This longitudinal study investigates the role of implicit psychological processes in student success in digital higher education (DHE). In traditional study formats, implicit academic attitudes, as measured with implicit association tests (IAT), were excellent predictors of student success. Since no such findings exist for DHE, we conducted two separate IATs to predict study behavior in a large sample of students over six months. To gain a more detailed understanding of student success in DHE, analyses differentiated between the engagement and persistence of students, which were operationalized by the completion of examinations and dropping out of university, respectively. Predictors primarily included the student's implicit and explicit academic identification and -attitude. An Attitude-IAT (study + good) predicted engagement (completed examinations) in a logistic regression model, and an Identification-IAT (study + me) correlated with factors that are indirectly associated with persistence (educational background, self-efficacy, identification). The effect of the Attitude-IAT also remained significant after accounting for explicit measures. This shows that student success in DHE is predictable based solely on students' reaction times in a simple cognitive-psychological test. This test could potentially become part of a battery of items to identify students at risk of dropout. The findings also have important implications for understanding the relationship between student success and student motivation in DHE.

We investigate the role of individual adaptability (I-ADAPT) in newcomer socialization by exploring its effects on the proximal and distal outcomes of the work-role transition process. Based on I-ADAPT theory, our study assesses how I-ADAPT affects the three key indicators of newcomer adjustment (i.e., role clarity, task mastery, and social acceptance) and, in turn, their effect on the distal socialization outcomes of job satisfaction, turnover intention, and work withdrawal. Using a 2-wave prospective design, we collected data from 280 newcomers recruited through Prolific. Results indicated that I-ADAPT dimensions differentially predict newcomer adjustment, with learning adaptability affecting role clarity, uncertainty adaptability affecting task mastery, and cultural adaptability affecting social acceptance. Additionally, role clarity mediated the link between learning adaptability and distal outcomes, whereas uncertainty adaptability had an indirect effect on turnover intention and work withdrawal through task mastery. Our findings highlight the value of I-ADAPT as a newcomer characteristic for effective onboarding and offer practical insights to managers and human resource practitioners.

This study examines how health, depression, and relationship dynamics contribute to both relationship satisfaction and life satisfaction among couples, using data from 7213 German couples. The analyses show that the key predictors differ substantially between these two domains. For relationship satisfaction, intimacy and mutual partner satisfaction emerge as the strongest predictors. In contrast, life satisfaction is primarily shaped by personal health status and depressive symptoms, with relationship quality providing additional but less dominant effects. Importantly, partners' subjective evaluations of their relationship account for more variance in both outcomes than objective factors such as age, income, or health constraints. These findings challenge the view that individual characteristics alone determine well-being within couples and suggest that shared relational processes play a central role. The results highlight the need to consider dyadic dynamics when seeking to understand how couples maintain relationship and life satisfaction, particularly in the face of health-related and psychological challenges.

Although previous studies have established the crucial role of stigma in inhibiting psychological help-seeking, little is known about how distinct mental disorders shape this process through their specific cognitive characteristics. To address this gap, the present study takes Generalized Anxiety Disorder (GAD) as an illustrative case to explore how its core cognitive traits amplify stigma effects and suppress help-seeking intention. This study is a conceptual investigation that proposes the Cognitively-Sensitized Stigma Model, integrating the Theory of Planned Behavior (TPB) to elucidate how stigma influences help-seeking through the pathways of attitude, subjective norm, and perceived behavioural control. The model posits that three core cognitive traits of GAD-fear of negative evaluation, meta-worry, and intolerance of uncertainty-function as cognitive amplifiers that intensify the inhibitory impact of stigma. Attachment styles are further incorporated as moderating mechanisms, highlighting how different interpersonal regulation patterns may either magnify or buffer the "cognition-stigma-help-seeking" chain. Using Chinese culture as an illustrative context, the study demonstrates how collectivism, face concerns, and emotional restraint systematically heighten sensitivity to social evaluation and construct a cultural context of shame surrounding help-seeking. This conceptual framework bridges cognitive, cultural, and stigma theories, offering a foundation for future cross-cultural validation and culturally sensitive interventions.

