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Mixed Products: How Adding Different Attributes Influences Consumer Perceptions and Product Evaluation

  • Yi, Youjae;Muhn, Sunhee
    • Asia Marketing Journal
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    • v.15 no.1
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    • pp.83-105
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    • 2013
  • During recent decades, the number of mixed attribute products (henceforth mixed products), which have both utilitarian and hedonic benefits, has increased dramatically. Despite these products' growing popularity, academic research has paid little attention to them, and there remains a gap between theory and the real world. Hence, our study was undertaken to understand consumers' perceptions about and behaviors toward mixed products, as well as factors affecting the evaluation and choice of these products. We divided mixed attribute products into two categories: mixed utilitarian products (utilitarian products adding hedonic attributes) and mixed hedonic products (hedonic products adding utilitarian attributes). We then showed how adding different attributes affects consumers' perception, willingness to pay (WTP), and the choice of mixed attribute products compared to pure utilitarian or pure hedonic products. We conducted an experiment using a within-subject design. A total of 160 office workers and college students participated in the study. The pure utilitarian product used in the study was orange juice, and the mixed utilitarian product was carbonated orange juice. The pure hedonic product was chocolate, and the mixed hedonic product was polyphenol enriched chocolate. Results showed that consumers perceived a mixed utilitarian product to be less utilitarian, less pleasurable and more guilty than a pure utilitarian product. On the other hand, a mixed hedonic product was perceived to be more utilitarian, less pleasurable and less guilty than a pure hedonic product. Also, WTP for a mixed hedonic product was higher than WTP for a pure hedonic product, but WTP was lower for a mixed utilitarian product than for a pure utilitarian product. Furthermore, mixed hedonic products were likely to be evaluated more favorably when they were presented together with pure hedonic products, more so than when they were presented alone. Finally, when compared to low self-control participants, high self-control participants chose mixed hedonic products more frequently. The present study contributes to the existing literature on utilitarian and hedonic consumption by adding to the sparse literature on the consumption of products that have both utilitarian and hedonic purposes. Also, our research findings provide several useful implications for practitioners in related fields. First, the current study provides marketers with a useful guide for understanding consumers' perceptions of these types of products, and helps to predict how adding different attributes influences these products. Second, this study has examined the conditions that may moderate the evaluation and choice of hedonic base products and this finding will serve as a good reference for marketers of mixed hedonic products in marketing communication strategy, in-store marketing and targeting. Specifically, comparative advertising with a pure hedonic product will be beneficial for a mixed hedonic product. Also, displaying mixed hedonic products near pure hedonic products may enhance the effectiveness of in-store marketing of mixed hedonic products.

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A Test to Compare the Water Resistance Sun Protection Factor of General Water, Artificial Seawater, and Natural Seawater of Sunscreen (자외선 차단제의 일반 물, 인공 해수, 자연 해수의 내수성 차단지수를 비교하기 위한 시험)

  • Hyoung Hoon Hwang;Eun Young Kang;Su Yeong Kim;Hui Jeong Jung;Jun Seong Yang;Won Kyu Hong;Hong Suk Kim
    • Journal of the Society of Cosmetic Scientists of Korea
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    • v.49 no.4
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    • pp.349-354
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    • 2023
  • Sunscreen is a product that protects against ultraviolet rays by blocking and scattering ultraviolet rays, and has now become a daily necessity beyond cosmetics. Applying sunscreen is a common and easy way to prevent skin damage caused by ultraviolet rays. Due to its significance, the evaluation of sunscreen has evolved since its regulation by the FDA in 1978, progressing to standardized methods established by ISO. Additionally, to assess the loss of sunscreen due to activities such as water exposure or sweating, the Ministry of Food and Drug Safety in Korea and ISO have established protocols for evaluating the water-resistant sun protection factor (SPF). However, existing evaluations of water resistance have been mainly confined to test methods involving plain water, and methods accounting for the impact of seawater during activities like beach leisure, sports, and recreation are yet to be established. Based on the existing guidelines for testing the water-resistant UV protection index, this study compared the water-resistant UV protection index in water, artificial seawater (salt water) and natural seawater (sea water) to evaluate the UV protection index in real-world situations such as marine leisure, sports, and leisure activities. Through these results, we were able to compare the differences between water resistance sun protection index tests in ordinary water, artificial seawater, and natural seawater, and suggest a method for water resistance sun protection index tests using natural seawater.

Detecting Adversarial Examples Using Edge-based Classification

  • Jaesung Shim;Kyuri Jo
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.10
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    • pp.67-76
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    • 2023
  • Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.

