Earticle

Purpose: We aimed to identify factors influencing self-esteem among elementary school students and to provide foundational data for the development of effective intervention programs. Methods: We conducted an online survey between January 16 and February 4, 2025, targeting students in grades 4 to 6. Data were analyzed using independent t-tests, one-way ANOVA, Pearson’s correlation coefficients, and multiple regression analysis with SPSS version 27.0. Results: We found that self-esteem was significantly associated with body mass index (BMI), stress, dietary self-efficacy, and physical self-efficacy. Among these variables, BMI emerged as the strongest predictor of self-esteem (Adj. R² = .930). Conclusion: Our findings underscore the importance of maintaining a healthy BMI through dietary management and physical activity as a means to enhance self-esteem during childhood. We contribute to the field by identifying BMI as a key modifiable factor in children’s psychological development and by providing evidence to support school-based intervention strategies. We acknowledge the limitations related to our geographically restricted sample and the exclusion of environmental factors, and we recommend future research incorporating more diverse populations and contextual variables.

There is a notable lack of research directly investigating the effect of positive psychological capital and mental health literacy on the quality of life in local residents. Therefore, our study aimed to investigate the effects of mental health literacy and positive psychological capital on the quality of life of residents in G city. To achieve this, the total number of subjects was 500, and the data collection period was from November 1, 2024 to December 11, 2024. The data analysis methods were frequency analysis, t-test, one-way ANOVA, Pearson’s correlation analysis, and step-wise multiple regression analysis using IBM SPSS program. The results of this study are as follows: 1) Quality of life showed a positive correlation with positive psychological capital and mental health literacy. 2) Influential factors on the quality of life were positive psychological capital (β=.70, p<.001), satisfaction with family income (β=.13, p<.001), marital status (β =.11, p<.001), physical health status (β=.10, p<.001), and mental health literacy (β=.07, p=.010), and the total explanatory power was 69.2% (F=224.73, p<.001). In conclusion, we found that positive psychological capital is the most significant factor influencing the quality of life through this study. Based on these findings, we suggest developing positive psychological capital and providing education to improve mental health literacy as ways to enhance quality of life. Additionally, we recommend taking measures to improve quality of life by considering economic, familial, and physical health conditions, which are known to be influential factors. However, since this study was conducted on residents of one local community, the generalizability of the findings is limited. Therefore, we recommend that further studies be conducted on residents in various regions.

This study investigates the determinants of review helpfulness and evaluates the predictive performance of traditional machine learning models and large language models (LLMs) using a 14-year dataset of 46,392 user-generated reviews from the OpenTable restaurant reservation platform. We compare four traditional machine learning (ML) classifiers—logistic regression, decision tree, random forest, and gradient boost tree— with a fine-tuned version of distilBERT, a lightweight large language model (LLM) based on bidirectional encoder representations from transformers (BERT). While previous studies on review helpfulness have primarily focused on surface-level features such as length, sentiment, or rating, we address a critical gap by incorporating both information diagnosticity and cognitive load as core theoretical perspectives. Specifically, we apply information diagnosticity theory (IDT) and cognitive load theory (CLT) to conceptualize helpful reviews as those that are both specific and cognitively accessible. Our findings show that distilBERT outperforms all baseline machine learning (ML) models in terms of precision and area under the curve (AUC), while maintaining computational efficiency. Topic modeling results further reveal that reviews featuring functional, clear, and experience-based content are more likely to be classified as helpful, whereas emotionally vague or technically dense reviews tend to be less effective. We contribute to the literature by showing how theory-informed large language model (LLM) can capture both diagnostic and cognitive dimensions of helpfulness—an area previously underexplored.

This study explores consumer motivations and perceived barriers to fashion rental services by analyzing 5,000 App Store reviews of Rent the Runway (RTR). Using a mixed-methods text mining approach—including LDA topic modeling, sentiment analysis with the AFINN lexicon, and time series-network analysis—the study identifies key value drivers such as convenience, variety, and emotional satisfaction, alongside risk factors like hidden fees, garment hygiene, and delivery issues. Six coherent topics reveal themes from premium access to service-related concerns, while sentiment analysis and temporal trends illustrate an emotional trajectory from initial excitement to skepticism and eventual trust recovery. Network visualization further maps shifts in consumer attitudes. Theoretically, this research extends value-risk frameworks by highlighting the joint role of cognitive and emotional appraisals in service adoption. Practically, it offers actionable insights for improving pricing transparency, service quality, and consumer trust. Methodologically, it demonstrates the value of user-generated reviews for capturing authentic consumer experiences beyond traditional survey methods.