External Behavior Research Articles

Protective factors for externalising behaviour problems in children and adolescents living in out-of-home care: A systematic review

Children and young people in out-of-home care (OOHC) often display disproportionate levels of externalising behaviour problems compared to the general population, which are further linked with detrimental outcomes. Yet, despite similar levels of vulnerability, not all young people in OOHC develop these behaviours. The aim of this systematic review was to identify and synthesise knowledge on protective factors for externalising behaviour problems in young people between 0 and 19 years old residing in OOHC. A systematic search was conducted in PubMed, PsycINFO, Embase, CINAHL, and the Proquest Social Abstracts and Social Services databases, with 31 studies meeting inclusion criteria (n = 8310 individual participants across the studies). Results indicated that protective factors associated with fewer and/or lower levels of externalising behaviour problems included: individual (e.g., better self-concept), relationship (e.g., quality relationships with family members and peers), and contextual (e.g., higher neighbourhood income) factors. Given the short and long-term consequences associated with externalising behaviours, professionals working with young people in OOHC should focus on identifying protective factors that can be targeted in prevention and intervention efforts. This review indicated that strengthening individual, relational, and contextual factors may be most beneficial for reducing risk of externalising behaviours.

BrainMass: Advancing Brain Network Analysis for Diagnosis With Large-Scale Self-Supervised Learning.

Foundation models pretrained on large-scale datasets via self-supervised learning demonstrate exceptional versatility across various tasks. Due to the heterogeneity and hard-to-collect medical data, this approach is especially beneficial for medical image analysis and neuroscience research, as it streamlines broad downstream tasks without the need for numerous costly annotations. However, there has been limited investigation into brain network foundation models, limiting their adaptability and generalizability for broad neuroscience studies. In this study, we aim to bridge this gap. In particular, 1) we curated a comprehensive dataset by collating images from 30 datasets, which comprises 70,781 samples of 46,686 participants. Moreover, we introduce pseudo-functional connectivity (pFC) to further generates millions of augmented brain networks by randomly dropping certain timepoints of the BOLD signal; 2) we propose the BrainMass framework for brain network self-supervised learning via mask modeling and feature alignment. BrainMass employs Mask-ROI Modeling (MRM) to bolster intra-network dependencies and regional specificity. Furthermore, Latent Representation Alignment (LRA) module is utilized to regularize augmented brain networks of the same participant with similar topological properties to yield similar latent representations by aligning their latent embeddings. Extensive experiments on eight internal tasks and seven external brain disorder diagnosis tasks show BrainMass's superior performance, highlighting its significant generalizability and adaptability. Nonetheless, BrainMass demonstrates powerful few/zero-shot learning abilities and exhibits meaningful interpretation to various diseases, showcasing its potential use for clinical applications.

Flourishing levels among health and non-health profession students in Saudi Arabian colleges

ObjectivesThis study aimed to evaluate the levels of flourishing among university students, compare these levels between students in health-related and non-health-related colleges, and identify factors that predict flourishing.MethodsA cross-sectional study was conducted at Imam Abdulrahman Bin Faisal University, Saudi Arabia, involving 1,148 students from the first to fourth year across both academic sectors. Data were collected utilizing the self-reported Global Flourishing Study Questionnaire (GFS) during the period from September 2023 to June 2024. Multistage sampling techniques were employed to ensure a representative sample, with data collection facilitated through a self-administered electronic link on QuestionPro resulting in a response rate of 51.7%. Data analysis was performed using SPSS version 22, incorporating descriptive statistics, t-tests, ANOVA, and regression analysis to identify predictors of flourishing. The reliability and validity of the questionnaire were assessed using Cronbach’s alpha and Pearson’s correlation analysis.ResultsThe study included nearly equal proportions of students from health (51.0%) and nonhealth (49.0%) colleges. The average flourishing score was 85.6 (SD=18.6), with a slight increase in health colleges (85.7) compared with non-health colleges (85.4). Factors that significantly affected flourishing included gender, employment status, exercise frequency, experiences of abuse, and income. In the multiple regression analysis, well-being emerged as the strongest predictor, followed by external factors, disposition, and behavior. Gender exhibited a positive association with flourishing, whereas religion had a negative influence. Furthermore, employment and higher income levels were found to positively contribute to flourishing.ConclusionThis study revealed elevated levels of flourishing among university students in Saudi Arabia, with a notable average score of 85.6. Although the differences between students enrolled in health-related and non-health-related colleges were minimal, significant predictors of flourishing were identified, including well-being, external factors, disposition and behavior, gender, religious affiliation, employment status, income, frequency of exercise, and experience of abuse. These findings underscore the complexity of flourishing and highlight the necessity of considering a range of sociodemographic and lifestyle factors to promote student well-being.