An Exploratory Study of Generative AI Service Quality using LDA Topic Modeling and Comparison with Existing Dimensions (LDA토픽 모델링을 활용한 생성형 AI 챗봇의 탐색적 연구 : 기존 AI 챗봇 서비스 품질 요인과의 비교)

  • YaeEun Ahn;Jungsuk Oh
    • Journal of Service Research and Studies
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    • v.13 no.4
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    • pp.191-205
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    • 2023
  • Artificial Intelligence (AI), especially in the domain of text-generative services, has witnessed a significant surge, with forecasts indicating the AI-as-a-Service (AIaaS) market reaching a valuation of $55.0 Billion by 2028. This research set out to explore the quality dimensions characterizing synthetic text media software, with a focus on four key players in the industry: ChatGPT, Writesonic, Jasper, and Anyword. Drawing from a comprehensive dataset of over 4,000 reviews sourced from a software evaluation platform, the study employed the Latent Dirichlet Allocation (LDA) topic modeling technique using the Gensim library. This process resulted the data into 11 distinct topics. Subsequent analysis involved comparing these topics against established AI service quality dimensions, specifically AICSQ and AISAQUAL. Notably, the reviews predominantly emphasized dimensions like availability and efficiency, while others, such as anthropomorphism, which have been underscored in prior literature, were absent. This observation is attributed to the inherent nature of the reviews of AI services examined, which lean more towards semantic understanding rather than direct user interaction. The study acknowledges inherent limitations, mainly potential biases stemming from the singular review source and the specific nature of the reviewer demographic. Possible future research includes gauging the real-world implications of these quality dimensions on user satisfaction and to discuss deeper into how individual dimensions might impact overall ratings.

A Comparative Study on Data Augmentation Using Generative Models for Robust Solar Irradiance Prediction

  • Jinyeong Oh;Jimin Lee;Daesungjin Kim;Bo-Young Kim;Jihoon Moon
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.11
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    • pp.29-42
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    • 2023
  • In this paper, we propose a method to enhance the prediction accuracy of solar irradiance for three major South Korean cities: Seoul, Busan, and Incheon. Our method entails the development of five generative models-vanilla GAN, CTGAN, Copula GAN, WGANGP, and TVAE-to generate independent variables that mimic the patterns of existing training data. To mitigate the bias in model training, we derive values for the dependent variables using random forests and deep neural networks, enriching the training datasets. These datasets are integrated with existing data to form comprehensive solar irradiance prediction models. The experimentation revealed that the augmented datasets led to significantly improved model performance compared to those trained solely on the original data. Specifically, CTGAN showed outstanding results due to its sophisticated mechanism for handling the intricacies of multivariate data relationships, ensuring that the generated data are diverse and closely aligned with the real-world variability of solar irradiance. The proposed method is expected to address the issue of data scarcity by augmenting the training data with high-quality synthetic data, thereby contributing to the operation of solar power systems for sustainable development.

A Critical Essay on 'new cold war' Discourses: The Political Consequences of the 'cold peace' ('신냉전(new cold war)' 담론에 관한 비판적 소론: '차가운 평화(cold peace)'의 정치적 결과)

  • Jun-Kee BAEK
    • Analyses & Alternatives
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    • v.7 no.3
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    • pp.27-59
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    • 2023
  • This study aims to serve as a critical comparison of the currently controversial 'new cold war' discourse. It took three triggers for the 'new cold war' discourse to emerge as a major issue in the media and academia and to have real political impact. With the launch of China's 'Belt and Road' project and Russia's annexation of Crimea leading to the 'Ukraine crisis,' the 'new cold war' discourse has begun to take shape. Trump's U.S.-China trade spat has brought the 'new cold war' debate to the forefront. The 'new cold war' debate is currently being intensified by the Biden administration's framing of "democracy versus authoritarianism" and Putin's invasion of Ukraine. Currently, there is no consensus among scholars on whether the controversial 'new cold war' is a new version, or a continuation of the historically defined concept of the Cold War. The term 'New Cold War' is less of an analytical concept and more of a topical term that has yet to achieve analytical status, let alone a theoretical validation and systematization, and the related debate remains at the level of assertion or discourse. Through this comparative analysis, I will argue that the ongoing discourse of the 'New Cold War' does not have the instrumental explanatory power to analyze the transitional phenomena of the world order today.

Study on the current research trends and future agenda in animal products: an Asian perspective