Grounded in psychological flexibility theory, this study examined the mediating role of academic self-efficacy in the relationship between parental rearing styles and academic self-handicapping among Chinese university students. A sample of 520 undergraduate and postgraduate students completed measures assessing parental warmth, overprotection, and rejection, as well as academic self-efficacy and self-handicapping behaviors. Structural equation modeling indicated that both paternal and maternal warmth were positively related to academic self-efficacy, which in turn was negatively related to self-handicapping, supporting partial mediation. Overprotection from both parents was associated with lower self-efficacy and higher self-handicapping, with modest indirect pathways. For parental rejection, differentiated patterns emerged: paternal rejection was not significantly related to self-efficacy but directly predicted higher self-handicapping, whereas maternal rejection was negatively associated with self-efficacy and also positively associated with self-handicapping, resulting in a significant indirect effect. This suggests that role differences may not be universal across parenting dimensions but are more pronounced in contexts of rejection. Overall, the findings indicate that academic self-efficacy may help explain observed associations between parenting behaviors with students' tendencies toward self-handicapping.

The purpose of this study is to explore the mechanism by which the perceived social sustainability of brands influences consumers' willingness to pay a premium through brand image. Additionally, environmental concern is examined as a moderator in this mediating relationship. Two survey studies were conducted to examine the theoretical effects in two different contexts: the first study focused on general brands, and the second study focused on fashion brands. The first study collected data from 335 respondents in Malaysia, whereas the second study gathered data from 183 respondents in the US through Prolific. The data were analyzed using partial least squares structural equation modeling (PLS-SEM). Results obtained from two studies report the same results. It is found that perceived social sustainability of brands positively affects both willingness to pay a premium and brand image. Additionally, brand image positively influences willingness to pay a premium. It is also found that brand image mediates the relationship between perceived social sustainability of brands and willingness to pay a premium. Moreover, environmental concern moderates the relationship between perceived social sustainability of brands and brand image. Furthermore, environmental concern also moderates the mediating relationship between perceived social sustainability of brands and willingness to pay a premium through brand image. Understanding the impact of sustainability in business has been a central focus for many scholars. However, few studies have examined the consequences of the social aspects of sustainability. This study uniquely assesses the influence of perceived social sustainability of brands on consumers' willingness to pay a premium. Additionally, mediating and moderating mechanisms are proposed and analyzed.

This study develops and validates a Critical Thinking Disposition (CTD) scale specifically designed for Chinese high school students, addressing the need for assessment tools tailored to this particular demographic. Anchored in Facione's seven-dimensional framework and previous conceptual research, the study employed a rigorous three-phase process-item development, pilot testing, and final validation-using two convenience samples to examine the scale's psychometric properties. Exploratory and confirmatory factor analyses refined the instrument into a five-dimensional structure: Systematic-analyticity, Open-mindedness, Confidence in Reasoning, Reflective Skepticism, and Truth-seeking. The final model demonstrated optimal fit indices (χ/df = 2.173, CFI = 0.964, RMSEA = 0.043), confirming its structural stability. In addition, convergent and discriminant validity were supported by Average Variance Extracted (AVE), Composite Reliability (CR), and the Fornell-Larcker criterion. At the same time, internal consistency was evidenced by robust Cronbach's α (0.881) and McDonald's ω (0.908) values for the overall scale. Multi-group CFA further established measurement invariance across gender (ΔCFI <0.01, ΔRMSEA <0.015). Overall, the scale provides a reliable and theoretically grounded instrument for assessing CTD in secondary education contexts, enabling educators to evaluate the CTD of high school students comprehensively.

This cross-sectional study (N = 572) examined how chronological age and individual risk perceptions influence the perceived usefulness of disruptive implant and wearable technologies, controlling for sex. Previous research has frequently examined age effects in technology acceptance but rarely employed differentiated measures of perceived risk, especially for body-integrated technologies. To address this gap, we developed and validated the Implant Risk Scale (IRS) to assess specific risk dimensions associated with implantable technologies. Ten potential risk dimensions were identified from prior research, and 50 items were developed based on the relevant scientific literature. Exploratory factor analysis of this item pool identified four distinct risk factors: ethical, physiological, psychological, and data hacking risk, accounting for 85 % of the total variance. Each risk could reliably be assessed with three items. In addition to age (β = -0.22, p < .01), physiological risk (β = -0.27, p < .01) and psychological risk (β = -0.16, p < .01) had independent negative effects on perceived usefulness. Moreover, moderation analyses indicated that each implant risk dimension enhanced the negative effect of age on perceived usefulness (ΔRs between 0.01 and 0.02, ps < 0.01). Differentiated risk dimensions should be considered when examining technology adoption behavior. The validated IRS provides a concise framework for capturing these risk assessments. Different demographic groups seem to be affected to varying degrees by these risk dimensions. Social inequalities between demographic groups due to the use of disruptive technologies might be widened by perceived risk.

