management strategies are associated with students’ reported AI dependency. Participants were 478 students from Israeli higher education institutions who completed a cross-sectional online survey assessing AI dependency, AI literacy (four subscales), academic self-efficacy, and resource management strategies (time and study management, effort regulation, and help seeking). Multiple regression and K-means cluster analysis were used. The skill-based dimensions of AI literacy were positively associated with AI dependency, whereas AI self-efficacy and academic self-efficacy were both negatively associated, suggesting a unified compensatory self-efficacy mechanism. Effort regulation also predicted lower dependency, while general academic help seeking predicted higher dependency. The model explained 27.3% of the variance in AI dependency. Cluster analysis identified four learner profiles differing in literacy-dependency combinations and in self-regulatory resources. The findings suggest that fostering AI literacy alone is insufficient. Developing students’ self-efficacy beliefs and self-regulated learning practices appears equally important for promoting balanced and intentional AI engagement in higher education. Full article

As generative artificial intelligence (GenAI) increasingly permeates educational contexts, understanding the factors driving learners’ Behavioral Intention (BI) toward GenAI-powered tools has become critical. This study integrates the Technology Acceptance Model (TAM), the Task-Technology Fit (TTF) framework, and privacy and ethical risk considerations to explore the determinants of Chinese higher education students’ Behavioral Intention to adopt these tools. Data were collected from 716 students via a structured self-reported questionnaire. A multi-stage analytical approach was employed by integrating structural equation modeling (SEM) with artificial neural networks (ANN) and support vector regression (SVR). SEM was first utilized to validate the theoretical hypotheses and the measurement model. Subsequently, ANN and SVR models were constructed to explore non-linear relationships and rank the importance of core predictors for Behavioral Intention, including Perceived Ease of Use (PEU), Privacy and Ethical Concerns (PEC), Perceived Technical Features (PTF), and TTF. The modeling performance of the two algorithms was then rigorously compared. The SEM results indicate that PTF exerts an indirect impact on Behavioral Intention via the sequential mediation of Task-Technology Fit and Perceived Usefulness (PU), while PEU positively influences both Perceived Usefulness and Behavioral Intention. Notably, PEC did not exhibit a significant negative effect on users’ Attitude (ATT) or Behavioral Intention. These findings were further elucidated by the machine learning analyses, where PTF and PEU emerged as the dominant predictors, whereas the non-linear contribution of PEC was marginal. Furthermore, SVR outperformed ANN in terms of predictive accuracy and model stability. This study demonstrates the efficacy of combining theoretical modeling with machine learning techniques to elucidate the adoption mechanisms of GenAI in higher education. In addition, preliminary teaching observations in undergraduate mathematics and logistics management courses link quantitative results with actual learning scenarios. We acknowledge that future research should validate these patterns using observed behavioral data. Full article

Diabetes mellitus is a major chronic disease that requires continuous monitoring and timely risk assessment to reduce the likelihood of severe long−term complications. Recent advances in mobile health technologies and machine learning (ML) provide new opportunities for developing intelligent systems that support diabetes self−management and early risk screening. This paper presents an end−to−end mobile health platform that integrates diabetes monitoring functionalities with an ML−based prediction service within a modular client−server architecture. The proposed system enables users to record glucose measurements, insulin injections, physical activity, and other health−related information while providing historical data visualization, automated reminder notifications, and real−time diabetes risk prediction. The prediction module was trained using the publicly available PIMA Indians Diabetes Dataset and evaluated using Decision Tree (DT), Random Forest (RF), and XGBoost classifiers. Model performance was assessed using accuracy, precision, recall, specificity, F1−score, calibration analysis, and the area under the receiver operating characteristic curve (AUC). Experimental results showed that the RF classifier achieved the highest AUC (0.925), demonstrating superior discrimination capability among the evaluated models and making it the most suitable candidate for deployment within the proposed platform. Although the current prediction model was trained on a benchmark public dataset, the proposed framework provides a practical foundation for integrating ML-driven decision support into mobile health applications and can be further extended through external clinical validation and personalized prediction models. Full article