Bilateral Turing Test: Assessing machine consciousness simulations

Recent advancements in Artificial Intelligence (AI) have brought discussions on machine consciousness back to the forefront. However, methodologies for assessing machine consciousness simulations remain highly constrained and lack consensus. This study introduces the Bilateral Turing Test, a thought experiment to assess machine consciousness simulations based on a functionalist perspective, focusing on external behavior and cognitive characteristics associated with consciousness. The test involves both machines and humans participating as judges in an enhanced Turing Test, and compares the external behavior of machine-simulated consciousness with the behavior of human consciousness. We derive the experimental principles via three theoretical propositions reflecting the human–machine cognitive relationship and elucidate the basic conditions and procedures of the thought experiment. A systematic measure is employed to evaluate emulation against human benchmarks, and we establish a statistical threshold for successful consciousness simulations. Our study provides a comprehensive framework for redefining machine consciousness simulations and enhancing interdisciplinary conversations across computer science, neuroscience, and philosophy. This framework also advances bottom-up approaches for assessing machine consciousness simulations and provides pivotal insights into human–machine interaction dynamics.

Mental Health Treatment-Seeking Appraisal, Afrocentric Cultural Norms, and Mental Health Functioning: Buffering Factors of Young Black Men's Externalizing Behavior.

Depression rates are disproportionately high among Black American Men. This disparity--compounded by low mental healthcare seeking rates and high incorrect diagnosis rates in men--could be related to masculine norms, including self-reliance, restrictive emotionality, and stoicism. Furthermore, men are more likely to engage in externalized behavior, such as aggression, to cope with mental health challenges; this pattern is influenced by cultural and environmental factors. Contrary to these detrimental factors, social relationships, belief in social networks, and collectivism have been associated with positive mental health in these populations. Similarly, an Afrocentric worldview (including concepts like Ubuntu and African self-consciousness) has been hypothesized to promote positive mental health outcomes among Black American men. However, little research exists on harnessing these factors as a means of increasing health-seeking behaviors in young Black males. To elucidate the effect of region, depression, African humanism, collectivism, and help-seeking values and needs concerning aggression in young Black males. This study included Black or African American participants (n = 428) identifying as male, aged 18-25 years, who responded to a Qualtrics survey with questions on region, aggression, depression, African humanism, collectivism, and help-seeking value and need. Hierarchical linear regression revealed that collectivism, humanness, value, and the need for seeking treatment were inversely associated with aggression (p < 0.001). Highlighting the effect of cultural norms and help-seeking behaviors and the aggravating effect of depression on aggression in young Black males can help to develop aggression-mitigating interventions rooted in Afrocentric Norms.

R&D subsidies, tax reductions and innovation outsourcing of enterprises

As an important part of the innovation behavior of enterprises, innovation outsourcing is characterized by high technology, high threshold, and industry–university–research integration. Its status in the process of China's scientific and technological development has been continuously improved and it has received increasing attention and support through the government's innovation policies. This paper systematically tests the impact of research and development (R&D) subsidies and tax reductions on enterprises' innovation outsourcing. The results show that R&D subsidies and tax reductions can significantly increase enterprises' total amount and share of innovation outsourcing investment. An internal mechanism test shows that R&D subsidies can promote enterprises' innovation outsourcing by improving the adequacy of R&D funds and the quality of R&D staff. In addition, compared to the eastern region and non-state-owned enterprises, government subsidies have a more significant driving effect on innovation outsourcing in the central and western regions and for state-owned enterprises. The conclusions drawn in this paper provide an important empirical basis and policy reference for Chinese enterprises' internal and external innovation behaviors in the context of government innovation policies.

