Search Results (455)

Selecting the appropriate academic track is a critical decision for students, as misalignment between program requirements and individual cognitive, personality, and competency profiles can significantly impact academic performance, persistence, and overall educational outcomes. Traditional educational recommender systems often rely solely on skill matching or on the correlation of interests, failing to account for the dimension of competency that is required for success in specific academic tracks. This paper introduces a novel Multi-Dimensional Psychometric Alignment (MDPA) algorithm that moves beyond simple rank-order correlation between skills and programs by jointly integrating multiple psychometric perspectives and evaluating both preference similarity and competency sufficiency. Based on a structured synthesis of Cognitive Preferences (MBTI), Cognitive Modalities (Gardner’s Multiple Intelligences), and Personality Stability (Big Five), the proposed profile captures complementary dimensions of student readiness that are usually examined separately in prior educational recommender systems. Then applies an alignment algorithm-which is based on a hybrid similarity metric that fuses Spearman’s Rank Correlation (Interest Shape) with Weighted Euclidean Distance (Competency Magnitude), enforced by non-linear threshold penalties for critical traits- in order to find the best options for students. This approach constitutes a deterministic, explainable recommender system whose novelty lies in combining heterogeneous psychometric evidence with an explicit magnitude–shape matching mechanism and threshold-based academic viability constraints. Our approach is validated through a case study of university students in Kazakhstan, and the results demonstrate how “academic fit” is better modeled as a function of both interest pattern and trait sufficiency, offering a robust alternative to “black-box” skill-based recommenders. Full article

This study aimed to develop and internally validate a predictive model of perceived stress among first-year military male students to examine the predictive contribution of personality traits, depressive symptoms, and psychological well-being. Understanding these psychological predictors may support interventions for students at elevated risk of stress during military and academic transition. A cross-sectional web-based survey included 274 first-year male students at the Military Technological College in Oman. Outcome measures included the Perceived Stress Scale (PSS-10), the Patient Health Questionnaire (PHQ-9) for depressive symptoms, the WHO-5 Well-being Index, and the Big Five Inventory assessing personality traits. All variables were analyzed as continuous measures. Predictive modeling was performed using Least Absolute Shrinkage and Selection Operator (LASSO) linear regression with repeated 70/30 train–test splitting across 100 iterations and 10-fold cross-validation for internal validation. The final analytic sample included 266 participants after exclusion of incomplete responses. Across the 100 internal validation runs, the LASSO model accounted for approximately 40% of the variance in perceived stress (training R² = 0.44 ± 0.04; test R² = 0.40 ± 0.08). Neuroticism (β = 0.35) and depressive symptoms (β = 0.15) showed positive associations with perceived stress, whereas psychological well-being showed a negative association (β = −0.32). PHQ-9, WHO-5, and neuroticism were selected in 100% of the repeated LASSO models, which showed the most stable predictive contribution. Model performance on the test datasets showed stable predictive accuracy (MSE = 20.24 ± 2.48; RMSE = 4.49 ± 0.28; MAE = 3.61 ± 0.23). These findings demonstrate that personality traits, depressive symptoms, and psychological well-being collectively contribute to the statistical modeling of perceived stress among military students. The internally validated associative model may support institutional interventions for students vulnerable to elevated stress, informing targeted preventive mental health strategies within military training environments. Full article

Background: Decision-making is often a shared and collective process, facilitated by negotiation dynamics. This study adopted fNIRS hyperscanning to explore similar brain hemodynamic responses during naturalistic negotiation and decision-making interactions and the role of individual differences. Methods: Homologous dyads of speaker A and speaker B engaged in a realistic negotiation task, deciding how to handle a non-conforming team member. The task included three steps: the Individual step (Indstep), where the participants individually selected how to decide; the Cooperation step (Coopstep), involving collaborative negotiation; and the Agreement step (Agrstep), where a mutual agreement was reached. General Decision-Making Style (GDMS), Maximization Scale (MS) and Big Five Inventory (BFI-10) were also administered. Results: Higher deoxygenated hemoglobin (HHb) dissimilarity emerged when speaker B was speaking and speaker A was listening, suggesting that the members exhibited differences in the level of cognitive demand required for the conversation, or that speaker A was attempting to assert his perspective. Moreover, the avoidant decision-making style, the alternative-search tendency, and the decision-making difficulties subscales negatively correlated with HHb dissimilarity in the left hemisphere during the Agrstep, as well as the extraversion trait during the Indstep and the Coopstep, highlighting how individual differences modulate the neural mechanisms underlying negotiation. Conclusions: These findings reveal that brain patterns of neural similarity in negotiation contexts are sensitive to both conversational roles and individual decision-making profiles. Integrating a hyperscanning paradigm with psychological assessment offers interesting insights into the neurocognitive foundations of cooperation, revealing the influence of cognitive and personality traits on neural activity associated with the naturalistic negotiation process. Full article

