• Asia Marketing Journal
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• v.15 no.4
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• pp.33-49
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• 2014

Environmental concern has been an important issue for a few decades, and the extent of consumer demand for eco-friendly consumption has been increased. This study seeks to investigate consumers' willingness to pay (WTP) a premium for eco-friendly cosmetics. This study evaluates how much more a consumer is willing to pay for eco-friendly cosmetics and examines significant factors influencing consumers' WTP for eco-friendly cosmetics. Consumers' WTP is measured using four different ecofriendly cosmetics: low-priced skin care cosmetics, low-priced makeup cosmetics, high-priced skin care cosmetics, and high-priced makeup cosmetics. This study uses the contingent valuation method (CVM) to estimate consumer's WTP for eco-friendly cosmetics. Survey questions were designed using both dichotomous choice and payment card method of CVM. Through face to face interviews and on-line surveys, the data were collected from women between 20 and 49 years old residing in Seoul and Kyeonggi area, Korea, in May 2010. A total of 226 questionnaires (132 from interviews and 94 from on-line) were included for the analytical sample in this study. The data were analyzed using descriptive analysis, T-test and Log-Logit analysis. The findings are as follows: First, the WTP measured by dichotomous choice method was estimated using the Log-Logit analysis. The results showed that the estimated WTP for low-priced skin care cosmetics was 19,152 won, which was 27.7% higher than the reference price, 15,000 won. For low-priced makeup cosmetics, the estimated WTP was 18,524 won, and its green premium was 21.0%. The estimated WTP for high-priced skin care cosmetics was 59,128 won, which was 18.3% higher than the reference price, 50,000 won. For high-priced makeup cosmetics was 57,666 won, and its green premium was 15.3%. Second, the WTP measure by payment card method was estimated using descriptive analysis. The results showed that the respondents were willing to pay 17,955 won for low-priced skin care cosmetics, which was 19.7% higher than the reference price, 15,000 won and 17,595 won for low-priced makeup cosmetics, which was 17.3% higher than the reference price. For high-priced skin care cosmetics, the average WTP was 56,950 won which was 13.9% higher than the reference price, 50,000 won. For high-priced makeup cosmetics, the average WTP was 55,650 won, which was 11.3% higher than the reference price. Overall, the WTP was higher in order of low-priced skin care, low-priced makeup, high-priced skin care, and high-priced makeup. It means that consumers decide degree of premium based on the price and the attributes of eco-friendly products. Third, the findings showed that age, monthly income, and having children or not were statistically significant factors that influenced consumers' willingness to pay for eco-friendly cosmetics. Other explanatory variables such as education, marital status, job, purchase experience of eco-friendly products, and environmental concerns did not show any statistical significance. The major contribution of this study is the investigation of the value of green attributes of the products by using CVM. Unlike most previous researches, this research used two methods of CVM, the dichotomous choice and the payment card, so it enhanced the reliability of research. According to this study, consumers showed price sensitivity when they pay green premium. These findings can be used as useful information to establish marketing strategies for green cosmetics.

• Asia Marketing Journal
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• v.11 no.3
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• pp.91-124
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• 2009

With the intensity of competition and the standardization of technical attributes in telecommunications service market increasing, differentiated activity and customer experience in service encounter is regarded as an important means for creating customer value, however, there is a dearth of good literature examining what MOT activity is composed of according to consumption chain, and how service quality of MOT has influenced customer performance. Especially there exist various services across different phase of Product life cycle(PLC) in household telecommunication service market, customer requirement for MOT might depend on whether its phase is introduction-growth stage or maturity-decline stage, the empirical study is completely lacking. This study classified household telecommunication services into two types by PLC, VOIP and IPTV as Introduction-growth stage services, Internet and PSTN as maturity-decline stage service, and investigated whether there exists a gap between service types in how consumer have experienced MOT, what they consider as important and the relative importance of quality dimension how service quality of MOT has influence on consumer performance. The empirical result from 858 participants shows that there is a difference in consumer experience and requirements across different phases of the PLC, tangibles and assurance are regarded as the most important service quality factors which have a positive influence on customer performance (consumer satisfaction, repurchase intention and word of mouth) at the introduction-growth stage, whereas, reliability, empathy and interactivity are at the maturity-decline stage. Finally, managerial implication is made, limitation is clarified and a direction for further studies is suggested.