Impact of External Auditors’ Ethical Behavior on Audit Quality: A Client Perspective

Purpose: This study explores the impact of external auditors' ethical behavior on audit quality, with a particular focus on the perspectives of internal auditors in publicly listed Pakistani companies. It aims to address the gap in literature concerning the interplay between ethical behavior, corporate ethical values, and perceived audit quality. Design/Methodology/Approach: Using Structural Equation Modeling (SEM), the study examines the direct and indirect effects of external auditors' ethics on audit quality, with corporate ethical values as a moderating factor. Data were collected via a mail survey and Google Forms from auditors in listed Pakistani firms, yielding 232 usable responses (61% response rate). The analysis utilized SPSS and AMOS for reliability, validity, factor analysis, regression, and correlation. Findings: Results reveal a positive relationship between ethical behavior and perceived audit quality, amplified by strong corporate ethical values. Companies prioritizing ethics reported enhanced audit quality, providing valuable insights for regulators and auditors. Implications/Originality/Value: This research contributes to the understanding of ethical behavior in audit quality, offering actionable insights for improving ethical standards in audit practices. The findings are pertinent for regulatory bodies, auditors, and policymakers aiming to strengthen financial disclosure integrity in Pakistan

Disruption of network hierarchy pattern in bulimia nervosa reveals brain information integration disorder

The human brain works as a hierarchical organization that is a continuous axis spanning sensorimotor cortex to transmodal cortex (referring to cortex that integrates multimodal sensory information and participates in complex cognitive functions). Previous studies have demonstrated abnormalities in several specific networks that may account for their multiple behavioral deficits in patients with bulimia nervosa (BN), but whether and how the network hierarchical organization changes in BN remain unknown. This study aimed to investigate alterations of the hierarchy organization in BN network and their clinical relevance. Connectome gradient analyses were applied to depict the network hierarchy patterns of fifty-nine patients with BN and thirty-nine healthy controls (HCs). Then, we evaluated the network- and voxel-level gradient alterations of BN by comparing gradient values in each network and each voxel between patients with BN and HCs. Finally, the association between altered gradient values and clinical variables was explored. In the principal gradient, patients with BN exhibited reduced gradient values in dorsal attention network and increased gradient values in subcortical regions compared to HCs. In the secondary gradient, patients with BN showed decreased gradient values in ventral attention network and increased gradient values in limbic network. Regionally, the areas with altered principal or secondary gradient values in BN group were mainly located in transmodal networks, i.e., the default-mode and frontoparietal network. In BN group, the principal gradient values of right inferior frontal gyrus were negatively associated with external eating behavior. This study revealed the disordered network hierarchy patterns in patients with BN, which suggested a disturbance of brain information integration from attention network and subcortical regions to transmodal networks in these patients. These findings may provide insight into the neurobiological underpinnings of BN.

The Moderator Effect of Internal Prosocial Behaviors on the Relationship Between Helicopter Parenting and External Prosocial Behaviors

The rate of helicopter parenting, which is one of the parenting styles, has been increasing in recent years and this style might have negative effects on the individuals' lives. Prosocial behaviors are the actions expected to be displayed in the society and the attitudes of the parents are effective on these behaviors. It is important to determine these effects to contribute to the healthy development of individuals. Thus, this study aimed to investigate the gender differences in helicopter parenting and prosocial behaviors, to reveal the relationships between age and helicopter parenting and prosocial behaviors, and to determine whether internal prosocial behaviors acted as a moderator in the relationship between maternal/paternal helicopter parenting and external prosocial behaviors. Path analysis, which is an advanced statistical model, was used to determine the moderator effect of internal prosocial behaviors. Data were collected from 363 adolescents with a mean age of 16.08 years. Data were obtained with the "Helicopter Parent Attitude/s Scale" and "Adolescent Prosociality Scale". The results showed that adolescents girls exhibited more prosocial behaviors than boys. There was an inverse and significant relationship between age and paternal helicopter parenting. Internal prosocial behaviors acted as a moderator in the relationship between maternal/paternal helicopter parenting and external prosocial behaviors. The findings of the current study suggest that helicopter parenting has an adverse impact on external prosocial behaviors and internal prosocial behaviors may regulate this relationship.

