Search Results (1,709)

The emergence of online brokerage platforms, mobile banking applications, and commission-free trading has altered the investment landscape, renewing commercial and scholarly interest in retail investors. In light of these changes, the present study aims to provide a structural overview of the current state of research on the behaviour of retail investors. Based on a dataset of 386 articles sourced from the Web of Science database, this study employs a composite bibliometric approach of a co-word and co-citation analysis as well as a network analysis to determine preceding scientific discourses, current research themes, and potential avenues for future research. The co-word analysis identifies seven distinct research themes: (1) implications for financial performance; (2) information behaviour; (3) behavioural biases and investor characteristics; (4) investor attention; (5) attitudes towards financial risks; (6) socially responsible investing; and (7) complex financial retail instruments. Incorporating applicable research on individual investors, private investors, and household investors from referenced articles, the co-citation analysis reveals nine preceding scientific discourses. Additionally, the network analyses highlight the concepts and publications currently shaping and likely to influence future research in this field. The present study contributes to the academic discourse by mapping the intellectual landscape of retail investor behaviour, suggesting avenues for future research, and offering valuable insights for navigating this dynamic field. Full article

(1) Background: Sexual harassment in the workplace is a problem that particularly affects women and is not an exception in the world of work. Factors such as power asymmetry, the predominantly masculinized culture in many organizations, and the potential impunity of perpetrators increase the associated risks. This study aims to analyze the psychometric properties of the Sexual Experiences Questionnaire-Workplace (SEQ-W) to measure sexual harassment at work and assess its validity in Spanish. (2) Methods: A total of 120 active female workers (67.5% European and 32.5% South American) participated, completing validated instruments to measure sexual harassment, workplace bullying, self-perceived health, and job satisfaction. The questionnaires were administered online, ensuring anonymity and explaining the study’s objectives. (3) Results: An exploratory factor analysis revealed a bifactorial structure with the dimensions “Sexual Harassment by Coercion” and “Harassment by Unwanted Sexual Attention.” Both dimensions demonstrated adequate internal consistency, concurrent validity with workplace bullying and well-being scales, and discriminant validity with job satisfaction. Additionally, a pronounced floor effect was observed, indicating a low prevalence of sexual harassment in the sample. (4) Conclusions: the SEQ-W scale is a useful tool for assessing sexual harassment among active Spanish-speaking female workers, considering its validated bidimensional structure in Spanish. Full article

Background/Objectives: Research suggests that attitudes toward aging significantly impact health and well-being outcomes in older adults and are influenced by various factors. Our study aims to identify gender differences in attitudes toward aging among aging individuals with disabilities while also examining the influence of demographic and psychological factors on these attitudes. Methods: For this cross-sectional study, we collected data from 393 middle-aged and older adults with disabilities via an online Qualtrics survey administered through the Prolific platform in the United States. Participants completed the Attitudes Towards Aging Questionnaire Short Form, Purpose in Life Test Short Form, Mindfulness Attention Awareness Scale, Acceptance of Chronic Health Conditions Scale, and Three-Item Loneliness Scale. Descriptive and correlation analyses, t-tests, and multiple regression analyses were performed. Results: The independent t-test findings reveal significant differences in physical change and psychological growth between men and women, with men scoring higher in physical change and women in psychological growth. In multiple regression analyses, purpose in life significantly predicted all three domains of attitudes toward aging in men, while both purpose in life and acceptance were predictors across all domains in women. Additionally, age, employment, and financial stability contributed to aging attitudes only among women. Conclusions: Attitudes toward aging, specifically physical change and psychological growth, were found to vary by gender, with purpose in life, acceptance, and loneliness influencing these attitudes among both groups, while certain demographic factors influenced aging attitudes only among women. These findings underscore the need for gender-specific interventions addressing these substantial factors. Full article

Personalized recommendation for online learning courses stands as a critical research topic in educational technology, where algorithmic performance directly impacts learning efficiency and user experience. To address the limitations of existing studies in multimodal heterogeneous data fusion and high-order relationship modeling, this research proposes a Heterogeneous Hypergraph and Attention-based Online Course Recommendation (HHAOCR) algorithm. By constructing a heterogeneous hypergraph structure encompassing three entity types (students, instructors, and courses), we innovatively designed hypergraph convolution operators to achieve bidirectional vertex-hyperedge information aggregation, integrated with a dynamic attention mechanism to quantify important differences among entities. The method establishes computational frameworks for hyperedge-vertex coefficient matrices and inter-hyperedge attention scores, effectively capturing high-order nonlinear correlations within multimodal heterogeneous data, while employing temporal attention units to track the evolution of user preferences. Experimental results on the MOOCCube dataset demonstrate that the proposed algorithm achieves significant improvements in NDCG@15 and F1-Score@15 metrics compared to TP-GNN (enhanced by 0.0699 and 0.0907) and IRS-GCNet (enhanced by 0.0808 and 0.0999). This work provides a scalable solution for multisource heterogeneous data fusion and precise recommendation for online education platforms. Full article