• Journal of IKEEE
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• v.27 no.4
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• pp.486-493
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• 2023

In this paper, we propose an automatic hand gesture area extraction and recognition technique using FMCW radar-based point cloud and LSTM. The proposed technique has the following originality compared to existing methods. First, unlike methods that use 2D images as input vectors such as existing range-dopplers, point cloud input vectors in the form of time series are intuitive input data that can recognize movement over time that occurs in front of the radar in the form of a coordinate system. Second, because the size of the input vector is small, the deep learning model used for recognition can also be designed lightly. The implementation process of the proposed technique is as follows. Using the distance, speed, and angle information measured by the FMCW radar, a point cloud containing x, y, z coordinate format and Doppler velocity information is utilized. For the gesture area, the hand gesture area is automatically extracted by identifying the start and end points of the gesture using the Doppler point obtained through speed information. The point cloud in the form of a time series corresponding to the viewpoint of the extracted gesture area is ultimately used for learning and recognition of the LSTM deep learning model used in this paper. To evaluate the objective reliability of the proposed technique, an experiment calculating MAE with other deep learning models and an experiment calculating recognition rate with existing techniques were performed and compared. As a result of the experiment, the MAE value of the time series point cloud input vector + LSTM deep learning model was calculated to be 0.262 and the recognition rate was 97.5%. The lower the MAE and the higher the recognition rate, the better the results, proving the efficiency of the technique proposed in this paper.

• Journal of the Korean Geosynthetics Society
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• v.22 no.4
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• pp.17-26
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• 2023

To consider the risk of damage and fracture of P.C strands, the existing post-maintenance system alone has the limitations, hence it is necessary to quantitatively evaluate and predict the deterioration, durability and safety of facilities and establish a reasonable maintenance system considering the asset value of facilities. Therefore, it is worth considering a preventive maintenance plan that allows proactive measures to be taken before a major defect occurs in the temporary anchor. This study devised a preventive over tension method, reviewed its effectiveness through design and field tests, by calculating the resistance factors by performing a reliability-based optimization design. At this time, the over tension anchor method was evaluated using the ratio of the residual tension force after the fracture of P.C strands to the effective tension force before the fracture of P.C strand, followed by the resistance factor calculated by the optimal solution for each random variables using Excel solver and applying it to the limit state equations. As a result of the study, if the over tension ratio is 125% to 130%, the remaining strands showed a high resistance effect even after the fracture of P.C strand. As a result of the optimization design, it was found that it is appropriate to apply the load factor (γ) of 1.25, and the resistance factors of Φ₁, Φ₂, Φ₃ as 0.7, 0.5, 0.6.

• Journal of Venture Innovation
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• v.6 no.3
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• pp.73-93
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• 2023