This study investigates how learners of Chinese as a Foreign Language (CFL) interact with generative artificial intelligence (GenAI) tools to support oral script-writing within a project-based learning (PBL) environment. Positioned at the intersection of cognition, motivation, and AI mediation, the research examines both learner perceptions and the challenges of integrating GenAI into classroom practice. A qualitative approach was adopted, drawing on focus group interviews, AI-assisted student scripts, and teacher observation logs. Thematic analysis revealed four core themes: GenAI as a cognitive scaffold, GenAI-induced cognitive load, creativity and efficiency in script preparation and over-reliance and usability challenges. Results indicate that learners' proficiency levels shaped their engagement with GenAI. Higher-proficiency students engaged with outputs criticall evaluated AI outputs, refining and creatively adapting suggestions, while lower-proficiency peers relied more heavily on AI's linguistic accuracy yet often struggled to adjust content for contextual and communicative appropriateness. Teacher observations highlighted peer collaboration as an essential compensatory mechanism, particularly for less proficient learners, in refining AI-generated scripts. Nonetheless, a persistent gap remained between AI-assisted writing and spontaneous oral performance, underscoring the limitations of GenAI in fostering real-time communicative competence. This study contributes to the growing body of research on AI in language education by demonstrating how GenAI can simultaneously scaffold and constrain learning within PBL contexts. The findings emphasize the importance of differentiated scaffolding, the development of AI literacy, and instructional designs that combine AI-assisted writing with opportunities for authentic oral interaction.

The pervasive implementation of generative AI technology in art and design has made exploring students' learning intentions towards generative art a pivotal research topic in higher education. This study aims to explore the factors influencing university students' intention to learn generative art, providing a theoretical basis for teaching reform. A combination of Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) hybrid analysis was employed to analyse data from 556 valid questionnaires collected from 58 Chinese universities with art and design programs. A multi-dimensional factor prediction model was constructed, incorporating Cognitive Expectancy (CE), Learning Expectancy (LE), Social Context Influence (SCI), Affective Motivation (AM), Human-AI Co-creation Experience (HACE), and Ethical Responsibility Awareness (ERA). The findings indicate that AM and HACE exert the most significant influence on students' learning intention, with normalized relative importance values of 100 % and 91.6 %, respectively. This is followed by ERA (81.5 %), LE (77.7 %), CE (77.6 %), and SCI (76.9 %). The model's R value is 66.3 %, and its Q value is 0.657, indicating strong predictive power. The findings provide theoretical guidance and practical insights for generative art education, suggesting that teaching should focus on affective motivation and human-AI co-creation experience while integrating ethical responsibility education. Establishing a clear learning path will enhance the appeal of generative art, promote students' learning intention and creative innovation, and provide theoretical support and empirical reference for the development and expansion of generative art courses in higher education.

The treatment of depression involves numerous barriers, both before and after individuals seek professional care. Online health communities (OHCs) have emerged as valuable platforms for people coping with depression. We collected 1,585,429 posts from a depression-focus OHC in China between May 2022 and September 2023. Based on both keyword filtering and deep learning classification, we identified two user groups: 25,743 post-treatment users (those currently or previously under treatment) and 4891 pre-treatment users (those who had not yet sought formal care). We examined engagement differences between these two groups across four key dimensions: information sharing, continued participation, community status, and interaction preference. Furthermore, the content analysis was conducted to gain insights into the treatment barriers they faced. The results showed that post-treatment users emerge as central figures in OHC, exhibiting higher engagement levels and actively driving information exchange. The barriers for pre-treatment patients include fear and hesitation regarding treatment (26.63 %), difficulty accessing mental health services (19.41 %), financial barriers (18.62 %), lack of understanding and support (15.53 %), shame and stigma (12.20 %), and a lack of information (7.61 %). Medication side effects (34.23 %), medication management (20.29 %), emotional fluctuations (19.36 %), lack of social support (14.54 %), doctor-patient relationship (6.30 %), financial and healthcare access challenges (5.28 %) are the barriers for post-treatment patients. This study contributes to the understanding of the multifaceted nature of depression treatment and underscores the potential of online communities to play a transformative role in mental health care.