Background: Stress adversely affects health behaviors, particularly problematic eating. However, the psychological mechanisms underlying this relationship remain underexplored. This study seeks to examine the mediating role of irrational health beliefs and the moderating role of negative coping styles in the associations of perceived stress with three types of problematic eating—restrained, emotional, and external eating. Methods: A total of 929 emerging adults (57.8% females; mean age = 21.50 ± 2.36 years, age range = 17–35 years) participated in an online survey to provide their self-reported data. Results: Perceived stress was positively associated with restrained, emotional, and external eating. Irrational health beliefs partially mediated these associations, with indirect effects of 0.24, 0.40, and 0.07, respectively. Negative coping styles only moderated the associations of perceived stress with restrained eating (β = 0.05, p = 0.047) and emotional eating (β = 0.08, p = 0.001), but not external eating (β = 0.01, p = 0.859). Conclusions: Our findings suggest the effect of cognitive factors such as irrational health beliefs and negative coping styles on stress-induced eating. Interventions aimed at cognitively restructuring irrational health beliefs and raising attention on health, as well as promoting adaptive stress-coping strategies that alleviate emotional distress without compromising other aspects of health, are therefore essential. Full article

Whether it is asking Alexa to set a reminder or having Google Assistant place a call, AI-powered assistants are becoming an increasingly seamless part of our daily lives. This study aims to address what predicts the users’ satisfaction with Alexa by analyzing the using motives, cognitive involvement, and emotional involvement of its consumers. The variables include using motives, attention, elaboration, emotional involvement, and usage satisfaction. Alexa users (N = 299) completed a brief online survey, including Scales of Using Motives for Media, the Perceived Attention Scale, the Elaboration Scale, the Mood Adjective Check List Scale, and Television Viewing Satisfaction Scale. Participants who were at least eighteen years of age and owned and used Alexa were included in this study. An exploratory factor analysis revealed four distinct types of motivation for using Alexa: Companionship, Entertainment–Information, Work-Efficiency, and Pastime. The results from hierarchical regressions showed that Alexa usage satisfaction was predicted by Entertainment–Information and feeling positive emotions while using. Full article

Adolescents’ pervasive use of social media has been increasingly linked to aggression, including cyberbullying and hostile online interactions. While this association is well documented, the psychological and contextual mechanisms that mediate or moderate it remain unclear. This scoping review maps quantitative evidence on mediators and moderators between social media use and aggression among adolescents. A comprehensive search using ProQuest’s Summon platform was conducted across PsycINFO, Scopus, PubMed, and Web of Science, following the PRISMA 2020 guidelines. Eligible studies, published between January 2020 and March 2025, included adolescents aged 11–18 and reported at least one statistical mediation or moderation analysis. Forty-four studies from 19 countries (N > 90,000) were thematically synthesized. Key mediators included problematic use, moral disengagement, depression, attention-seeking, and risky digital behaviors. Moderators included gender, body satisfaction, cultural setting, school type, and family attachment. Most of the studies used structural equation modeling or PROCESS macro, although cross-sectional designs predominated. Limitations included reliance on self-reports and inconsistent social media measures. The findings suggest that social media–aggression links are indirect and shaped by emotional, cognitive, and ecological factors. Multi-level interventions targeting digital literacy, moral reasoning, and resilience are needed. This review was not registered and received no external funding. Full article

The issue of pesticide and chemical residue in food has drawn increasing public attention, making effective control of plant pests and diseases a critical research focus in agriculture. Monitoring of pest populations is a key factor constraining the precision of pest management strategies. Low-cost and high-efficiency monitoring devices are highly desirable. To address these challenges, we focus on Cacopsylla chinensis and design a portable, AI-based detection device, along with an integrated online monitoring and forecasting system. First, to enhance the model’s capability for detecting small targets, we developed a backbone network based on the RepVit block and its variants. Additionally, we introduced a Dynamic Position Encoder module to improve feature position encoding. To further enhance detection performance, we adopt a Context Guide Fusion Module, which enables context-driven information guidance and adaptive feature adjustment. Second, a framework facilitates the development of an online monitoring system centered on Cacopsylla chinensis detection. The system incorporates a hybrid neural network model to establish the relationship between multiple environmental parameters and the Cacopsylla chinensis population, enabling trend prediction. We conduct feasibility validation experiments by comparing detection results with a manual survey. The experimental results show that the detection model achieves an accuracy of 87.4% for both test samples and edge devices. Furthermore, the population dynamics model yields a mean absolute error of 1.94% for the test dataset. These performance indicators fully meet the requirements of practical agricultural applications. Full article

