• Information Systems Review
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• v.23 no.3
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• pp.127-158
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• 2021

Many online travel agencies (OTAs) provide average ratings and time-relevant information or the most recently posted reviews regarding hotels to satisfy customers. To identify these two factors' relative influence on behavioral decision-making processes, we conducted two studies: (1) an experimental research design to explore the relative influence of the two on online review consumption and (2) an empirical approach to examine their relative impact on online review generation. The results show that when review posters observe an inconsistency between average ratings and recent reviews, they tend to deviate from the recent reviews regardless of the overall direction (reactance behavior). Meanwhile, review consumers tend to conform to the opinions presented in recent reviews (herding behavior). Additionally, in both cases, the effects are amplified in case of a negative aberration. Based on the findings, this study provides theoretical and practical implications regarding the relative influences of average rating and recently posted reviews and their different impacts on online review consumption and generation.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.21-29
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• 2024

This study analyzed the difficulty of scoring in different match situations in relation to which competitor scored first. The study analyzed the data from the 2022 Guadalajara World Taekwondo Championships. The analysis was performed for two separate weight classes: lightweight and heavyweight. Four game content variables were used: whether the athlete scored first, attack type, attack area, and game situation. Descriptive statistics, the Rasch model, and discrimination function questions were applied for data processing. SPSS and Winsteps were used for the statistical analysis, and the statistical significance level was set at 0.05. Consequently, in the lightweight class, the scoring frequency of the first scorer was high for all the game variables. In the heavyweight class, the scoring frequency for the first scorer was high for the attack type and attack area. By contrast, those who did not score first were more frequently found to be in a loss situation. By analyzing the scoring difficulties in different match situations based on whether the competitor scored first, the athletes who scored first in attack type most easily scored first. In losing situations, the athletes who scored first in attack area scored most easily, whereas those who did not score first scored most easily in body and match situations. For the heavyweight class, those who scored first in terms of attack type, counter-attack, and attack area scored the most easily while winning in body and match situations.

• Journal of Industrial Convergence
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• v.22 no.4
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• pp.57-64
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• 2024

This study was to confirm the effect of emotional intelligence on organizational commitment of general hospital nurses and the mediating effect of affect. Data was collected using a structured online self-report questionnaire on emotional intelligence, organizational commitment, and both negative and positive affect from 236 nurses working in general hospitals. The data collected were analyzed using descriptive statistics, Pearson's correlation, and a parallel multiple mediation model. This analysis was conducted with IBM SPSS Statistics (Version 27.0) and the PROCESS macro (Model 4). This study's findings revealed that emotional intelligence was significant correlated with positive affect, negative affect, and organizational commitment. Emotional intelligence had a significant direct effect on positive affect (β=.16, p=.015), negative affect (β=-.28, p<.001), and organizational commitment (β=.33, p<.001). Positive affect (β=.20, p=.001) and negative affect (β=-.25, p<.001), had a significant direct effect on organizational commitment. And the mediating effect of positive affect (β=.03, 95% bootstrap CI=0.01~0.07) and negative affect (β=.07, 95% bootstrap CI=0.03~0.12) was also significant. Based on these research results, it will be necessary to research various training programs that can manage emotional intelligence and affect together in developing programs to improve nurses' organizational commitment.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.751-758
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• 2021

Objective: Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis. Materials and Methods: In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error. Results: Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65-95%), 100% (41 out of 41; 95% CI, 91-100%), 95% (59 out of 61; 95% CI, 87-98%), and 0.966 (95% CI, 0.912-1.000), respectively. Conclusion: Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1850-1857
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• 2021

Objective: The purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT. Materials and Methods: This retrospective study included 52 patients (26 male and 26 female; median age, 60.5 years) who had undergone CT-guided lumbar bone biopsy between October 2015 and April 2020. Initial 100-mAs survey images and 50-mAs intraprocedural images were reconstructed by filtered back projection. Denoising was performed using a vendor-agnostic DL model (ClariCT.AI^TM, ClariPI) for the 50-mAS images, and the 50-mAs, denoised 50-mAs, and 100-mAs CT images were compared. Noise, signal-to-noise ratio (SNR), and edge rise distance (ERD) for image sharpness were measured. The data were summarized as the mean ± standard deviation for these parameters. Two musculoskeletal radiologists assessed the visibility of the normal anatomical structures. Results: Noise was lower in the denoised 50-mAs images (36.38 ± 7.03 Hounsfield unit [HU]) than the 50-mAs (93.33 ± 25.36 HU) and 100-mAs (63.33 ± 16.09 HU) images (p < 0.001). The SNRs for the images in descending order were as follows: denoised 50-mAs (1.46 ± 0.54), 100-mAs (0.99 ± 0.34), and 50-mAs (0.58 ± 0.18) images (p < 0.001). The denoised 50-mAs images had better edge sharpness than the 100-mAs images at the vertebral body (ERD; 0.94 ± 0.2 mm vs. 1.05 ± 0.24 mm, p = 0.036) and the psoas (ERD; 0.42 ± 0.09 mm vs. 0.50 ± 0.12 mm, p = 0.002). The denoised 50-mAs images significantly improved the visualization of the normal anatomical structures (p < 0.001). Conclusion: DL-based reconstruction may enable simultaneous noise reduction and improvement in image quality with the preservation of edge sharpness on low-dose lumbar spine CT. Investigations on further radiation dose reduction and the clinical applicability of this technique are warranted.