Analyzing Energy Efficiency and Battery Supervision in Electric Bus Integration for Improved Urban Transport Sustainability

Addressing the critical challenge of reducing local emissions through the electrification of urban public transport, this research specifically focuses on integrating electric buses. The primary objectives are to evaluate energy efficiency and ensure battery cell supervision. Introducing electric buses plays a significant role in reducing emissions, contributing to more sustainable urban transport systems. However, this transition introduces a set of new challenges, including the complexities of electric charging logistics, the establishment of new consumption standards, and the intricate relationships between distance traveled, ambient temperature, passenger load, and battery health. Methodologically, this study collects and examines factors impacting energy consumption, including external temperatures, bus conditions, road conditions, and driver behavior. By analyzing these variables, a baseline for actual consumption can be established, allowing for the calculation of an energy balance to identify energy inefficiencies. This enables the optimization of route planning, the strategic selection of stops, and the efficient scheduling of charging times, along with ensuring the proper scaling of the bus battery system. This study found that energy consumption peaked at 116.73 kWh/100 km in the lowest temperature range of −5 °C to 0 °C. Consumption decreased significantly with rising temperatures, dropping by 25 kWh between 5 °C and 10 °C and by an additional 10 kWh between 10 °C and 15 °C. Beyond 20 °C, variations were more influenced by route and driving style than by temperature. Route and driver variability significantly influenced energy consumption, with up to threefold differences across routes due to factors such as road type and traffic volume. Additionally, there was a 31.85% difference between the most and least efficient drivers, highlighting the critical impact of driving style. Furthermore, this study explores the assessment of battery systems through cell-level diagnostics to detect potential faults. Considering that buses are equipped with significantly more batteries than typical electric vehicles, detecting and localizing faults at the cell level is crucial to avoid the substantial costs and environmental impact associated with replacing large battery systems. Utilizing the results of this research and the applied examination methods, it is possible to enhance energy efficiency and extend battery life, thereby contributing to the development of more sustainable and cost-effective urban transport solutions.

Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure

AbstractNeuromorphic devices capable of emulating biological synaptic behaviors are crucial for implementing brain‐like information processing and computing. Emerging 2D ferroelectric neuromorphic devices provide an effective means of updating synaptic weight aside from conventional electrical/optical modulations. Here, by further synergizing with an energy‐efficient synaptic plasticity strategy, a multimodal mechano‐photonic synaptic memory device based on 2D asymmetric ferroelectric heterostructure is presented, which can be modulated by external mechanical behavior and light illumination. By integrating the asymmetric ferroelectric heterostructured field‐effect transistor and a triboelectric nanogenerator, the mechanical displacement‐derived triboelectric potential is ready for gating, programming, and plasticizing the synaptic device, resulting in superior electrical properties of high on/off ratios (&gt; 107), large storage windows (equivalent to ≈95 V), excellent charge retention capability (&gt; 104 s), good endurance (&gt; 103 cycles), and primary synaptic behaviors. Besides, optical illumination can effectively synergize with mechanoplasticity to implement multimodal spatiotemporally correlated dynamic logic. The demonstrated multimodal memory synapse provides a facile and promising strategy for multifunctional sensory memory, interactive neuromorphic devices, and future brain‐like electronics embodying artificial intelligence.

Detecting refactoring type of software commit messages based on ensemble machine learning algorithms

Refactoring is a well-established topic in contemporary software engineering, focusing on enhancing software's structural design without altering its external behavior. Commit messages play a vital role in tracking changes to the codebase. However, determining the exact refactoring required in the code can be challenging due to various refactoring types. Prior studies have attempted to classify refactoring documentation by type, achieving acceptable results in accuracy, precision, recall, F1-Score, and other performance metrics. Nevertheless, there is room for improvement. To address this, we propose a novel approach using four ensemble Machine Learning algorithms to detect refactoring types. Our experimentation utilized a dataset containing 573 commits, with text cleaning and preprocessing applied to address data imbalances. Various techniques, including hyperparameter optimization, feature engineering with TF-IDF and bag-of-words, and binary transformation using one-vs-one and one-vs-rest classifiers, were employed to enhance accuracy. Results indicate that the experiment involving feature engineering using the TF-IDF technique outperformed other methods. Notably, the XGBoost algorithm with the same technique achieved superior performance across all metrics, attaining 100% accuracy. Moreover, our results surpass the current state-of-the-art performance using the same dataset. Our proposed approach bears significant implications for software engineering, particularly in enhancing the internal quality of software.