Service quality research has traditionally focused either on identifying Kano two-dimensional quality categories or detecting service quality deficiencies. However, integrating these perspectives remains a challenge due to the Kano model’s nonlinear characteristics and the importance-performance and gap analysis (IPGA) model’s linear approach. This study proposes the Kano-IPGA (KIPGA) model, incorporating mutual information (MI) to bridge the gap between these two models. The KIPGA model first employs moderated regression analysis to classify service attributes into Kano’s quality categories. MI is then used to calculate the relative importance (RI), while relative performance (RP) is determined using the original IPGA approach. The results are mapped into the KIPGA strategic matrix, categorizing service attributes into eight management strategies. An empirical analysis of Taiwan’s online insurance systems demonstrates the model’s effectiveness in simultaneously identifying Kano categories and prioritizing service quality improvements. The findings reveal that critical improvement and enhanced improvement regions require immediate attention. The proposed KIPGA model offers a systematic approach for service quality management, providing decision-makers with a structured framework to allocate resources effectively and enhance customer satisfaction. This study contributes to service quality research by offering an integrated model that accounts for both linear and nonlinear quality assessment perspectives. Full article

Background/Objectives: Chronic attention problems occur in approximately 25% of children after acquired brain injury (ABI). When delivered daily, transcranial direct current stimulation (tDCS) may improve attention; however, access to daily in-clinic tDCS treatment can be limited by other commitments, including concurrent therapy, school commitments, and caregiver schedules. Treatment access can be improved through home-based interventions, though these require several practical and safety considerations in a pediatric ABI population. This study evaluated the safety, feasibility, and tolerability of remotely monitored at-home tDCS during online gamified attention training in pediatric ABI. Methods: We conducted a randomized, single-blind, dose-controlled clinical trial of at home tDCS in Brisbane, Australia (10 tDCS sessions; 20 min; 1 mA or 2 mA; bilateral dorsolateral prefrontal cortex). Participants attended our clinic at baseline for clinical assessments, fitting of the personalized tDCS headband, and training in how to use tDCS at home. All sessions were remotely supervised using live videoconferencing. We assessed the feasibility and tolerability of at-home tDCS and our customized, personalized at-home tDCS headband as primary outcomes. As secondary outcomes, we evaluated changes in functional connectivity (fc) and reaction time (RT). Results: Seventy-three participants were contacted over six months (January-June 2023) and ten were enrolled (5 males; mean age: 12.10 y [SD: 2.9]), satisfying a priori recruitment timelines (CONSORT reporting). All families successfully set up tDCS and completed attention training with excellent protocol adherence. There were no serious adverse events over the 100 total sessions. Nine participants completed all stimulation sessions (1 mA: n = 5, 2 mA: n = 4). Participants in the 2 mA group reported greater tingling, itching, and discomfort (all p < 0.05). One participant in the 1 mA group was unable to complete all sessions due to tolerability challenges; however, these challenges were resolved in the second half of the intervention by gradually increasing the stimulation duration across the 10 days alongside additional coaching and support. Conclusions: Overall, daily remotely supervised at-home tDCS in patients with pediatric ABI is safe, feasible, and tolerable. Our results support larger, sham-controlled efficacy trials and provide a foundation for the development of safe and effective at-home stimulation therapeutics that may offer targeted improvement of neurocognitive symptoms in children. Full article

The rapid growth of social media and online information-sharing platforms facilitates the spread of rumors. Accurate rumor detection to minimize manual verification efforts remains a critical research challenge. While multimodal rumor detection leveraging both text and visual data has gained increasing attention due to the diversification of social media content, existing approaches face the following three key limitations: (1) yhey prioritize lexical features of text while neglecting inherent logical inconsistencies in rumor narratives; (2) they treat textual and visual features as independent modalities, failing to model their intrinsic connections; and (3) they overlook semantic incongruities between text and images, which are common in rumor content. This paper proposes a dual-chain multimodal feature learning framework for rumor detection to address these issues. The framework comprehensively extracts rumor content features through the following two parallel processes: a basic semantic feature extraction module that captures fundamental textual and visual semantics, and a logical connection feature learning module that models both the internal logical relationships within text and the cross-modal semantic alignment between text and images. The framework achieves the multi-level fusion of text–image features by integrating modal alignment and cross-modal attention mechanisms. Extensive experiments on the Pheme and Weibo datasets demonstrate that the proposed method performs better than baseline approaches, confirming its effectiveness in detecting multimodal rumors. Full article