Intelligent customer service is increasingly used to reduce service costs and improve efficiency, yet users often experience divergent outcomes, ranging from value co-creation to value co-destruction. Using task–technology fit theory as the primary theoretical lens, this study examines how the alignment between users’ service task requirements and intelligent customer service capabilities is associated with divergent value outcomes. Within this framework, cognitive load and perceived control are theorized as two localized psychological mechanisms, human–AI trust as a conversion mechanism, and digital self-efficacy as a boundary condition. Using two scenario-based experiments, we develop and test the proposed model. Study 1 shows that participants in the high-fit condition reported higher value co-creation and lower value co-destruction than those in the low-fit condition. The results further reveal an asymmetric mechanism: cognitive load did not directly explain value co-creation but was more strongly associated with value co-destruction, whereas perceived control showed a broader association with both value outcomes. Study 2 extends Study 1 by examining digital self-efficacy as a boundary condition and shows that the conditional indirect association patterns vary across levels of digital self-efficacy. These findings contribute to extending task–technology fit theory to post-use value formation and provide cautious implications for intelligent customer service design and management. Full article

The purpose of this study is to characterize, through an exploratory empirical diagnosis, the degree of development and preliminary association between circular economy capabilities and sustainability-oriented digital transformation capabilities in Chamber-affiliated SMEs in Lambayeque, Peru. Guided by three exploratory working hypotheses, the study expected intermediate levels of development, heterogeneous performance across dimensions, and a positive but non-confirmatory coupling between both capability families. A self-administered questionnaire with thirty Likert-type items measured four circular economy dimensions—circular design and eco-design, resource optimization, circular waste management, and circular business models—and four sustainability-oriented digital transformation dimensions—digital technology infrastructure, dynamic digital capabilities, sustainable digital strategy, and digital innovation culture. The initial database contained 111 complete Chamber-affiliated responses; however, seven large Chamber-affiliated firms were retained only as contextual comparators and were excluded from all statistical processing. Consequently, all descriptive, psychometric, and SEM results were calculated using the final analytical sample of 104 micro-, small-, and medium-sized enterprises. The findings show intermediate development in both constructs, higher perceived performance in digital innovation culture and resource optimization, and lower performance in digital technology infrastructure, reverse logistics, platforms enabling circularity, and monetization of circular models. The latent association between the two higher-order constructs was very high (β = 0.985, p < 0.001); however, because global fit indices were below conventional thresholds, this coefficient is interpreted as preliminary evidence of empirical overlap and capability co-occurrence rather than confirmatory evidence of a validated structural model or causal integration. Full article

Recent advances in wearable, vision-based, trajectory, physiological, and multimodal sensing technologies, together with deep learning, have enabled continuous, objective, and individualized assessment of sport performance and athlete health. Unlike prior reviews that primarily focus on a single sensing modality, sport, or algorithmic series, this review integrates wearable, vision-based, trajectory, physiological, and multimodal sensing streams with deep learning models across both performance analysis and athlete health monitoring, thereby clarifying modality-task-model relationships and translational limitations. This review synthesizes recent progress in sensor-based sports intelligence, focusing on how heterogeneous data streams are transformed into performance- and health-related decision support. The reviewed applications include athlete and ball perception, multi-object tracking, pose estimation, action recognition, trajectory and tactical analysis, training-load and fatigue monitoring, injury-risk prediction, rehabilitation monitoring, and return-to-play support. Deep learning architectures, including CNNs, LSTMs, GRUs, TCNs, Transformers, attention mechanisms, graph neural networks, and multimodal fusion models, are discussed in relation to their suitability for visual, temporal, spatial, physiological, and multisource data. This review further identifies key challenges, including data heterogeneity, annotation scarcity, limited cross-sport and cross-device generalization, real-time deployment constraints, model interpretability, privacy protection, and ethical governance. Moving forward, research efforts should focus on the development of standardized datasets, reliable multimodal data fusion strategies, self-supervised and transfer learning approaches, and deployment on edge or cloud computing platforms. Additionally, enhancing interpretability through explainable AI and implementing closed-loop, individualized monitoring systems are critical. By synthesizing advances in sensing technologies, deep learning methodologies, and real-world applications, this review aims to provide a practical reference for optimizing athletic performance, preventing injuries, guiding rehabilitation, and supporting long-term health management of athletes. Full article