Background/Objectives: Clinical empathy is a core competency in nursing education and is conceptually relevant to person-centered nursing care. However, limited evidence is available on how clinical empathy in advanced nursing students is associated with dispositional characteristics such as personality traits and resilience. This study aimed to examine cross-sectional associations between clinical empathy, Big Five personality traits, and resilience in third- and fourth-year nursing students. Methods: A descriptive, cross-sectional secondary analysis was conducted using an existing survey database. The final analytic sample comprised 66 third- and fourth-year nursing students from a nursing school in Spain. Clinical empathy was assessed with the Jefferson Scale of Empathy, resilience with the 6-item Brief Resilience Scale, and personality traits with the Big Five Inventory-44. Life satisfaction, academic engagement, and general self-efficacy were included as secondary psychosocial variables. Descriptive analyses, correlation analyses, group comparisons, and exploratory multiple linear regression were performed. Results: Higher agreeableness was associated with higher total clinical empathy (ρ = 0.390, p = 0.001) and perspective-taking (ρ = 0.440, p < 0.001). Higher conscientiousness was also associated with higher total clinical empathy (ρ = 0.480, p < 0.001), perspective-taking (ρ = 0.432, p < 0.001), and compassionate care (ρ = 0.324, p = 0.008). In the exploratory multivariable cross-sectional model, agreeableness and conscientiousness were independently associated with total clinical empathy, whereas resilience was not. Findings involving the Standing in the Patient’s Shoes subscale should be interpreted cautiously because of its low internal consistency. Conclusions: In this exploratory sample of advanced nursing students, self-reported clinical empathy was associated mainly with agreeableness and conscientiousness. These findings should be interpreted as cross-sectional associations based on self-report data and should not be taken as evidence of causal effects, ethical behavior, or person-centered care practices. Further longitudinal and multicenter studies are needed to examine whether these associations are stable and whether they relate to observable educational or clinical outcomes. Full article

(1) Background: Problematic social media use has increasingly been conceptualized as a non-clinical addictive-like behavior characterized by impaired control and negative functional consequences. Despite the rapid growth of TikTok and its algorithm-driven content delivery, the contribution of individual psychological factors and users’ awareness of algorithmic processes to addictive symptoms remains insufficiently understood, particularly in Latin American contexts. This study examined the associations between personality traits, algorithmic awareness, and addictive symptoms of TikTok use among university students. (2) Methods: A quantitative, cross-sectional design was conducted with a convenience sample of 238 university students from Ecuador. Participants completed self-report measures of social media addiction, algorithmic media content awareness, and Big Five personality traits. Spearman correlations and hierarchical multiple regression analyses were performed, controlling for age and sex. (3) Results: Algorithmic awareness dimensions were not significant predictors of addictive symptoms. Demographic variables explained minimal variance, whereas personality traits accounted for the largest increase in explained variance in the final model. Neuroticism and Extraversion were positively associated with addictive symptoms, while Conscientiousness and Openness to Experience were negatively associated. (4) Conclusions: Personality traits were more informative than algorithmic awareness in explaining addictive-like TikTok use among university students, underscoring the relevance of self-regulatory and affective dispositions for prevention and intervention strategies. Full article