Proton exchange membrane fuel cell (PEMFC) fault diagnosis faces two critical limitations: conventional offline methods lack real-time predictive capability, while existing prediction approaches are confined to single fault types. To address these gaps, this study proposes an online multi-fault prediction framework integrating three novel contributions: (1) a sensor fusion strategy leveraging existing thermal/electrochemical measurements (voltage, current, temperature, humidity, and pressure) without requiring embedded stack sensors; (2) a real-time sliding window mechanism enabling dynamic prediction updates every 1 s under variable load conditions; and (3) a modified CNN-based Bi-LSTM parallel model with attention mechanism (ConvBLSTM-PMwA) architecture featuring multi-input multi-output (MIMO) capability for simultaneous flooding/air-starvation detection. Through comparative analysis of different neural architectures using experimental datasets, the optimized ConvBLSTM-PMwA achieved 96.49% accuracy in predicting dual faults 64.63 s pre-occurrence, outperforming conventional LSTM models in both temporal resolution and long-term forecast reliability. Full article

Background and Objectives: Psychological disorders are prevalent and distressing. Early treatment initiation can prevent adverse outcomes and reduce healthcare system impacts. Improving mental health literacy (MHL)—one’s knowledge regarding psychological disorders—and treatment-seeking attitudes (TSAs) is key in early treatment initiation. Examining the facets of dispositional mindfulness—the capacity to pay attention to present-moment experiences with acceptance—may offer more granular insights into understanding MHL and TSAs. This study examined (a) associations between mindfulness facets and MHL and TSAs, (b) facets’ prediction of MHL and TSAs beyond demographics, (c) moderation of the MHL–TSA relationship by mindfulness facets, and (d) mediation of mindfulness–TSA relationships via general self-efficacy (GSE). Methods: A community sample of 299 adults was recruited online (TurkPrime) and completed demographic questions and self-report measures: Five-Facet Mindfulness Questionnaire-15, Mental Health Literacy Scale, Mental Help-Seeking Attitudes Scale, and General Self-Efficacy Scale. Results: Describe, Non-Judgment, and Act with Awareness were modestly associated with MHL; all five facets correlated with TSAs. Hierarchical regressions controlling for demographics showed that Describe and Non-Reactivity predicted MHL, while Act with Awareness uniquely predicted TSAs. Non-Reactivity moderated the MHL–TSA relationship, with higher Non-Reactivity amplifying the relationship. GSE fully mediated relationships between Observe and Non-Judgment with TSAs, suggesting self-efficacy is a key mechanism of these facets. Conclusions: Interventions cultivating Non-Reactivity, Describe, and Act with Awareness may improve the translation of mental health knowledge into treatment-seeking behaviors. Future research should explore how mindfulness facets independently and interactively foster early intervention and treatment engagement. Full article

This paper explores the role of artificial intelligence (AI) in personalizing social media marketing strategies and its impact on customer experience, with a focus on consumers within the MENA region. Using data collected from an online questionnaire completed by 893 individuals, the study confirms that AI significantly enhances social media marketing by offering personalized content, optimizing influencer selection, and enabling real-time consumer interaction. These capabilities not only increase customer awareness but also improve user experience and purchase intentions. Key AI tools such as influencer marketing, content optimization, and customization are effective in capturing consumer attention, although further research is necessary to deepen understanding. By examining AI’s ability to analyze vast datasets and support targeted marketing efforts, the study contributes to both academic and practical discourse, offering insights that businesses can use to refine their AI-driven social media strategies. Ultimately, the research aims to guide marketers through the complexities of AI deployment, ensuring its benefits are fully realized for consumers. Full article

Contamination with foreign fibers—such as mulch films and polypropylene strands—during cotton harvesting and processing severely compromises fiber quality. The traditional detection methods often fail to identify fine impurities under visible light, while full-spectrum hyperspectral imaging (HSI) techniques—despite their effectiveness—tend to be prohibitively expensive and computationally intensive. Specifically, the vast amount of redundant spectral information in full-spectrum HSI escalates both the system’s costs and processing challenges. To address these challenges, this study presents an intelligent detection framework that integrates optimized spectral band selection with a lightweight neural network. A novel hybrid Harris Hawks–Whale Optimization Operator (HWOO) is employed to isolate 12 discriminative bands from the original 288 channels, effectively eliminating redundant spectral data. Additionally, a lightweight attention mechanism, combined with a depthwise convolution module, enables real-time inference for online production. The proposed attention-enhanced CNN architecture achieves a 99.75% classification accuracy with real-time processing at 12.201 μs per pixel, surpassing the full-spectrum models by 11.57% in its accuracy while drastically reducing the processing time from 370.1 μs per pixel. This approach not only enables the high-speed removal of impurities in harvested seed cotton production lines but also offers a cost-effective pathway to practical multispectral solutions. Moreover, this methodology demonstrates broad applicability for quality control in agricultural product processing. Full article