Advancements in generative artificial intelligence (genAI) have made it easier to impersonate someone else, allowing users to create realistic images of entirely new personas or trusted individuals. While susceptibility to message-based inauthenticity (e.g., phishing) is well investigated, it remains unclear if there are similar cognitive mechanisms that support inauthenticity detection across message- and image-based techniques (e.g., AI-generated faces). The present study examined (1) if users are similar in their detection of inauthenticities that are image- or messaged-based, (2) if the same individual differences that predict susceptibility to message-based inauthenticity extend to image-based inauthenticity, and (3) if there are other individual differences that are broadly related to digital inauthenticity detection. For the message-based tasks, participants classified real and phishing emails/text messages. For the image-based task, participants classified real and AI-generated faces. Participants' cognitive reasoning styles, digital and risk literacy, and demographics were also assessed. Our findings suggest that users may be less likely to detect image-based digital inauthenticity, like AI-generated faces, compared to message-based scams, like phishing attacks. Additionally, our results indicate that while participants made poorer and riskier classifications with the faces, user characteristics such as risk literacy, cognitive reflection, and gender may be linked to the successful identification of image-based inauthenticity. Shared vulnerability across methods may depend on similarities in content, such as incorporating images/text. As online actors leverage genAI-tools to help develop more elaborate methods of digital inauthenticity, users will likely continue to struggle to identify digital inauthenticity, emphasizing the need for better technical safeguards and user interventions.

In the rapidly evolving educational training sector, organizations are increasingly investing in digital technologies to support service innovation. However, the relationship between these investments and employee behavior remains underexplored. This study investigates how organizational digital capability is associated with employees' service innovation behavior (SIB), considering the potential mediating role of cognitive flexibility and the moderating effect of empowering leadership. Grounded in the conservation of resources (COR) theory, data were collected from 283 paired leader-employee surveys across 25 education and training institutions in Guangxi, China. Partial least squares structural equation modeling (PLS-SEM) was employed for analysis. The results suggest a positive relationship between organizational digital capability and SIB, although the effect size is small. Cognitive flexibility partially mediates the relationship between organizational digital capability and SIB. Additionally, empowering leadership strengthens the influence of digital capability on cognitive flexibility, which in turn facilitates SIB. These findings underscore the importance of not only digital resources but also leadership practices and cognitive flexibility in fostering service innovation. The study contributes to the literature by offering insights into the mechanisms linking digital transformation to service innovation, providing practical implications for organizations aiming to enhance service innovation through digital resources and leadership support.

Mindfulness music training may reduce music performance anxiety (MPA) and improve technical precision.

This study explores the Supervisor-Graduate Student Relationship (SGSR) using text mining and introduces a 2 × 2 quadrant model based on "control level" and "direction level," identifying four relationship types: Juice Coach (High-Control, High-Direction), Shadow Hermit (Low-Control, Low-Direction), Pilot Captain (High-Direction, Low Control), and Alchemy Master (High-Control, Low-Direction). It also examines how these relationships vary across disciplines and geo-economic contexts, finding that social sciences favor Alchemy Master styles, while economically underdeveloped regions show stronger control. A dataset of 25,219 evaluation texts from 445 Chinese universities was analyzed using BERT-TextCNN, BERTopic modeling, and BERT embedding-based semantic similarity, showing strong performance in sentiment analysis and thematic clustering. The findings offer not only theoretical insights into SGSR dynamics but also concrete practical implications: the proposed framework enables institutions to systematically monitor supervisory quality, identify at-risk relationships through routine feedback, and trigger timely interventions-functioning as an early warning system to protect student well-being and support faculty development. This work thus provides both a novel typology of SGSR and a scalable, data-driven tool for improving research supervision in real-world educational settings.

Despite theoretical links between dispositional hope and adaptability in education and their respective roles as key orientations that facilitate learning, their measurement in English as Foreign Language (EFL) students remains relatively underexplored. Previous validations of Snyder's Dispositional Hope Scale (DHS) and Martin's Adaptability Scale (AS) have primarily relied upon classical test theory rather than more sophisticated methodologies. This study assessed the psychometric properties of the Chinese versions of these scales (CDHS and CAS) in EFL college students using Rasch analysis, exploratory structural equation modeling (ESEM), and network analysis.