Background/Objectives: The purpose of the present study was to assess the relationships between attention level and personality traits among 44 older adults aged from 65 to 75 in the Israeli population. The participants were 19 females and 25 males, with a mean age of 68.13 years, and 12.32 years average of formal education, who provided clinical history for screening. Methods: The participants completed the 44-item Big Five Inventory, the d2 Test of Attention, and the Mathematics Continuous Performance Test (MATH-CPT), a computerized test that measures participants’ attention levels by recording their responses to visual stimuli. Results: The results showed significant correlations between attention and the domains of Conscientiousness and Neuroticism. Linear regression analysis, with the MATH-CPT measures as the dependent variable and the Big Five personality factors as independent variables, revealed a significant model explaining 41.60% of the variance in MATH-CPT performance. Linear regression analysis of the measures of the d2 test of attention as dependent variables and the Big Five factors as independent variables showed a significant model that explained 47.70% of the variance. No association was found between age and the domains of the Big Five personality traits. Conclusions: This study discusses the implications of the significant correlations between personality domains and measures of attention in older adults. The findings suggest that individuals who are organized, task-oriented, goal-focused, responsible, imaginative, creative, and interested in educational experiences may be better able to perform attention-related tasks in older age. Full article

Autonomous UAVs for urban traffic monitoring must respond quickly to changing operational conditions while maintaining stable, transparent decision-making. Rule-based controllers respond only at predefined thresholds, while learning-based methods adapt well but lack the certification transparency required for safety-critical deployment. This paper proposes a bio-inspired emotion-regulated decision-control mechanism and introduces the Pull–Push Engine (PPE), a regulatory architecture that balances environmental stimuli against personality-anchored baselines through weighted temporal integration. The PPE is embedded in a three-layer framework combining Big Five personality traits, the Pleasure–Arousal–Dominance (PAD) model, and Ortony–Clore–Collins (OCC) event appraisal. Validation in a SUMO-based simulation across three scenarios of increasing complexity showed that PPE regulation maintained bounded PAD trajectories and zero saturation despite concurrent stressors, whereas removing the pull term caused 57–88% saturation. Behavioral diversity scaled naturally with operational demands: Surprised mood dominated across all scenarios (47.8–67.5%), with Anxious and Focused increasing systematically with complexity. Strategy entropy rose monotonically (1.885–2.033 bits). A sensitivity sweep confirmed robust regulation across a stable operating region, with degradation only at the boundary (p < 0.001 for all key comparisons). Every simulated decision remains causally traceable from stimulus through emotional processing to action. This ensures interpretability, which is essential for future safety-critical UAV deployment, although hardware implementation and field validation are still required. Full article

People have become accustomed to studying or working with the guidance of artificial intelligence (AI) in recent years. Studies have begun investigating the risk factors of AI dependence, though most have used hypothesis-testing methods. The present study aimed to investigate predictors of AI dependence using machine-learning and network analysis, which are data-driven approaches. The included risk factors were Big Five personality traits, self-efficacy, depression, social anxiety, adverse childhood experiences, and AI use motivation, selected based on theories and empirical studies. Participants consisted of 1258 university students (942 females and 316 males) with a mean age of 22.11 years (SD = 2.69). Four machine-learning algorithms were tested, including Elastic Net, Random Forest, XGBoost, and LightGBM. Machine-learning results indicate that escape and social motivation for AI use, along with social anxiety, were the main predictors of AI dependence. Network analysis results show that escape and social motivation were the most central nodes, with the highest Expected Influence (EI) indices. This study indicates that when addressing mental health problems related to AI dependence, it is more effective to focus on emotional isolation and social interaction challenges rather than simply cutting down on AI use. Full article

During the COVID-19 pandemic, working from home became widespread, substantially altering work arrangements. This study investigates whether employees working predominantly from home report higher job satisfaction than those working primarily in traditional office settings. In addition, it examines which contextual factors in the home office environment are associated with job satisfaction. A cross-sectional online survey was conducted among 201 employees in Germany. Job satisfaction was measured using the Arbeitsbeschreibungsbogen (ABB), and personality traits were assessed with the Big Five Inventory (BFI-10) as a control variable. Group differences were analyzed using independent-samples t-tests and analysis of covariance (ANCOVA). Results indicate that employees working predominantly from home reported significantly higher overall job satisfaction than those working mainly in traditional office settings (F(1, 193) = 69.79, p < 0.001, partial η² = 0.27). This effect remained stable after controlling for personality traits and age and was evident across all job satisfaction subdimensions. Furthermore, effective communication tools, adequate technical equipment, a quiet workspace, and prior experience with working from home were positively associated with job satisfaction. The presence of children or other household co-workers did not significantly reduce job satisfaction, whereas sufficient childcare arrangements showed a strong positive association. Women working from home reported lower job satisfaction than men. Overall, the findings highlight the importance of supportive home office conditions for sustaining job satisfaction beyond the pandemic. Full article