Driving engagement in metaverse-mediated tourism environments: exploring the role of perceived realism

PurposeDespite the promising development and marketing potential of the metaverse, our understanding of how realistic metaverse environments impact user engagement and behaviours remains limited. This study investigates the role of perceived realism in influencing user engagement, thereby affecting external search behaviour and visit intentions.Design/methodology/approachWe surveyed 270 active metaverse users to test the research model. The data were analysed using partial least squares structural equation modelling (PLS-SEM).FindingsThe results of our study show that three dimensions of realism – avatar involvement, perceptual pervasiveness and social realism – significantly enhance user engagement, which in turn influences external search behaviour and visit intention. In contrast, simulation realism and freedom of choice have minimal effects on absorption and dedication.Research limitations/implicationsThis study highlights the role of perceived realism in enhancing user engagement with the metaverse and its impact on physical world behaviours. It contributes to metaverse literature by demonstrating that engagement within the metaverse significantly influences physical world behaviours, including visit intentions and external search behaviours.Practical implicationsThis study offers practical guidance for developers to enhance user engagement in metaverse environments. Specifically, our findings advocate for visual and audio quality enhancements, greater persuasiveness of virtual spaces, improved avatar representativeness and a closer alignment of metaverse activities with real-life events.Originality/valueThis study advances the theoretical understanding of perceived realism by examining how its dimensions – such as visual and audio quality, avatar representativeness and alignment with real-life events – impact user engagement in the metaverse. It also explores how this engagement influences offline behaviours, thus bridging the gap between virtual and real-world interactions.

Optoelectronic synapses with chemical-electric behaviors in gallium nitride semiconductors for biorealistic neuromorphic functionality

Optoelectronic synapses, leveraging the integration of classic photo-electric effect with synaptic plasticity, are emerging as building blocks for artificial vision and photonic neuromorphic computing. However, the fundamental working principles of most optoelectronic synapses mainly rely on physical behaviors while missing chemical-electric synaptic processes critical for mimicking biorealistic neuromorphic functionality. Herein, we report a photoelectrochemical synaptic device based on p-AlGaN/n-GaN semiconductor nanowires to incorporate chemical-electric synaptic behaviors into optoelectronic synapses, demonstrating unparalleled dual-modal plasticity and chemically-regulated neuromorphic functions through the interplay of internal photo-electric and external electrolyte-mediated chemical-electric processes. Electrical modulation by implementing closed or open-circuit enables switching of optoelectronic synaptic operation between short-term and long-term plasticity. Furthermore, inspired by transmembrane receptors that connect extracellular and intracellular events, synaptic responses can also be effectively amplified by applying chemical modifications to nanowire surfaces, which tune external and internal charge behaviors. Notably, under varied external electrolyte environments (ion/molecule species and concentrations), our device successfully mimics chemically-regulated synaptic activities and emulates intricate oxidative stress-induced biological phenomena. Essentially, we demonstrate that through the nanowire photoelectrochemical synapse configuration, optoelectronic synapses can be incorporated with chemical-electric behaviors to bridge the gap between classic optoelectronic synapses and biological synapses, providing a promising platform for multifunctional neuromorphic applications.

Engineering transmitted light speckle statistics with field-tunable magnetic nanofluid scatterers

The active manipulation of electromagnetic wave scattering cross-sections within disordered media is crucial for understanding mesoscopic physics. An adaptable strategy for exploring the parameter space in order to control the scattering cross-section of such systems involves employing external field-tunable disorder. In this study, we utilize magnetic nanofluid as an in-situ tunable scattering medium, whose scattering parameters like scatterer number, density, size, and shape can be adjusted by an external magnetic field which is administered either constantly or with varying ramp rates. Our study on the transmitted light and the associated speckle statistics reveals intriguing behavior in response to changes in the strength and duration of the applied magnetic field. We observe that this tunable system remains within the weak scattering regime across various scattering scenarios: Rayleigh (for scatterer sizes smaller than the light wavelength, λ), Mie (when scatterer size approaches λ), and Geometric (for scatterer sizes exceeding λ) under both constant and ramped magnetic field application. Further, the transmitted speckle patterns deviate from Rayleigh behavior, with speckle contrast values increasing over time until it reaches a saturation point. However, the speckle patterns display intriguing dynamics with faster ramp rates resulting in larger speckle contrast values. This phenomenon arises from the inability of equilibrium field-induced structures to form rapidly under faster ramp rates, leading to a broader size distribution of scatterers and consequently a wider range of phase differences among scattered light components. Our study illuminates the dynamic behavior of wave transport through a tunable disordered media and underscores the potential of magnetic nanofluid as a versatile tool for controlling mesoscopic transport.