This study provides a prospect for the fast growing the smartwatch market by investigating the relationship between the satisfaction and the continuous use intention of Apple watch users, as well as the factors influencing their satisfaction. Based on the TAM, this study uses system quality, information quality, and self-efficacy as independent variables, perceived usefulness, perceived ease of use, and satisfaction as mediators, and continuous use intention as the dependent variable. We analyze the data of 256 individuals who completed an online survey with SPSS 26.0 and AMOS 26.0 software. This study conducts several tests and analyses to empirically evaluate the data including reliability analysis, factor analysis, feasibility analysis, path analysis, hypothesis verification, and mediation analysis. Our results investigate which factors may influence consumers' intention to continuously using Apple Watch devices in the future. In summary, satisfaction has a positive effect on the intention to continuously use smartwatchs. Perceived usefulness and perceived ease of use have a positive effect on satisfaction. Among the three factors (system quality, information quality, and self-efficacy), only self-efficacy has no significant impact on perceived usefulness but had a positive effect on perceived ease of use. In addition, system quality and information quality positively affect perceived usefulness, perceived ease of use, satisfaction, and continuous intention to use an Apple Watch. Taking the Apple Watch as the subject of our research topic, this study provides theoretical value by exploring the impact of user's satisfaction with their smartwatch on their continuous usage intention. This study further explains the influence of system quality, information quality, and self-efficacy on user satisfaction. Additionally, this research offers valuable insight to practitioners by confirming that information quality, system quality, and self-efficacy are important features for enhancing satisfactory user experiences which in turn may increase users' intention to continued using smartwatches.

• Journal of Venture Innovation
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• v.6 no.1
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• pp.115-137
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• 2023

This study analyzed the relationship between organizational innovative capability and absorptive capacity, market and technology orientations, and their impact on business performance for IT-BPO companies that are required to absorb new technologies from a leading perspective in the digital transformation era. To achieve this, an online specialized research company and offline surveys were conducted on 291 domestic IT-BPO companies, and SPSS 23 was used for descriptive statistics and reliability analysis while AMOS 23 was used for hypothesis testing including validity and mediating effects. The main findings were as follows: First, in the relationship between innovation and absorptive capabilities and Market Orientation Strategic(MOS), learning capability and knowledge network capability were found to have a statistically significant positive (+) effect on MOS. In the relationship between innovation and absorptive capabilities and Technology Orientation Strategic(TOS), R&D capability, potential absorptive capacity, and realized absorptive capacity had a statistically significant positive (+) effect on TOS. Second, in the relationship between innovation and absorptive capabilities and BP, only R&D capability was found to have a significant effect on BP. Third, both market orientation and technology orientation were found to have a significant positive (+) effect on BP. These findings suggest that effective competency factors can be identified according to the market and technology orientations pursued by IT-BPO companies to increase their growth and value creation, and provide implications for developing differentiated competency enhancement strategies based on strategic objectives.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.432-438
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• 2011

The purpose of the present thesis is to develop bankruptcy prediction models capable of being applied to the Korean construction industry and to deduce an optimal model through comparative evaluation of final developed models. A study population was selected as general contractors in the Korean construction industry. In order to ease the sample securing and reliability of data, it was limited to general contractors receiving external audit from the government. The study samples are divided into a bankrupt company group and a non-bankrupt company group. The bankruptcy, insolvency, declaration of insolvency, workout and corporate reorganization were used as selection criteria of a bankrupt company. A company that is not included in the selection criteria of the bankrupt company group was selected as a non-bankrupt company. Accordingly, the study sample is composed of a total of 112 samples and is composed of 48 bankrupt companies and 64 non-bankrupt companies. A financial ratio was used as early predictors for development of an estimation model. A total of 90 financial ratios were used and were divided into growth, profitability, productivity and added value. The MDA (Multivariate Discriminant Analysis) model and BLRA (Binary Logistic Regression Analysis) model were used for development of bankruptcy prediction models. The MDA model is an analysis method often used in the past bankruptcy prediction literature, and the BLRA is an analysis method capable of avoiding equal variance assumption. The stepwise (MDA) and forward stepwise method (BLRA) were used for selection of predictor variables in case of model construction. Twenty two variables were finally used in MDA and BLRA models according to timing of bankruptcy. The ROC-Curve Analysis and Classification Analysis were used for analysis of prediction performance of estimation models. The correct classification rate of an individual bankruptcy prediction model is as follows: 1) one year ago before the event of bankruptcy (MDA: 83.04%, BLRA: 93.75%); 2) two years ago before the event of bankruptcy (MDA: 77.68%, BLRA: 78.57%); 3) 3 years ago before the event of bankruptcy (MDA: 84.82%, BLRA: 91.96%). The AUC (Area Under Curve) of an individual bankruptcy prediction model is as follows. : 1) one year ago before the event of bankruptcy (MDA: 0.933, BLRA: 0.978); 2) two years ago before the event of bankruptcy (MDA: 0.852, BLRA: 0.875); 3) 3 years ago before the event of bankruptcy (MDA: 0.938, BLRA: 0.975). As a result of the present research, accuracy of the BLRA model is higher than the MDA model and its prediction performance is improved.