Background/Objectives: Contemporary healthcare systems face compound challenges (including technological acceleration, demographic aging, rising chronic disease burden, and growing patient expectations) that demand models that are simultaneously efficient, high-quality, and person-centered. Despite a substantial body of research addressing organizational design, human capital management, and clinical competencies, these dimensions have largely been theorized in isolation. This study aims to construct and justify an integrated theoretical framework explaining how organizational models, human capital, and transversal competencies may jointly shape care quality, patient safety, and institutional sustainability in healthcare organizations. Methods: A narrative literature review was conducted, integrating contributions from business economics, healthcare management, organizational psychology, and nursing sciences. This design was selected for its suitability in synthesizing heterogeneous, multidisciplinary knowledge into a coherent conceptual framework, a purpose for which systematic meta-analytic approaches are not appropriate. Sources encompassed 79 references: peer-reviewed journals (PubMed, JSTOR, Google Scholar), institutional reports (WHO, OECD, European Commission, Joint Commission), normative standards (ISO 30414:2018), and Italian regulatory frameworks, spanning foundational twentieth-century contributions through the most recent literature (2025). Results: Four principal findings emerged: (1) healthcare organizations are evolving from rigid hierarchical structures toward flexible, value-based configurations, with the Value-Based Healthcare (VBHC) paradigm redirecting institutional attention from service volume to patient-meaningful outcomes per unit of cost; (2) transversal competencies (communication, empathy, emotional intelligence, teamwork, and transformational leadership) are closely associated with care quality and patient safety, with 70–80% of sentinel events associated with communication failures; (3) human capital, encompassing technical expertise and relational capacity, constitutes the primary lever of competitive advantage in healthcare institutions; and (4) the trajectory from pyramidal toward participatory and self-managed models is supported by international evidence, including the Buurtzorg experience in the Netherlands. Conclusions: The integrated three-pillar framework (combining resource-based theory and dynamic capabilities, Value-Based Healthcare, and evolutionary organizational theory) provides a theoretically grounded basis for understanding how organizational structure, human capital, and transversal competencies are jointly associated with clinical performance. Healthcare institutions should systematically integrate soft-skills training into professional education and invest in participatory organizational structures. Health policy should revise financing mechanisms to incentivize patient-meaningful outcomes over service volumes and support the broader transition toward Value-Based Healthcare models. The Italian SSN is discussed as an illustrative national context rather than as the primary empirical focus of the review. Full article

Informal science, technology, engineering, and mathematics (STEM) learning settings can provide early and accessible opportunities for students who have been historically underrepresented in computing to engage with cybersecurity concepts and emerging technologies. This one-group pretest-posttest mixed-methods study examined a week-long, GenAI-integrated informal cybersecurity summer camp for 33 underrepresented and underserved racial and ethnic minority (UUREM) middle school girls. The study investigated pre-post patterns in perceived cybersecurity knowledge, domain-specific self-efficacy, interest, utility value, career aspirations, and selected AI-literacy practices. Quantitative findings showed the strongest support for increases in perceived cybersecurity knowledge and cyber threat identification self-efficacy, both of which remained significant after Holm adjustment for multiple comparisons. Networking and web management self-efficacy showed preliminary unadjusted increases, whereas overall cybersecurity interest, enjoyment and intent to pursue, utility value, career aspirations, and the remaining self-efficacy domains did not show statistically significant differences. Qualitative observations documented activity-specific engagement, collaborative discussion of cybersecurity ideas, and mentor-mediated GenAI practices such as prompt development, output comparison, and script revision. These findings suggest that informal GenAI-integrated cybersecurity programs may support selected aspects of perceived learning and cybersecurity self-efficacy, while highlighting the need for more rigorous designs, validated AI-literacy measures, and longer-term follow-up. Full article