The impact of personality traits and AI literacy on the adoption intentions of AI among design faculty in Chinese higher education.

AI-driven mixed-methods analysis of technology dependence: Personality-moderated pathways to Oral English anxiety in language learning.

Background: The aim of the study presented in this article was to conduct an analysis that would answer the research question: do personality traits such as extraversion, neuroticism, openness to experience, agreeableness, conscientiousness, intelligence level, and ability to cope with stressful situations correlate with the assessment of judo fights? Materials and Methods: Ninety individuals participated in the study, including international-class referees, national-class referees, coaches, and active judo athletes. The study used a correlational analysis approach to examine the relationships between personality traits and fight assessment criteria in judo. Results: The results indicate that in the referees' group, there is a correlation between neuroticism (NEU) and the evaluation of shido actions (p = 0.024), hansoku-make (p = 0.019), and conscientiousness (p = 0.004) at the significance level of p < 0.050. For coaches, significant correlations were found between neuroticism and the wazari action (p = 0.031), neuroticism and the ippon action (p = 0.010), agreeableness and hansoku-make (HSM) (p = 0.047), as well as conscientiousness and ippon (p = 0.036) at the significance level of p < 0.050. In the judo athletes' group, a significant correlation was found between neuroticism and the shido action (p = 0.006), as well as extraversion with p = 0.011 at the significance level of p < 0.050. Conclusions: It can be stated that personality traits such as extraversion and conscientiousness positively affect the assessment of fights in judo, while neuroticism may have a negative impact. Both referees, coaches, and athletes prefer coping strategies focused on the task, which may be essential for control and effectiveness in their field.

This study employs Mendelian Randomization (MR) to explore the causal relationships between Metformin and 20 mental illnesses. The aim is to provide new pharmacological treatment bases for the treatment and intervention of mental illnesses, thereby reducing incidence rates and alleviating the disease burden. This study uses summarized data from Genome Wide Association Studies (GWAS) to identify genetic instrumental variables (IVs) that are significantly associated with Metformin and are mutually independent. The primary method used to evaluate causal relationships is the Inverse Variance Weighted (IVW) approach, complemented by other MR methods for sensitivity analysis. MR analysis results indicate a significant negative causal relationship between genetically predicted Metformin and Neuroticism(NEU) (OR = 0.700, 95% CI: 0.505-0.970, P = 0.032) and Bipolar Disorder(BID) (OR = 0.0374, 95% CI: 0.00266-0.525, P = 0.015). Additionally, a significant positive causal relationship was found with Attention deficit hyperactivity disorder(ADHD) (OR = 15.4, 95% CI: 1.95-122, P = 0.010) and Insomnia(INS) (OR = 1.96E + 06, 95% CI: 351-1.10E + 10, P = 0.001). There were no significant causal relationships with the remaining mental illnesses (P>0.05). Sensitivity analyses indicate that the results are robust. From a genetic perspective, this study finds that Metformin may reduce the risk of NEU and BID while increasing the risk of ADHD and INS. These findings not only provide theoretical support for further research into the etiological mechanisms but also offer valuable reference points for the clinical use of Metformin in treating NEU and BID, and for exercising caution in its use among patients with ADHD and INS.

PurposeThis study investigates how the Big Five (BF) personality traits influence green consumerism (GCM), focusing on the extent to which each trait predicts eco-friendly behaviors. It also explores the relative importance of these traits using both partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN).Design/methodology/approachThis study uses a quantitative approach, surveying 689 respondents through a structured questionnaire. ANN was utilized to complement PLS-SEM and to validate the significance of antecedents identified via PLS-SEM, thereby improving the robustness and practical relevance of the findings.FindingsResults from both PLS-SEM and ANN revealed that extraversion (EXT) was the most significant predictor of GCM, followed by conscientiousness (CON), agreeableness, openness to experience and neuroticism (NEU). While EXT had the greatest influence, NEU negatively impacted GCM.Practical implicationsMarketers can tailor green campaigns by targeting individuals with high EXT and CON, emphasizing the social and ethical dimensions of green products.Social implicationsUnderstanding personality-based drivers of green behavior helps promote sustainable consumption patterns, contributing to environmental protection and social responsibility.Originality/valueThis research contributes by integrating PLS-SEM and ANN, offering a novel approach to understanding the influence of BF personality traits on GCM.