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures. Full article

The purpose of this study was to examine the multivariate relationship between college students’ Big Five personality traits and their perceptions of generative artificial intelligence (AI). Guided by sustainable digital education and expectancy-value theory, this study investigated whether personality profiles were associated with students’ knowledge of AI, attainment value, intrinsic value, utility value, perceived cost, and intention to use AI. Using a cross-sectional survey design, data were collected from 375 students enrolled at a Southwestern doctoral-granting public university. Participants completed an adapted measure of generative AI perceptions and the Big Five Inventory, and canonical correlation analysis (CCA) was conducted to examine the multivariate relationship between the two variable sets. The results indicated that the full canonical model was statistically significant and that three interpretable canonical functions were retained. The first and strongest function showed that higher openness, agreeableness, and conscientiousness were associated primarily with greater AI knowledge and, to a lesser extent, with higher perceived cost. The second function indicated that higher neuroticism was associated with greater perceived cost and lower utility and attainment value. The third function showed that lower neuroticism, together with higher openness and conscientiousness, was associated with a stronger attainment value, greater intention to use AI, and lower perceived cost. Our findings suggest that students differ meaningfully in how they understand and value generative AI. These results have important implications for higher education because they highlight the potential value of differentiated, human-centered AI literacy efforts in supporting more equitable and responsible AI integration. Full article

Candidate interview assessment is primarily reliant on subjective human judgment, while existing AI-based methods rely on end-to-end predictions with no psychometric basis. In this paper, we propose an interpretable multi-modal framework that combines nonverbal behavior, LLM-based verbal analysis, and Big Five personality traits into three theory-based constructs: professional-cognitive competence, observed leadership behavior, and leadership disposition. The proposed method utilizes computer vision and larger language models to extract features from video interviews. Rather than targeting predictive accuracy, the proposed method prioritizes construct validity and transparent aggregation under severe label scarcity. The proposed method aggregates the constructs into a Top Potential Score that reflects the executive abilities of the candidate. Experiments on the method show its ability to significantly differentiate top candidates from others (Cliff’s delta = 0.91 for the composite Top Potential Score, permutation p = 0.0002). Leave-one-out analysis verifies robustness, while rank-based evaluation yields 100% recall of executive candidates in the top 20% of rated applications. The findings justify the use of the proposed multi-modal method as an interpretable decision-support tool for candidate interview assessment. Full article

Existing research confirms associations between Big Five personality traits and emotional states, yet investigations into how personality traits modulate emotional dynamics and their gender-specific patterns remain limited. The present study developed a TFace-Bi-GRU-SE deep learning model that achieved a weighted accuracy of 63.50 ± 0.98% (peak single-run: 64.96%) and an F1 score of 65.21% in performance testing, with a single-inference time of 14.1 s, outperforming traditional methods. The model processed 10 min video recordings from 30 participants (19,262 observations), generating time-series data for valence (P) and arousal (A). Combined with Big Five personality assessments, continuous-time structural equation modeling (CTSEM) revealed distinct emotional dynamics: both P and A exhibited significant negative autoregression (−0.056 and −0.558, p < 0.001), with A reverting to baseline substantially faster (half-life: 1.2 s) than P (half-life: 12.3 s); cross-lagged effects were nonsignificant (P_A: 0.007; A_P: −0.026, p > 0.05). Arousal demonstrated greater instantaneous volatility (=0.339) than valence (=0.286, p < 0.001), with positive covariation between dimensions (0.218, p = 0.006). Exploratory analyses (N = 30) indicated that higher neuroticism and openness scores were associated with elevated arousal (Cohen’s d > 0.8), whereas higher agreeableness and conscientiousness scores were associated with elevated valence (d > 0.8). Gender moderated the neuroticism–arousal relationship, with more potent effects in females (r = 0.746, p = 0.008). Robustness analyses demonstrated high stability of core DRIFT parameters (P_P, A_A): bootstrap resampling (n = 50) yielded coefficients of variation < 0.35 with 100% directional consistency; subgroup validation confirmed cross-sample invariance. Sensitivity analyses revealed that an additional 8% measurement error induced less than 9% bias (8.3% for both P_P and A_A) in autoregressive parameters while preserving half-life ratios, confirming CTSEM’s capacity to extract reliable dynamics from moderately accurate AI outputs. Bootstrap and Bayesian analyses identified ten personality–DRIFT associations with directional consistency ≥ 70%; these constitute preliminary hypotheses for adequately powered future studies (N ≥ 61). This study provides methodological foundations for personalized affective intervention research. Data and code are publicly available (see Data Availability Statement). Full article