• Advanced Industrial SCIence
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• v.3 no.2
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• pp.8-16
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• 2024

This paper shows the system of drug classification, the goal of this is to foretell the apt drug for the patients based on their demographic and physiological traits. The dataset consists of various attributes like Age, Sex, BP (Blood Pressure), Cholesterol Level, and Na_to_K (Sodium to Potassium ratio), with the objective to determine the kind of drug being given. The models used in this paper are K-Nearest Neighbors (KNN), Logistic Regression and Random Forest. Further to fine-tune hyper parameters using 5-fold cross-validation, GridSearchCV was used and each model was trained and tested on the dataset. To assess the performance of each model both with and without hyper parameter tuning evaluation metrics like accuracy, confusion matrices, and classification reports were used and the accuracy of the models without GridSearchCV was 0.7, 0.875, 0.975 and with GridSearchCV was 0.75, 1.0, 0.975. According to GridSearchCV Logistic Regression is the most suitable model for drug classification among the three-model used followed by the K-Nearest Neighbors. Also, Na_to_K is an essential feature in predicting the outcome.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.137-148
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• 2024

The purpose of this study is to determine how the use of retailtech technology affects consumers' purchase intention. Furthermore, this study aims to investigate the mediating effects of technology usefulness and ease of use on this influence relationship and whether experiential marketing moderates consumers' purchase intention. The survey was conducted from August 1, 2023 to September 30, 2023, and a total of 257 people participated in the study. For statistical analysis, hierarchical regression analysis, three-stage mediation regression analysis, and hierarchical three-stage controlled regression analysis were conducted to test the hypothesis. The results of the study are as follows. First, it was confirmed that big data-AI utilization, mobile-SNS utilization, live commerce utilization, and IoT utilization affect purchase intention in retail technology utilization. Second, technology usefulness has a mediating effect on IoT utilization, mobile-SNS utilization, and big data-AI utilization. Third, perceived ease of use of technology mediated the effects of IoT utilization, mobile-SNS utilization, live-commerce utilization, and big data-AI utilization. Fourth, escapist experience has a moderating effect on mobile SNS utilization and live commerce utilization. Fifth, esthetic experience has a moderating effect on mobile-SNS utilization and big data-AI utilization. Through this study, we hope that the domestic distribution industry will contribute to national competitiveness by securing the competitive advantage of companies by utilizing new technologies in entering the global market.

• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.75-86
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• 2024

Objective : The purpose of this study was to examine the effects of an occupational therapy program on the school readiness, focusing on adaptation skills and daily life skills, in children with developmental delays. Methods : The study involved a boy with developmental delay, aged 5 years and 8 months. The program was conducted twice a week, with a total of 8 sessions spread over 4 weeks. The Canadian Occupational Performance Measure (COPM) was employed, targeting class preparation and use of the toilet. Pre-post tests and follow-up evaluations were carried out to compare changes. Data analysis involved video recordings of the subject's performance. Results : The COPM results indicated improvements in both the performance and satisfaction levels for class preparation and toilet use. Processing skills showed seven improvements in class preparation and eight improvements in toilet use during post-testing. Activity performance observations further confirmed improvements in both class preparation and toilet use during post-test and follow-up evaluations. Conclusion : Occupational therapy improves school readiness (adaptation skill, daily living activity skill) for children with developmental delays, and has a positive effect on overall school readiness.

• Journal of Industrial Convergence
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• v.22 no.5
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• pp.69-78
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• 2024

This study verified the relationship between marital conflict perceived by working mothers and the influence of children's cyber delinquency and the mediating effect of school life adaptation. For the analysis data, the 14th data of the Korean Children's Panel (2021) were used, and SPSS WIN 23.0 and PROCESS MACRO were used for analysis. The analysis method used the PROCESS MACRO Model Number 4 technique to verify the hypothesis, first, frequency analysis to understand the demographic and sociological characteristics of the survey subjects, second, technical statistics, correlation analysis to understand the technical statistics and correlations of major variables. As a result of the main analysis, first, it was found that marital conflict perceived by working mothers did not have a statistically significant effect on children's cyber delinquency. Second, in the relationship between marital conflict perceived by working mothers and cyber delinquency of children, school life adaptation had a complete mediating effect. Based on these results, it is significant in that it has presented basic data to prevent cyber delinquency problems before children who are active a lot online are exposed.

• Journal of Industrial Convergence
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• v.22 no.5
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• pp.1-10
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• 2024

This study proposes a developmental policy direction for the online platform industry, moving away from the regulatory-centered discussions that have predominated thus far. To offer policy directions, the PEST-SWOT-AHP analysis model was employed. The study first categorizes the issues of the domestic online platform industry into political, economic, social, and technological aspects, which are then further categorized into 16 strengths, weaknesses, opportunities, and threats. The relative importance among these factors was measured, leading to the derivation of four final strategies. The analysis indicates that policy directions should prioritize addressing weaknesses, with 'improving regulations that hinder innovation' being the most important factor across all categories, while technological factors were consistently rated highly in importance apart from this. Accordingly, the policy direction for the domestic online platform industry suggests avoiding excessive regulation and instead emphasizing policy support centered around technological development. This study is significant in that it presents a macroscopic developmental direction for online platform policies that have not been discussed in existing academic research, and it provides professional and objective indicators through consensus among scholars and researchers. In the future, it is hoped that research will continue to propose detailed policy strategies and implementation systems based on a macroscopic perspective.