With the rapid advancement of artificial intelligence (AI) technologies, AI has evolved from an exploratory technology into a critical component of the design process. This study develops an extended model utilizing the Big Five Personality (BFP) framework, the Technology Acceptance Model (TAM), and Perceived Risk Theory (PRT) to examine the influence of BFP traits on Generation Z designers’ willingness to subscribe to paid AI drawing tools. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were employed to analyze data from 477 valid responses. The SEM results demonstrate that Openness (OPE) and Extraversion (EXT) positively affect Perceived Ease of Use (PEU) and Willingness to Pay for Subscription (WPS), while negatively influencing Perceived Risk (PR). EXT and Agreeableness (AGR) enhance Perceived Usefulness (PU). Neuroticism (NEU) adversely affects PU, PEU, and WPS but enhances PR. Moreover, WPS is positively affected by PEU and PU, yet negatively by PR, with PEU, PU, and PR acting as mediators in certain paths. The fsQCA findings exhibit the complex interplay of BFP traits, revealing four configurations that influence WPS among Generation Z designers, distinctly contrasting with the SEM results. This research systematically explores for the first time the impact of personality traits on technology acceptance behaviors among Generation Z designers, contributing theoretical developments and fresh insights into personality psychology and technology acceptance studies. It also offers practical implications for technology development, marketing, education, and human resource management.

Neuroticism (NT) is a fundamental personality trait and a major risk factor for both the onset and persistence of depression and anxiety disorders. Although NT involves alterations in emotion–cognition interaction, its precise neural mechanism remains insufficiently understood. Leveraging the word-face Stroop task, we examined neural circuits engaged during emotional conflict using a relatively large sample that exhibited a wide range of NT levels. Generalized psychophysiological interaction (gPPI) analyses revealed that individuals with high NT were characterized by decreased functional connectivity between the anterior midcingulate cortex (aMCC) and both the left dorsolateral prefrontal cortex (dlPFC) and the left amygdala. None of these regions showed modulated brain activation by NT. Our findings suggest that the neural substrates of NT can be better characterized by reduced top-down aMCC-amygdala regulation as well as inefficient communication within the dorsal cognitive system (aMCC-dlPFC), rather than changes in brain activation in isolated regions. These observations offer valuable insights into the neural markers of vulnerability to mood and anxiety disorders.

Personality identification is a common and central problem in text processing. Sensing personality is helpful for various purposes; for example, estimating users' personalities before providing them with any service is necessary. Individuality is essential in a person's nature in every outlook, for instance, in text writing. But, this remains a core challenge because of the low accuracy achieved. The proposed study solves this problem and presents a big five trait identification technique from text data, which applies a feature selection method to increase accuracy. This technique is called linguistic feature selection for personality trait identification (LFSPTI). This technique first finds features based on mutual information (MI), F-statistic, principal component analysis (PCA), and chi-square, then uses the genetic algorithm (GA) to select high-ranked features from all feature subsets. These four parameters provide various forms of the dataset. The experimental results exhibit that the LFSPTI method enhances the classification accuracy against the best of the competing methods by 1.18%, 0.83%, 1.61%., 1.15%, 1.82%, and 1.39% for extraversion (EXT), neuroticism (NEU), agreeableness (AGR), conscientiousness (CON), openness (OPN), and mean overall personality traits, respectively.

