Search Results (1,674)

Background: In an era characterized by rapid technological disruption and vocational uncertainty, Career Adaptability (CA) has emerged as a critical meta-competency for university students transitioning into the workforce. While the importance of CA is well-documented, the internal mechanisms that foster it remain under-explored. This research adopts a resource-based perspective to investigate how Mindfulness—a state of non-judgmental present-moment awareness—acts as a catalyst for career readiness. Specifically, this study examines a dual-mediation model, proposing that Mindfulness enhances Emotional Intelligence (EI) and Psychological Capital (PsyCap) (comprising hope, efficacy, resilience, and optimism), which in turn bolsters an individual’s capacity to adapt to changing career landscapes. By integrating these four constructs, the study provides a comprehensive framework for understanding how “being present” (Mindfulness) translates into “being prepared” (Career Adaptability) through the cultivation of emotional and psychological resources. Methods: The study collected data from 705 final-year students at Wollo University (male = 399 and female = 306). The study employed several well-established instruments: the Compound Psychological Capital Scale (CPC), the Five Facet Mindfulness Questionnaire (FFMQ), the Wong and Law Emotional Intelligence Scale (WLIES), and the Career Adapt-Abilities Scale (CAAS). These instruments were rigorously evaluated for their psychometric applicability within the Ethiopian context. Results: PLS-SEM analysis revealed: (a) direct and positive influences of mindfulness, PsyCap, and EI on career adaptability; (b) partial and positive mediation effects of PsyCap and EI in the mindfulness-career adaptability link; (c) a serial mediation effect of mindfulness through PsyCap and EI; and (d) the proposed model explained a substantial amount of variance in university students’ career adaptability. Conclusions: Despite its strengths, the study acknowledged certain limitations and discussed potential implications for enhancing career adaptability, highlighting the benefits of cultivating mindfulness. Full article

Artificial Emotional Intelligence (AEI) has gained significant attention for enabling machines to recognize and interpret human affective states through modalities such as speech. While deep learning-based speech emotion recognition (SER) models have achieved promising accuracy levels, their practical deployment in resource-constrained IoT edge environments remains insufficiently explored. In particular, there is a lack of systematic evaluation approaches that jointly consider classification performance, computational efficiency, and deployment feasibility under edge-oriented operational constraints. In this study, I address this gap by proposing a deployment-aware evaluation perspective for SER systems operating under IoT edge constraints. Rather than introducing a new model architecture, I focus on establishing a unified and reproducible evaluation framework that reflects practical deployment considerations for edge-based intelligent systems. Within this framework, three widely used deep learning architectures, convolutional neural networks (CNN), long short-term memory (LSTM), and dense neural networks, are systematically analyzed using the EMODB dataset. The experimental results demonstrate that CNN-based models achieve the most consistent classification performance, with peak validation accuracy reaching approximately 84%, while also providing a favorable balance between recognition performance and computational efficiency. To better reflect deployment-oriented evaluation, the study also considers latency-related behavior and computational characteristics relevant to edge computing environments based on benchmark-driven estimations. The findings highlight the importance of deployment-aware evaluation strategies and provide practical insights for selecting suitable model architectures in edge-oriented speech emotion recognition scenarios. This study contributes to bridging the gap between theoretical deep learning performance and practical feasibility considerations in IoT-based intelligent systems. Full article

This study developed and validated a specialised emotional intelligence (EI) scale for youth in the conflict-affected southern border provinces of Thailand. The primary objective was to establish a psychometric instrument tailored to this unique multicultural and sensitive context. Utilizing a sample of 500 local youth leaders, the instrument’s quality was rigorously evaluated through Second-order Confirmatory Factor Analysis (CFA) using Maximum Likelihood estimation. The final validated model comprises 25 indicators categorized into five dimensions: Self-Awareness, Self-Regulation, Self-Motivation, Social Awareness/Empathy, and Relationship Management. Results indicated an excellent model fit with empirical data (χ² = 284.15, df = 265, p = 0.198, CFI = 0.99, GFI = 0.97, RMSEA = 0.02). Factor loadings ranged from 0.72 to 0.92, while composite reliability (CR) and average variance extracted (AVE) values exceeded 0.88 and 0.61, respectively, confirming high internal consistency and construct validity. Social Awareness/Empathy emerged as the most significant dimension (B = 0.91). This study suggests that the scale is a robust tool for assessing EI in conflict zones, providing a critical foundation for targeted psychosocial interventions and sustainable peace-building initiatives among youth in the region. Full article