Exploring the Unmet Needs of Teachers of Young Children with ADHD Symptoms: A Qualitative Study.

Children with Attention and Hyperactivity Deficit Disorder (ADHD) and those at risk of ADHD typically exhibit challenging behaviours that may disrupt the classroom environment and be frustrating for teachers. This study aimed to explore teachers' experiences and emotions regarding teaching children with high levels of ADHD symptoms and their perceptions of what might help to meet their unmet support needs. Semi-structured interviews were conducted with 17 primary educational practitioners for children aged between four and eight years in the UK. Interview scripts were analysed using reflexive thematic analysis. Six main themes and 8 subthemes were developed. These included: (1) ADHD behaviours can disrupt the learning environment; (2) teachers face practical demands on their expertise and particular skills; (3) managing ADHD behaviours can be overwhelming for teachers; (4) teachers and children may treat children with ADHD negatively, which can impact on children's emotions and lead to labelling; (5) existing support for teachers is limited; and (6) teachers need more specific training about ADHD. The analysis revealed that teachers working with children with externalising behaviours such as ADHD can feel overwhelmed. The findings from this study suggest that teachers require more training in managing externalising behaviour in the classroom.

Multilingual code refactoring detection based on deep learning

Refactoring is a critical process of improving the internal structure of the source code without altering its external behavior. Existing deep learning-based refactoring detection relies on commit messages to extract features. However, these commit messages are not trustful enough since some developers do not consistently record refactoring activities. Furthermore, current approaches are designed for a single programming language and lack multilingual refactoring support. To this end, this paper proposes RefT5, a multilingual code refactoring detection approach based on deep learning. Firstly, we select 110 real-world projects with Java and Python programming languages as a corpus to construct the dataset. Secondly, we extract features including commit messages, code changes, and refactoring types from these projects. RefT5 generates edit sequences from code changes and takes refactoring types as labels. Thirdly, we employ CodeT5 and BiLSTM-attention to extract semantic and structural features and generate feature vectors. Finally, the feature vectors are input into a classification layer to detect the refactoring type. The experimental results show that RefT5 obtains 98.05% precision and 97.77% recall. Furthermore, compared with existing approaches, it improves precision by 51.61% and recall by 52.9% on average, demonstrating its effectiveness.

Modeling quick autonomous response for virtual characters in safety education games

Serious games have a wide range of applications. Modeling virtual character behaviors and emotions is a challenging task in developing serious games. To generate real-time responses, behavioral and emotional models must be simple and effective. Existing studies have paid little attention to the semantic understanding of virtual characters to external stimuli and have not effectively linked perceived semantics and motivation. This paper proposes a cognitive structure for the virtual character. The structure contains multiple modules: perception, personality, motivation, behavior, and emotion. Based on psychological theory, a semantic table that connects external stimuli, motivations, behaviors, and emotions is designed for each virtual character. Perceptivity is introduced to measure the degree of perception. According to Maslow’s motivation theory, a quantitative description of motivation is given and a discriminating method is proposed to generate behaviors and emotions. A prototype of a serious game is developed to verify the validity of the proposed method. The experimental results show that the proposed method can simulate the behavior and emotion of virtual characters in real time and will enhance the immersion of serious games.

External stress switching water corrosion behavior of SiC (C-10.2)

External stress switching water corrosion behavior of SiC (C-10.2)

The Impact of K-pop Merchandise on the Consumer Behavior of University Students in Shanghai

K-pop merchandise, as one of the main trends in current fashion development, is continuously expanding its audience and influence, particularly among teenagers. This paper conducts a comprehensive study on the impact of K-pop merchandise on the consumer behavior of university students in Shanghai through surveys and interviews. It aims to analyze the consumption behaviors and preferences of these students concerning K-pop merchandise, thereby exploring the significant effects of K-pop merchandise on this student group. The study finds that the consumption behavior of Shanghai university students regarding K-pop merchandise has long-term characteristics. Their purchasing decisions are directly influenced by their internal economic levels and indirectly by the external behaviors of fan communities. However, the cognitive and rational levels of university students are also improving, and their consumption behavior towards K-pop merchandise is shifting towards more rational and value-based decision-making. Overall, the influence and reach of K-pop merchandise are growing, and it is expected to have a sustained impact on the consumption behavior of university students in Shanghai in the future.