  • Seung Yun Lee;Da Young Lee;Ermie Jr Mariano;Seung Hyeon Yun;Juhyun Lee;Jinmo Park;Yeongwoo Choi;Dahee Han;Jin Soo Kim;Seon-Tea Joo;Sun Jin Hur
    • Journal of Animal Science and Technology
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    • v.65 no.6
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    • pp.1124-1150
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    • 2023
  • This study aimed to analyze the leading research materials and research trends related to livestock food in Asia in recent years and propose future research agendas to ultimately contribute to the development of related livestock species. On analyzing more than 200 relevant articles, a high frequency of studies on livestock species and products with large breeding scales and vast markets was observed. Asia possesses the largest pig population and most extensive pork market, followed by that of beef, chicken, and milk; moreover, blood and egg markets have also been studied. Regarding research keywords, "meat quality" and "probiotics" were the most common, followed by "antioxidants", which have been extensively studied in the past, and "cultured meat", which has recently gained traction. The future research agenda for meat products is expected to be dominated by alternative livestock products, such as cultured and plant-derived meats; improved meat product functionality and safety; the environmental impacts of livestock farming; and animal welfare research. The future research agenda for dairy products is anticipated to include animal welfare, dairy production, probiotic-based development of high-quality functional dairy products, the development of alternative dairy products, and the advancement of lactose-free or personalized dairy products. However, determining the extent to which the various research articles' findings have been applied in real-world industry proved challenging, and research related to animal food laws and policies and consumer surveys was lacking. In addition, studies on alternatives for sustainable livestock development could not be identified. Therefore, future research may augment industrial application, and multidisciplinary research related to animal food laws and policies as well as eco-friendly livestock production should be strengthened.

Towards high-accuracy data modelling, uncertainty quantification and correlation analysis for SHM measurements during typhoon events using an improved most likely heteroscedastic Gaussian process

  • Qi-Ang Wang;Hao-Bo Wang;Zhan-Guo Ma;Yi-Qing Ni;Zhi-Jun Liu;Jian Jiang;Rui Sun;Hao-Wei Zhu
    • Smart Structures and Systems
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    • v.32 no.4
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    • pp.267-279
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    • 2023
  • Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

NFT(Non-Fungible Token) Patent Trend Analysis using Topic Modeling

  • Sin-Nyum Choi;Woong Kim
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.12
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    • pp.41-48
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    • 2023
  • In this paper, we propose an analysis of recent trends in the NFT (Non-Fungible Token) industry using topic modeling techniques, focusing on their universal application across various industrial fields. For this study, patent data was utilized to understand industry trends. We collected data on 371 domestic and 454 international NFT-related patents registered in the patent information search service KIPRIS from 2017, when the first NFT standard was introduced, to October 2023. In the preprocessing stage, stopwords and lemmas were removed, and only noun words were extracted. For the analysis, the top 50 words by frequency were listed, and their corresponding TF-IDF values were examined to derive key keywords of the industry trends. Next, Using the LDA algorithm, we identified four major latent topics within the patent data, both domestically and internationally. We analyzed these topics and presented our findings on NFT industry trends, underpinned by real-world industry cases. While previous review presented trends from an academic perspective using paper data, this study is significant as it provides practical trend information based on data rooted in field practice. It is expected to be a useful reference for professionals in the NFT industry for understanding market conditions and generating new items.

Efficacy and Safety of Trastuzumab Deruxtecan and Nivolumab as Third- or Later-Line Treatment for HER2-Positive Advanced Gastric Cancer: A Single-Institution Retrospective Study

  • Keitaro Shimozaki;Izuma Nakayama ;Daisuke Takahari;Kengo Nagashima;Koichiro Yoshino ;Koshiro Fukuda;Shota Fukuoka ;Hiroki Osumi ;Mariko Ogura ;Takeru Wakatsuki;Akira Ooki ;Eiji Shinozaki;Keisho Chin ;Kensei Yamaguchi
    • Journal of Gastric Cancer
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    • v.23 no.4
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    • pp.609-621
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    • 2023
  • Purpose: Determination of optimal treatment strategies for HER2-positive advanced gastric cancer (AGC) in randomized trials is necessary despite difficulties in direct comparison between trastuzumab deruxtecan (T-DXd) and nivolumab as third or later-line treatments. Materials and Methods: This single-institution, retrospective study aimed to describe the real-world efficacy and safety of T-DXd and nivolumab as ≥ third line treatments for HER2-positive AGC between March 2016 and May 2022. Overall, 58 patients (median age, 64 years; 69% male) were eligible for the study (T-DXd group, n=20; nivolumab group, n=38). Results: Most patients exhibited a HER2 3+ status (72%) and presented metastatic disease at diagnosis (66%). The response rates of 41 patients with measurable lesions in the T-DXd and nivolumab groups were 50% and 15%, respectively. The T-DXd and nivolumab groups had a median progression-free survival of 4.8 months (95% confidence interval [CI], 3.3, 7.0) and 2.3 months (95% CI, 1.5, 3.5), median overall survival (OS) of 10.8 months (95% CI, 6.9, 23.8) and 11.7 months (95% CI, 7.6, 17.1), and grade 3 or greater adverse event rates of 50% and 2%, respectively. Overall, 64% patients received subsequent treatment. Among 23 patients who received both regimens, the T-DXd-nivolumab and nivolumab-T-DXd groups had a median OS of 14.0 months (95% CI, 5.0, not reached) and 19.3 months (95% CI, 9.5, 25.1), respectively. Conclusions: T-DXd and nivolumab showed distinct efficacy and toxicity profiles as ≥ third line treatments for HER2-positive AGC. Considering the distinct features of each regimen, they may help clinicians personalize optimal treatment approaches for these patients.