Driver emotion recognition constitutes a fundamental pillar of intelligent cockpit systems, playing a pivotal role in enhancing driving safety and optimizing human–machine interaction. Despite the integration of vehicle sensor data in recent multimodal approaches, conventional fusion paradigms frequently encounter performance degradation due to the inherent noise and weak semantic correlation between vehicle telemetry and emotional states. To address these challenges, this study introduces a Dimensional Emotion-Guided Multi-task (DEGM) framework, a novel architecture designed to explicitly formalize the asymmetric roles of visual and vehicular modalities. Rather than employing simplistic feature concatenation, the proposed method maps multivariate vehicle data into a continuous Valence–Arousal–Dominance (VAD) space to characterize latent emotional tendencies within specific driving contexts. These predicted dimensions subsequently serve as semantic priors to conditionally modulate global facial representations through a Feature-wise Linear Modulation (FiLM) mechanism, facilitating robust and interpretable cross-modal interaction. Furthermore, the framework adopts a multi-task learning strategy that jointly optimizes discrete emotion classification and continuous dimension regression, leveraging the latter as a structural regularizer to refine the latent feature space. Comprehensive evaluations on the public PPB driving emotion dataset demonstrate that the proposed DEGM achieves a competitive accuracy of 87.50% and a weighted F1-score of 0.8727. The results validate that our framework provides a lightweight and robust paradigm for context-aware affect sensing, demonstrating strong potential for practical deployment in intelligent transportation systems. Full article

Generative artificial intelligence (AI)-based music generation has the potential to create new opportunities for music therapy; however, integrated examinations of generative AI and music therapy remain limited. This paper provides a focused survey of recent studies that apply generative AI within music therapy-related contexts, examining how such approaches have been explored in relation to therapeutic considerations, including emotional and physiological regulation. Rather than offering an exhaustive historical review, we analyze generative AI-augmented music therapy systems from a system-level perspective, focusing on their overall design and implementation. Based on this survey, we discuss open research challenges at the intersection of generative music, adaptive systems, and digital health, and outline future research directions toward scalable and personalized generative AI-based music therapy. Full article

Nowadays, educational software across all learning levels is increasingly enhanced with Artificial Intelligence (AI), primarily through content generation or post-session learning analytics. However, most existing systems remain weakly connected to learners’ real-time affective states and rarely exploit emotional information as a direct control signal for instructional adaptation. In this work, we propose a proof-of-concept closed-loop affect-aware educational adaptation framework that integrates real-time facial emotion recognition into a dynamic learning control system. The proposed approach is built upon a dual-model ensemble architecture, combining a transformer-based model (CAGE) and a CNN-based model (DDAMFN++) trained on large-scale in-the-wild datasets. To bridge heterogeneous emotion representations, we introduce a probabilistic fusion strategy that aligns continuous valence–arousal predictions with discrete emotion classification via a Gaussian Mixture Model (GMM), enabling unified emotion inference in real time. Based on the fused emotional state, a temporal aggregation mechanism is applied to capture sustained affective trends rather than transient expressions. These aggregated signals are then mapped to instructional decisions through an emotion-driven adaptive control policy, which adjusts activity difficulty using an Average Emotion Score (AES). This establishes a fully automated closed-loop adaptation cycle, where detected learner affect directly influences the learning environment without requiring explicit user input or post-session questionnaires. The framework is integrated into an open-source educational platform (eduActiv8) to demonstrate feasibility and system-level behavior. Results from alpha-level validation show that the system can continuously monitor learner affect, generate interpretable emotional analytics, and dynamically adjust task difficulty in real time, while reducing user interaction overhead. This study contributes a modular architecture for affect-aware educational systems by combining real-time ensemble emotion recognition, probabilistic fusion of heterogeneous outputs, and closed-loop instructional adaptation. The proposed framework provides a foundation for future research in scalable, emotion-driven intelligent tutoring and adaptive learning environments. Full article

Oncology nursing is one of healthcare’s most emotionally demanding specialties, characterized by sustained exposure to patient suffering and mortality. While global burnout rates reach 40–60%, emotional intelligence (EI) is a potential protective resource that remains underexamined in Middle Eastern contexts. Despite growing global evidence, little is known about these relationships in Middle Eastern healthcare systems, where cultural norms and workforce structures may shape emotional processes differently. This study examined whether EI was significantly associated with lower burnout across personal, work-related, and client-related dimensions among oncology nurses in Saudi Arabia. Methods: A cross-sectional correlational study enrolled 172 oncology nurses from three tertiary hospitals in Riyadh. Participants completed validated Arabic versions of the Schutte Self-Report Emotional Intelligence Test (SSEIT) and the Copenhagen Burnout Inventory (CBI). Hierarchical regression analyses examined predictive relationships while controlling for age and experience. Results: EI demonstrated significant inverse correlations with personal (r = −0.41), work-related (r = −0.38), and client-related burnout (r = −0.33, p < 0.001). In hierarchical models, EI emerged as a significant predictor of lower scores across all dimensions, explaining 11–17% of unique variance beyond demographic factors. The strongest association was with personal burnout. Causality cannot be inferred from this cross-sectional design. Conclusion: EI functions as a significant protective factor against burnout. Healthcare organizations should integrate EI development into professional training to strengthen workforce resilience and sustain care quality. Full article