Mechanized personality discovery from image features has arisen and acquired a lot of consideration in the branch of knowledge of full of feeling registering and opinion examination. This work presents a deep learning model that can measure personality characteristics on five classes which is Conscientiousness (CON), Openness to experience (OPN), Extraversion (EXT), Neuroticism (NEU) and Agreeableness (AGR) from a picture. This work proposes a model utilizing Convolutional Neural Networks to naturally extract highlights from a representation that are marks of personality qualities. To improve the detection level of personality Traits, Faster Region Convolutional Neural Network (F-CNN) model with Adaptive Bacterial Foraging Optimization (ABFO) is presented. The proposed model exhibits the effectiveness of the acquainted technique with a promising personality forecast model and can group the client's personality qualities when contrasted with the best-in-class procedures. While assessing the proposed technique, results show a ruthless and critical exactness improvement in contrast with the latest outcomes for the Personality dataset for personality recognition. Moreover, present the usually utilized datasets and call attention to a portion of the difficulties of personality-mindful proposal frameworks.

Digestive and psychiatric disorders tend to co-occur, yet mechanisms remain unclear. Leveraging genetic and transcriptomic data integration, we conduct multi-trait analysis of GWAS (MTAG) and weighted gene co-expression network analysis (WGCNA) to explore shared mechanism between psychiatric and gastrointestinal disorders. Significant genetic correlations were found between these disorders, especially in irritable bowel syndrome (IBS), gastroesophageal reflux disease (GERD), depression (DEP), and neuroticism (NE). MTAG identify 60 novel pleiotropic loci for IBS and 14 for GERD, predominantly located near genes associated with neurological pathways. Further WGCNA identifies multiple co-expression modules enriched with genes involved in neurological pathways in digestive tissues, with some modules strongly preserved across brain and digestive tissues. Moreover, our network analysis suggests BSN, CELF4, and NRXN1 as central players in the regulation of the gut-brain axis (GBA). This study enhances our understanding of the GBA and underscores BSN, CELF4, and NRXN1 as crucial targets for future research.

ABSTRACT This study investigates the influence of personality traits, extracurricular entrepreneurship education and own business in the family on students’ entrepreneurial intentions. Grounded in the Big Five personality theory, the research examines how traits such as openness to new experiences (ABEX), extroversion (EX), conscientiousness (CON), and neuroticism (NEU) affect entrepreneurial intentions (INEM). Additionally, extracurricular entrepreneurship education (EDU) and family-owned businesses (NEGPRO) are also investigated in this relationship. Using a quantitative approach, using PLS-SEM, data were collected from a sample of 340 students, in Brazil, revealing significant positive relationships between ABEX, EX, and CON with INEM. Conversely, NEU exhibited no significant impact. Additionally, the presence of NEGPRO significantly influenced students’ EI. The findings underscore no significant impact of EDU, highlighting the critical relevance of tailoring extracurricular entrepreneurial programmes to foster these key personality traits, ultimately enhancing students’ likelihood of pursuing entrepreneurial ventures. This research contributes to the theoretical understanding of entrepreneurship while providing practical implications for educators and policymakers aiming to cultivate entrepreneurial mindsets among students. Future studies are recommended to explore additional factors influencing entrepreneurial intentions in diverse contexts.

Depending on Internet of Vehicle (IoV) and on-board units in 5 G, intelligent connected vehicles (ICVs) gradually become the digital infrastructure and data hub to provide personalized in-vehicle infotainment (IVI), which naturally collect, use, and share the data related to users’ bio-informatics, travel patterns, and usage habits. The privacy concern (PC) will be generated when using those data services. In this article, we construct the structural equation model (SEM) to study the influencing factors of users’ PC and self-disclosure behavior (SD) regarding to ICV data services from the personality traits (PTs) perspective. A total of 481 valid questionnaires from 700 IVI users was collected. Results show that extraversion (EXT), conscientiousness (CNS), neuroticism (NEUR), privacy invasion experience (PIE), and privacy self-efficacy (PE) have significant impacts on PC. Besides, PC does not directly affect SD, but is indirectly affected by limiting profile visibility (LPV). Through revealing the relationship among PTs, PC and SD toward ICV data services, we aim to provide insights for vehicle data governance to facilitate the human-vehicle interaction.

