• Journal of Korean Society for Quality Management
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• v.52 no.1
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• pp.23-42
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• 2024

Purpose: This study aims to investigate the impact of two types of information quality, which are platform-oriented information quality and customer-oriented information quality, on customers' decision-making processes in the Online to offline (O2O) platform environment. Grounded in the product brokering efficiency model, which encompasses screening cost, evaluation cost, and decision quality, a model framework was developed. Furthermore, this study explores how these decision-making processes affect customer loyalty. Methods: Given that food delivery apps are the most widely used O2O service in Korea, this study targeted users of these apps for data analysis. We conducted hypothesis testing through a purposive sampling methodology focusing on food delivery app users. A Partial Least Squares Structural Equation Modeling analysis was conducted to analyze the data. The data collection occurred via an online survey from October to December 2021, with a total of 212 respondents participating. Results: The results of this study revealed the significant role of information quality in helping customers' decision processes while using food delivery apps. Specifically, it was found that platform-oriented information positively influences decision quality, while customer-oriented information significantly affects both the reduction of evaluation cost and the enhancement of decision quality. Additionally, the study indicated that lower evaluation costs and higher decision quality lead to increased platform loyalty. However, a reduction in screening cost did not have a significant impact on platform loyalty. Conclusion: While previous studies have overlooked the existence of two sides, service provider and user, in a platform, this research holds significance in its analysis of how information quality impacts loyalty by utilizing the two kinds of information quality. Practitioners can enhance customer loyalty to the platform by enriching customer-oriented information, thereby reducing customers' evaluation costs and encouraging more loyal usage of the platform.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.1
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• pp.13-18
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• 2008

Purpose: The image processing method due to the algorism which is various portion nuclear medical image decision is important it makes holds. The purpose of this study is it applies hereupon new image processing method SIEMENS (made by Pixon co.) Onco. flash processing reconstruction and the comparison which use the image control technique of existing the clinical usefulness it analyzes with it evaluates. Materials & Methods: 1. Whole body bone scan-scan speed 20 cm/min, 30 cm/min & 40 cm/min blinding test 2. Bone static spot scan-regional view 200 kcts, 400 kcts for chest, pelvis, foot blinding test 3. 4 quadrant-bar phantom-20000 kcts visual evaluation 4. LSF-FWHM resolution comparison ananysis. Results: 1. Raw data (20 cm/min) & processing data (30 cm/min)-similar level image quality 2. Low count static image-image quality clearly improved at visual evaluation result. 3. Visual evaluation by quadrant bar phantom-rising image quality level 4. Resolution comparison evaluation (FWHM)-same difference from resolution comparison evaluation Conclusion: The study which applies a new method Onco. flash processing reconstruction, it will be able to confirm the image quality improvement which until high level is clearer the case which applies the method of existing better than. The new reconstruction improves the resolution & reduces the noise. This enhances the diagnostic capabilities of such imagery for radiologists and physicians and allows a reduction in radiation dosage for the same image quality. Like this fact, rising of equipment availability & shortening the patient waiting move & from viewpoint of the active defense against radiation currently becomes feed with the fact that it will be the useful result propriety which is sufficient in clinical NM.

• Korea Trade Review
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• v.47 no.3
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• pp.111-128
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• 2022

This study tried to verify the effect of situational consumer animosity on consumer attitudes and behaviors with the moderating effect of cultural internalization. For this purpose, 460 valid survey data were collected from Koreans. The causal and regulatory relationships between the variables were analyzed through confirmatory factor analysis and structural model analysis using AMOS with the collected data. It was confirmed that the national situational animosity had a negative effect on quality evaluation, while personal situational animosity did not. Also, the positive relationship between quality evaluation and purchase intention was confirmed. In addition, it was found that internalization of individual culture moderates the effect of quality evaluation on purchase intention.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.3
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• pp.234-243
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• 2012

In this paper, a sound quality indices for the evaluation of vehicle intake and exhaust noise were developed through a correlation analysis of objective measurement data and subjective evaluation data. At first, intake and exhaust orifice noise were measured at the wide-open throttle sweep condition. And then, acoustic transfer function between intake orifice noise and interior noise at the steady state condition was measured. Also, acoustic transfer function for exhaust system was measured as the same method. Simultaneously, subjective evaluation was carried out by the paired comparison and semantic differential method by 27 engineers. Next, the correlation analysis between the psycho-acoustic parameters derived from the measured data and the subjective evaluation was performed. The most critical factor was determined and the corresponding sound quality index for the intake and exhaust noise was obtained from the multiple factor regression method. At last, the effectiveness of the proposed index was investigated.

• Journal of the Korean Geotechnical Society
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• v.30 no.8
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• pp.25-37
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• 2014

In load and resistance factor design (LRFD) method, resistance factors are typically calibrated using resistance bias factors obtained from either only the data within ${\pm}2{\sigma}$ or the data except the tail values of an assumed probability distribution to increase the reliability of the database. However, the data selection approach has a shortcoming that any low-quality data inadvertently included in the database may not be removed. In this study, a data quality evaluation method, developed based on the quality of static load test results, the engineering characteristics of in-situ soil, and the dimension of aggregate piers, is proposed for use in constructing database. For the evaluation of the method, a total 65 static load test results collected from various literatures, including static load test reports, were analyzed. Depending on the quality of the database, the comparison between bias factors, coefficients of variation, and resistance factors showed that uncertainty in estimating bias factors can be reduced by using the proposed data quality evaluation method when constructing database.