Inhibition of return (IOR) refers to the longer reaction times (RTs) to targets presented at previously cued, fixated or attended locations. It has been suggested that there are two distinct forms of IOR. The input form, generated when the reflexive oculomotor system is suppressed, affects the sensory/perceptual processing. The output form, generated when the reflexive oculomotor system is not suppressed, biases responding. It has been demonstrated, using the locus of slack logic associated with the psychological refractory period (Pashler, 1998),that the input form of IOR operates on a pre-bottleneck stage of processing, Kavyani et al. (2017). Using the same logic, Klein et al. (2020) demonstrated that the output form of IOR operates at or after the bottleneck. Building on the methods of Klein et al. the present study used PRP paradigm to determine whether the output form of IOR operates at or after the bottleneck. The output form of IOR was generated by an initial saccade from a peripheral location to a central fixation point. Task 1 consisted of a manual response indicating the location (right/left) of a subsequent visual stimulus. Task 2 required participants to discriminate the frequency (high/low) of an auditory stimulus and make a key-press response with their other hand. The targets (T1 and T2) for the two tasks were presented in close succession with 200, 400 and 800 ms target-target onset asynchronies (TTOAs). Responses to T1 were delayed by IOR and responses to T2 were substantially delayed when the TTOA was brief. Statistical analysis of the amount of carry over of the IOR effect experienced by Task 1 onto the RTs for Task 2 strongly suggest that the output form of IOR operates after the bottleneck. Nevertheless, aspects of the results could be interpreted to support a weaker influence of IOR operating also at the bottleneck stage of processing.

This study establishes an integrative machine learning (ML) framework that bridges environmental psychology and data science to investigate the psychological well-being of older adults in urban green spaces (UGS). We applied decision tree (DT), random forest (RF), and artificial neural network (ANN) algorithms, complemented by SHAP interpretability, to multi-dimensional data from 536 seniors in Nanjing, China, aiming to identify key predictors of well-being. While DT achieved the highest accuracy (92.19 %), RF's ensemble approach (87.07 % accuracy) demonstrated superior robustness by effectively mitigating overfitting. Crucially, all models converged in identifying overall UGS satisfaction, a core subjective perceptual metric, as the paramount predictor, underscoring its primacy over traditional accessibility-centric paradigms. SHAP analysis further decoded this global satisfaction into actionable, psychologically salient elements, revealing nonlinear thresholds: wetland parks yielded significant well-being gains (ΔWOOP ≥3.5) only with frequent visits exceeding three weekly and high satisfactions of at least 4 out of 5, while safety facilities and vegetation diversity were identified as key design levers. Our methodology offers a replicable pipeline that balances predictive performance with psychological interpretability. These findings reposition UGS as scalable public health infrastructures for aging well, providing evidence-based, perception-centered strategies to enhance mental and emotional health in urban aging populations.

Children raised in socioeconomically disadvantaged environments experience poorer health outcomes than their more advantaged peers. Evidence examining infancy, a period of intense neuroplasticity, remains limited.

Building on the Conservation of Resources theory, in this study we investigated the longitudinal relationship between videoconference fatigue (VF)-also known as Zoom fatigue-and psychophysical strain, defined as psychophysical symptoms associated with work-related stress. We also investigated the role of age and flexible work arrangement (i.e., remote vs in-person working) as individual and occupational factors that may affect the association between VF and psychophysical strain. A total of 155 workers from different organizations completed two self-report questionnaires, administered at Time 1 (T1) and two months later at Time 2 (T2). Results from moderated multiple regression analysis indicate that VF at T1 positively predicted physical strain at T2. However, it was not associated with psychological strain at T2. Additionally, a positive association was observed between VF at T1 and psychological strain at T2 in remote workers aged 55 or older. Our results suggest that VF may be associated with poorer physical health over time, and that it appears to be linked to poorer mental health among remote workers aged 55 or older. By identifying a particularly vulnerable group of workers, the study provides managerial insights into the use of virtual communication platforms at work. Theoretical and practical implications are discussed.

This study examined the behavioral relationships between pre-service teachers' social competence, social outcome expectations and their social media usage patterns within the framework of social cognitive theory. As digital technologies become increasingly integrated into educational settings, understanding how future educators cognitively and socially engage with social platforms is crucial for fostering adaptive digital behaviors and teacher readiness in the 21st century. Using a relational survey design, data were collected from 354 pre-service teachers enrolled at a public university. Validated instruments were employed to assess social competence, outcome expectations, and social media usage purposes. The results indicated significant positive correlations among the key variables, along with age-related differences in socially motivated media use and a moderating effect of daily internet usage. These findings highlight the importance of incorporating behaviorally informed approaches into teacher education curricula to strengthen digital competence, interpersonal communication, and reflective social media engagement in technology-rich learning environments.