Anorexia nervosa (AN) is a heritable eating disorder (50–60%) with an array of commonly comorbid psychiatric disorders and related traits. Although significant genetic correlations between AN and psychiatric disorders and related traits have been reported, their shared genetic architecture is largely understudied. We investigated the shared genetic architecture of AN and schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), mood instability (Mood), neuroticism (NEUR), and intelligence (INT). We applied the conditional false discovery rate (FDR) method to identify novel risk loci for AN, and conjunctional FDR to identify loci shared between AN and related phenotypes, to summarize statistics from relevant genome-wide association studies (GWAS). Individual GWAS samples varied from 72,517 to 420,879 participants. Using conditional FDR we identified 58 novel AN loci. Furthermore, we identified 38 unique loci shared between AN and major psychiatric disorders (SCZ, BIP, and MD) and 45 between AN and psychological traits (Mood, NEUR, and INT). In line with genetic correlations, the majority of shared loci showed concordant effect directions. Functional analyses revealed that the shared loci are involved in 65 unique pathways, several of which overlapped across analyses, including the “signal by MST1” pathway involved in Hippo signaling. In conclusion, we demonstrated genetic overlap between AN and major psychiatric disorders and related traits, and identified novel risk loci for AN by leveraging this overlap. Our results indicate that some shared characteristics between AN and related disorders and traits may have genetic underpinnings.

The study is focused on certain personality traits that are significantly associated with investors’ biases. The study attempts to develop a more comprehensive mathematical model that covers a wider range of behavioral aspects related to individual investors The proposed Mathematical model can be used to better understand the major behavioral dimensions that need to be considered for investment decisions in the stock market In this study, a Partial Least Square Structural Equation Modelling (PLS-SEM) is used to quantify the association between major personality traits i.e. Agreeableness (AG), Conscientiousness (CO), Extroversion (EX) Neuroticism (NE), and Openness (OP) and major psychological biases such as Herding (HE), Overconfidence (OC), Representativeness (RP), and Anchoring (AN) in the stock market. The model is based on a survey of 467 individual investors, who provided information on their personality traits and psychological biases. The regression analysis was done to examine the relationship between personality traits, and psychological biases. Further, the explanatory power and predictive relevance of the model are tested using R2, Q2, and RMSE.

Objective: We employed latent profile analysis (LPA) to discern configurations of executive functioning (EF) and neuroticism (NE) and tested their concurrent validity with respect to internalizing and externalizing problems and physical health. Participants: A total of 125 college students completed the study. Methods: Participants self-reported NE and EF on separate normed rating scales and completed computerized tests of EF. Self-reported internalizing problems, externalizing problems, and global physical health were collected. Results: LPA revealed four profiles: (1) Lower EF + Higher NE, (2) Higher EF + Lower NE, (3) Inconsistent EF + Higher NE, and (4) Inconsistent EF + Lower NE. Adjusting for covariates, profiles were differentially associated with internalizing problems, externalizing problems, and physical health. Conclusions: Screening EF and NE in college students may identify those at risk for psychopathology and physical health concerns. Tailored prevention and intervention efforts on college campuses targeting EF and NE may enhance well-being.

Machine learning has been an effective tool to connect networks of enormous information for predicting personality. Identification of personality-related indicators encrypted in Facebook profiles and activities are of special concern in most research efforts. This research modeled user personality based on set of features extracted from the Facebook data using Map-Reduce Back Propagation Neural Network (MRBPNN). The performance of the MRBPNN classification model was evaluated in terms of five basic personality dimensions: Extraversion (EXT), Agreeableness (AGR), Conscientiousness (CON), Neuroticism (NEU), and Openness to Experience (OPN) using True positive, False Positive, accuracy, precision and F-measure as metrics at the threshold value of 0.32. The experimental results reveal that MRBPNN model has accuracy of 91.40%, 93.89%, 91.33%, 90.43% and 89.13% CON, OPN, EXT, NEU and AGR respectively for personality recognition which is more computationally efficient than Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Therefore, personality recognition based on MRBPNN would produce a reliable prediction system for various personality traits with data having a very large instance. Keywords— Machine learning, Facebook, MRBPNN, Personality Recognition, Neuroticism, Agreeableness.