The COVID-19 pandemic significantly increased adolescent digital engagement, but whether the rise in cyberbullying persists beyond the crisis is not well understood, especially in Southeast Asia. This study examined social media addiction, perceived stress, emotional intelligence, and cyberbullying among 416 Thai secondary students (grades 7–12) during the pandemic-to-endemic transition (June–October 2023). Participants completed validated Thai-language instruments assessing cyberbullying, social media addiction, perceived stress, and emotional intelligence. Results showed 66.4% of adolescents were involved in cyberbullying, with 32.2% as bully-victims. Social media addiction correlated with cyberbullying perpetration (r_s = 0.33, p < 0.001) and victimization (r_s = 0.22, p < 0.001), as did perceived stress (r_s = 0.20 and 0.29; p < 0.001). Emotional intelligence showed negative correlations with cyberbullying perpetration (r_s = −0.15, p = 0.002) and victimization (r_s = −0.10, p = 0.048). Over one-third (34.4%) were at high risk for social media addiction. These findings indicate that during the pandemic-to-endemic transition, Thai adolescents showed elevated cyberbullying involvement, high social media addiction, and moderate-to-high stress—a profile consistent with sustained digital risk. These results highlight the need for integrated interventions that address digital wellness, stress management, and the development of emotional intelligence among Thai adolescents. Full article

This study investigates how two key characteristics of AI-enabled chatbots in mobile banking applications—perceived intelligence and perceived anthropomorphism—influence users’ cognitive and hedonic evaluations, namely perceived usefulness, confirmation, and perceived enjoyment, and how these evaluations subsequently shape user satisfaction and continuance intention. Grounded in the Expectation–Confirmation Model (ECM), the study also examines the moderating role of users’ need for interaction with service employees in these relationships. Using a quantitative research design, data were collected through a structured survey from 402 users of AI-enabled mobile banking applications in Türkiye. The proposed model was tested using partial least squares structural equation modeling (PLS-SEM), and moderated mediation effects were analyzed using Hayes’ PROCESS Macro (Model 58). The results reveal that perceived intelligence positively affects perceived anthropomorphism, perceived usefulness, perceived enjoyment, and confirmation, while perceived anthropomorphism further reinforces these effects. Cognitive and emotional evaluations significantly enhance user satisfaction, which in turn strongly predicts continuance intention toward chatbot usage. Moreover, the need for interaction with service employees significantly moderates the indirect effects of perceived usefulness, perceived enjoyment, and confirmation on satisfaction and continuance intention. By extending the expectation–confirmation model with both cognitive and emotional dimensions, this study offers novel insights into user-centered chatbot design in mobile banking and highlights the importance of individual differences in shaping sustained technology use. Full article

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, compounded by difficulties in emotional regulation, which have sparked growing interest due to their relationship with emotional intelligence (EI). Background/Objectives: The objective of this study was to analyze the effectiveness and characteristics of interventions aimed at developing EI in people diagnosed with ADHD. Methods: A systematic review was conducted following PRISMA 2020 in the Web of Science, Scopus, PubMed, Dialnet, ERIC, and SpringerLink databases. After applying inclusion and exclusion criteria and evaluating methodological quality, 31 studies were selected. Results: The evidence shows that children and adolescents with ADHD have lower levels of EI than the typically developing population, especially in emotional regulation, stress management, adaptability, and interpersonal skills. Interventions focused on emotional training have demonstrated improvements in emotional competencies, self-control, ADHD symptoms, and social functioning. However, variations are observed according to age, clinical subtype, the presence of comorbidities, and the type of informant, as well as heterogeneity in the assessment instruments used. Conclusions: Strengthening EI emerges as a promising complementary strategy for improving the emotional and social adaptation of people with ADHD. It is recommended to move toward longitudinal studies and more personalized interventions tailored to the clinical and developmental characteristics of the disorder. Full article

Generative artificial intelligence (AI) is increasingly integrated into sustainability-oriented entrepreneurial practices, raising important questions about its role in shaping human creativity and innovation. This qualitative study examines how postgraduate social entrepreneurship students engage with generative AI during the creativity phase of sustainable startup development. Drawing on Amabile’s componential theory of creativity, this study explores how AI is perceived to relate to domain-relevant skills, creativity-relevant processes, task motivation, and social–contextual factors. Data were collected through an AI-assisted ideation task, followed by semi-structured interviews, and analyzed using reflexive thematic analysis. The findings reveal that generative AI was perceived as supporting information access and associative thinking, while being unable to replicate human intuition and the “aha” moment associated with deep creativity. Moreover, AI was perceived to have limited influence on intrinsic motivation, which remains driven by personal values and contextual responsibility. Socially, AI was consistently described as a tool rather than a teammate, with emotional responses regarded as superficial. The study further suggests that AI may be understood as a social–contextual condition and highlights a perceived trade-off between efficiency and creativity in AI-assisted ideation. These insights extend the application of creativity theory to AI-supported sustainability contexts and offer practical implications for fostering responsible, human-centered innovation in entrepreneurship education. Full article