Background: This study examined age-related differences and interrelationships among psychological symptoms, personality traits, and emotional expression styles in a community sample of 151 participants aged 10–77 years, spanning four age groups: adolescents, young adults, middle-aged adults, and older adults. Methods: Psychological symptoms were assessed using the SCL-90, personality traits using the Big Five Inventory-2 (BFI-2), and emotional expression patterns were derived from facial expression recognition via a convolutional neural network (CNN) model. Kruskal–Wallis H tests were used to examine age-related differences. K-means cluster analysis was applied to identify emotional expression patterns, and logistic regression was used to construct a mental health risk screening model. Results: The young adult group (19–35 years) achieved the highest scores on the depression (M = 1.73) and anxiety (M = 1.61) dimensions, indicating a higher level of psychological distress during this life stage. Personality traits showed a significant developmental trajectory: neuroticism decreased with age (H(3) = 17.09, p < 0.001, η² = 0.11), declining from 2.69 in the young adult group to 2.17 in the older adult group; conscientiousness increased with age (H(3) = 37.39, p < 0.001, η² = 0.24), representing the most substantial age-related effect. K-means clustering identified three distinct emotional expression patterns: Cluster 1 was characterised by happiness, Cluster 2 by anger, disgust, and fear, and Cluster 3 by neutrality, sadness, and surprise. Cluster 2 exhibited the highest scores on neuroticism, anxiety, depression, and mood swings, and scored significantly higher than the other two clusters on interpersonal sensitivity, depression, anxiety, and hostility (p < 0.05). Mental health risk screening indicated that 26.5% of participants were classified as high-risk. Logistic regression analysis (AUC = 0.742) showed that neuroticism was the strongest predictor of elevated mental health risk (OR = 4.58), while extraversion (OR = 0.41) and conscientiousness (OR = 0.57) were significant protective factors. Conclusions: These findings provide exploratory evidence regarding age-related patterns of psychological symptoms and personality traits in a convenience sample and offer preliminary support for personality-based mental health risk screening. Notably, the SCL-90 was employed as a screening tool rather than for clinical diagnosis. Given the unequal age group sizes, particularly the small young adult subgroup, generalisability across the lifespan should not be assumed. Full article

Research on financial risk tolerance and risk-taking increasingly incorporates personality traits into predictive and descriptive models of risk-taking behavior; however, intercorrelations among traits can obscure the unique contributions of individual traits. This is known as the suppressor effect. This study employed a two-stage analytic framework to test and adjust for suppressor effects across the Big Five personality dimensions in describing financial risk tolerance. In Stage 1, correlation and OLS regression analyses identified suppression patterns, revealing that the explanatory validity of some factors was distorted by shared variance. In Stage 2, suppression-adjusted trait estimates were used to reassess their unique association with financial risk-taking mediated through financial risk tolerance. Results indicate that Openness to Experience and Extraversion are the strongest descriptors of financial risk-taking once suppressor effects are controlled. At the same time, Agreeableness and Conscientiousness contribute modestly and context-dependently to descriptions of financial risk-taking. These findings demonstrate that ignoring suppression effects can lead to mischaracterizing the role of personality in financial decision-making. This study shows that more precise estimates of trait influences can improve theoretical models of investor behavior and enhance the delivery of financial advice and education. Full article