Adolescence marks a period where depression will commonly onset. Twin studies show that genetic influences play a role in how depression develops and changes across adolescence. Recent genome-wide association studies highlight that common genetic variants - which can be combined into polygenic risk scores (PRS) - are also implicated in depression. However, the role of PRS in adolescent depression and changes in adolescent depression is not yet understood. We aimed to examine associations between PRS for five psychiatric traits and depressive symptoms measured across adolescence using cross-sectional and growth-curve models. The five PRS were as follows: depression (DEP), major depressive disorder (MDD), anxiety (ANX), neuroticism (NEU) and schizophrenia (SCZ). We used data from over 6,000 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC) to examine associations between the five PRS and self-reported depressive symptoms (Short Mood and Feelings Questionnaire) over 9 occasions from 10 to 24 years. The PRS were created from well-powered genome-wide association studies conducted in adult populations. We examined cross-sectional associations between the PRS at each age and then again with longitudinal trajectories of depressive symptoms in a repeated measures framework using multilevel growth-curve analysis to examine the severity and the rate of change. There was strong evidence that higher PRS for DEP, MDD and NEU were associated with worse depressive symptoms throughout adolescence and into young adulthood in our cross-sectional analysis, with consistent associations observed across all nine occasions. Growth-curve analyses provided stronger associations (as measured by effect sizes) and additional insights, demonstrating that individuals with higher PRS for DEP, MDD and NEU had steeper trajectories of depressive symptoms across development, all with a greater increasing rate of change during adolescence. Evidence was less consistent for the ANX and SCZ PRS in the cross-sectional analysis, yet there was some evidence for an increasing rate of change in adolescence in the growth-curve analyses with the ANX PRS. These results show that common genetic variants as indexed by varying psychiatric PRS show patterns of specificity that influence both the severity and rate of change in depressive symptoms throughout adolescence and then into young adulthood. Longitudinal data that make use of repeated measures designs have the potential to provide greater insights how genetic factors influence the onset and persistence of adolescent depression.

Anorexia nervosa is a psychological disorder regarding eating habits that affects females far more often than males and is most commonly observed in adolescent females. The exact cause of anorexia has not been definitively established, but thoughts about self-image, family dynamics, and community pressures to some extent genetic factors can be accountable for the disorder. According to a report it affects about one percent of adolescent girls in America. Under the condition, people be likely to illustrate neurotic behaviors and may become infatuated with food that led to extreme dieting and weight loss and to the stage of malnutrition. Actually, characterized by anxiety, especially is experienced during eating and poor self-image in the mirror. The present study is aimed to quantify the percentage of population suffering from the disorder and to correlate the age and gender with the issue. The current study was conducted among teen aged, undergraduate and adults. Male and female read a ten-point vignette describing the conditions that were further quantify to understand the relationship between food anxiety, eating disorders, and related correlates as like co- morbid disorders and personality. One in each twelve participants was observed for experiencing food anxiety at high. They spent meal time with full concentration on their imaginary obesity, mistakes and mishaps consequently stress lowers their appetite. The findings showed that how psychological and personality disorders of perfectionism correlates to anxiety during meals and emotional strain of being obese.

Higher polygenic risk scores for schizophrenia may be suggestive of treatment non-response in major depressive disorder

Attitudes toward dreams can be positive, e.g., “I think that dreaming is in general a very interesting phenomenon” or negative, e.g., “Dreams are boring for me”. At first, research treated positive and negative attitudes toward dream as two poles of a unidimensional concept but recent evidence has suggested that despite the large overlap both concepts can be differentiated. The findings of the present online survey (N = 1450) supported this notion as variables like age, dream recall frequency, neuroticism, agreeableness are differentially associated with positive and negative attitudes towards dreams. Dream recall frequency and neuroticism were more closely related to positive attitudes toward dreams, whereas low agreeableness was more closely associated with negative attitudes toward dreams. Overall, the correlations between attitude towards dreams and stable factors like personality dimensions raise the question as to how and when attitudes towards dreams are formed, i.e., longitudinal studies during childhood, adolescence, and young adulthood would be desirable.