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article

This study examines the impact of explainable artificial intelligence (XAI) features on user satisfaction and purchase intention in Saudi mobile shopping applications, utilising the stimulus–organism–response (S–O–R) framework. With the increasing reliance on AI-driven decision support in e-commerce, enhancing transparency, fairness, trustworthiness, and interpretability has become crucial for shaping consumer perceptions and behavioural responses. The research employed a quantitative methodology using partial least squares structural equation modelling (PLS-SEM) to examine the relationships among stimulus factors, cognitive and affective states, consumer satisfaction, and purchase intention. In a survey of 597 respondents from Jeddah and Makkah, Saudi Arabia, the findings highlight that fairness and bias detection, trustworthiness, and transparency significantly influence consumers’ cognitive and affective states, which in turn enhance satisfaction and intention to purchase. Consumer satisfaction emerged as a critical mediator, reinforcing the role of positive emotional and cognitive experiences in driving purchase behaviours. However, interpretability showed limited impact, suggesting that consumers may prioritise fairness and trustworthiness over technical clarity of explanations. Theoretically, this study contributes to advancing knowledge on the role of XAI in consumer behaviour by integrating fairness, transparency, and affective responses into the S–O–R paradigm. From a managerial perspective, the results underscore the importance for mobile shopping platforms to design AI systems that foster trust, reduce perceived bias, and ensure transparency, thereby improving consumer engagement and purchase outcomes. By addressing gaps in interpretability and transparency, businesses can strengthen user trust and loyalty, ultimately enhancing competitive advantage in Saudi Arabia’s rapidly growing e-commerce sector. Full article

Artificial intelligence (AI) is increasingly embedded in digital marketing, enabling organisations to personalise communication, analyse consumer data, and optimise decision-making processes. Despite its widespread adoption, limited empirical research has examined whether AI-driven digital marketing contributes to responsible consumption and production, as articulated in Sustainable Development Goal 12 (SDG 12). Grounded in a capability-based and marketing intelligence framework, this study investigates the mechanisms through which AI-driven digital marketing influences responsible marketing outcomes. Using survey data from 120 professionals in multinational corporations (MNCs) operating in South Africa, the study examines how AI-driven digital marketing influences responsible marketing outcomes aligned with Sustainable Development Goal 12 (SDG 12), with particular emphasis on the mediating roles of predictive consumer analytics and sentiment-based consumer understanding as distinct dimensions of AI-enabled marketing intelligence. Instead, its influence operates indirectly through sentiment-based consumer understanding, while predictive consumer analytics show no significant effect. These results suggest that AI contributes to responsible consumption primarily when it enhances firms’ capacity to interpret consumer values, emotions, and ethical concerns. The study advances the digital marketing and sustainability literature by reframing AI as a relational and sense-making capability while offering practical guidance for aligning AI-driven marketing strategies with SDG 12 in emerging markets. Full article

This study examines how integrating Creative Problem Solving (CPS) and generative artificial intelligence (GenAI) within animation storytelling education can foster sustainability-related competencies in higher education. A twelve-week mixed-methods action research design was implemented in a “Storytelling and Scriptwriting” course at a university of technology in northern Taiwan (N = 60). The intervention design combined a CPS-aligned instructional sequence, six scaffolded assignments (including a text-to-image resemiotization task), pre–post CPS cognition and affect scales, CPS-dimensioned assignment self-assessments, reflective journals, and expert evaluations of final story prototypes using the Consensual Assessment Technique. Quantitative results showed significant gains in students’ CPS-related narrative cognition and affective resilience (p < 0.001), as well as consistently high self-reported engagement across CPS dimensions for all assignments, particularly for the text-to-image and personal narrative tasks. Expert ratings indicated high levels of originality, narrative coherence, emotional impact, and social relevance in final prototypes, while qualitative data highlighted reduced “blank page” anxiety, greater willingness to revise, and more collaborative, systems-oriented narrative reasoning. The findings suggest that a CPS- and GenAI-supported teaching model can function as a cognitive bridge for heterogeneous cohorts, positioning GenAI as a conditional amplifier embedded within a reflective CPS framework and helping to translate abstract sustainability-related competencies—such as anticipatory, normative, strategic, and interpersonal competencies—into concrete creative media practices. Full article