• Korean Medical Education Review
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• v.22 no.2
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• pp.131-142
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• 2020

A systematic educational program evaluation system for continuous quality improvement in undergraduate medical education is essential. Monitoring and evaluation (M&E) are two distinct but complementary processes referred to in an evaluation system that emphasizes formative purpose. Monitoring involves regular data collection for tracking process and results, while evaluation requires periodic judgment for improvement. We have recently completed implementing an educational evaluation using the M&E concept in a medical school. The evaluation system consists of two loops, one at the lesson/course level and the other at the phase/graduation level. We conducted evaluation activities in four stages: planning, monitoring, evaluation, and improvement. In the planning phase, we clarified the purpose of evaluation, formulated a plan to engage stakeholders, determined evaluation criteria and indicators, and developed an evaluation plan. Next, during the monitoring phase, we developed evaluation instruments and methods and then collected data. In the evaluation phase, we analyzed results and evaluated the criteria of the two loops. Finally, we reviewed the evaluation results with stakeholders to make improvements. We have recognized several problems including excessive burden, lack of expertise, insufficient consideration of stakeholders' evaluation questions, and inefficient data collection. We need to share the value of evaluation and build a system gradually.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.193-207
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• 2021

As Deepfakes phenomenon is spreading worldwide mainly through videos in web platforms and it is urgent to address the issue on time. More recently, researchers have extensively discussed deepfake video datasets. However, it has been pointed out that the existing Deepfake datasets do not properly reflect the potential threat and realism due to various limitations. Although there is a need for research that establishes an agreed-upon concept for high-quality datasets or suggests evaluation criterion, there are still handful studies which examined it to-date. Therefore, this study focused on the development of the evaluation criterion for the Deepfake video dataset. In this study, the fitness of the Deepfake dataset was presented and evaluation criterions were derived through the review of previous studies. AHP structuralization and analysis were performed to advance the evaluation criterion. The results showed that Facial Expression, Validation, and Data Characteristics are important determinants of data quality. This is interpreted as a result that reflects the importance of minimizing defects and presenting results based on scientific methods when evaluating quality. This study has implications in that it suggests the fitness and evaluation criterion of the Deepfake dataset. Since the evaluation criterion presented in this study was derived based on the items considered in previous studies, it is thought that all evaluation criterions will be effective for quality improvement. It is also expected to be used as criteria for selecting an appropriate deefake dataset or as a reference for designing a Deepfake data benchmark. This study could not apply the presented evaluation criterion to existing Deepfake datasets. In future research, the proposed evaluation criterion will be applied to existing datasets to evaluate the strengths and weaknesses of each dataset, and to consider what implications there will be when used in Deepfake research.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.714-721
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• 2018

This study proposes a risk evaluation method based on RAM and AHP data in order to prevent subjectivity of risk assessment. The risk assessment consist of Risk Likelihood(RL) and Risk Consequence(RC) in five levels. However, risk analysis of warships is hard to make a judgment because of small quantity production(Ship), long building period, equipment changes, complexity, various kinds of equipments, etc. The proposed RAM data and AHP analysis method are used to quantify each level quantitatively. RAM(MTBF) date is used to classify the RL, and AHP analysis is used to classify the RC. These scientific and data-based method will increase objectivity as well as efficiency of risk evaluation.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.109-124
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• 2000

For rational human value judgement and evaluation, provision of clear evaluation data, objective value judgement criteria, and properly generalized methods are required. For instance, this is true for software quality evaluation, and the measure of software quality and the weighting method of evaluation target directly affect final decisions. However it is not easy to find a generalized method for the software quality evaluation or product selection, because of its complex characteristics. In this paper, we apply the qualitative preference method based on quantitative belief functions to find a general weighing method for the software quality evaluation. In particular, the qualitative preference method, in which the differentiated preference expression is possible, is conceptually expanded for general applications in future. For this purpose, we hierarchically differentiate the strong preference relation from the weak preference relation, and show an example of quantification of software quality evaluation on different applications, by comparing the qualitative preference method with AHP. We believe that the application domain of this method is not limited to the software quality evaluation and it is very useful to apply this results to other SE areas, e.g., metric selection with different views and riority determination of practices to be assessed in the SPICE.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.28-34
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• 2022

Many studies have been conducted on software quality evaluation. Recently, as artificial intelligence-related software has been developed a lot, research on methods for evaluating artificial intelligence functions in existing software is being conducted. Software evaluation has been based on eight quality characteristics: functional suitability, reliability, usability, maintainability, performance efficiency, portability, compatibility, and security. Research on the part that needs to be confirmed through evaluation of the function of the intelligence part is in progress. This study intends to introduce the contents of the evaluation method in this part. We are going to propose a quality evaluation method for artificial intelligence software by presenting the existing software quality evaluation method and the part to be considered in the AI part.