• Advances in nano research
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• v.16 no.4
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• pp.375-394
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• 2024

The extensive utilization of concrete has given rise to environmental concerns, specifically concerning the depletion of river sand. To address this issue, waste deposits can provide manufactured-sand (MS) as a substitute for river sand. The objective of this study is to explore the application of machine learning techniques to facilitate the production of manufactured-sand concrete (MSC) containing stone nano-powder through estimating the splitting tensile strength (STS) containing compressive strength of cement (CSC), tensile strength of cement (TSC), curing age (CA), maximum size of the crushed stone (Dmax), stone nano-powder content (SNC), fineness modulus of sand (FMS), water to cement ratio (W/C), sand ratio (SR), and slump (S). To achieve this goal, a total of 310 data points, encompassing nine influential factors affecting the mechanical properties of MSC, are collected through laboratory tests. Subsequently, the gathered dataset is divided into two subsets, one for training and the other for testing; comprising 90% (280 samples) and 10% (30 samples) of the total data, respectively. By employing the generated dataset, novel models were developed for evaluating the STS of MSC in relation to the nine input features. The analysis results revealed significant correlations between the CSC and the curing age CA with STS. Moreover, when delving into sensitivity analysis using an empirical model, it becomes apparent that parameters such as the FMS and the W/C exert minimal influence on the STS. We employed various loss functions to gauge the effectiveness and precision of our methodologies. Impressively, the outcomes of our devised models exhibited commendable accuracy and reliability, with all models displaying an R-squared value surpassing 0.75 and loss function values approaching insignificance. To further refine the estimation of STS for engineering endeavors, we also developed a user-friendly graphical interface for our machine learning models. These proposed models present a practical alternative to laborious, expensive, and complex laboratory techniques, thereby simplifying the production of mortar specimens.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Journal of the Korean Society for information Management
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• v.41 no.2
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• pp.245-268
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• 2024

As the recognition of data's value increases, the role of data repositories in managing, preserving, and utilizing data is becoming increasingly important. This study investigates ways to enhance the trustworthiness of data repositories through obtaining CoreTrustSeal (CTS) certification. Trust in data repositories is critical not only for data protection but also for building and maintaining trust between the repository and stakeholders, which in turn affects researchers' decisions on depositing and utilizing data. The study examines the CoreTrustSeal, an international certification for trustworthy data repositories, analyzing its impact on the trustworthiness and efficiency of repositories. Using the example of DataON, Korea's first CTS-certified repository operated by the Korea Institute of Science and Technology Information (KISTI), the study compares and analyzes four repositories that have obtained CTS certification. These include DataON, the Physical Oceanography Distributed Active Archive Center (PO.DAAC) from NASA, Yareta from the University of Geneva, and the DARIAH-DE repository from Germany. The research assesses how these repositories meet the mandatory requirements set by CTS and proposes strategies for improving the trustworthiness of data repositories. Key findings indicate that obtaining CTS certification involves rigorous evaluation of organizational infrastructure, digital object management, and technological aspects. The study highlights the importance of transparent data processes, robust data quality assurance, enhanced accessibility and usability, sustainability, security measures, and compliance with legal and ethical standards. By implementing these strategies, data repositories can enhance their reliability and efficiency, ultimately promoting wider data sharing and utilization in the scientific community.