• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.813-818
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• 2022

As the theme of a theme park space with limited space emerged as a keyword, many experiments and research were conducted in the field. In the domestic market, not only the creation of new spaces, but also the need to develop marginalized spaces and seek complementary points through new interpretations of actual spaces, but they show physical limitations. This study newly redefined and analyzed the concept of space by approaching and interpreting virtual space as a new space. By exploring real-world examples and analyzing the results, we also derive the results that the possibility of realistic media in the virtual world is an exemplary alternative to complementing the expansion and limitations of the space. Realistic media has a wide range of content provision in selective development such as AR and VR, and the theme park's characteristic of providing various contents for quarterly concepts even if the development budget and time are small, which has a significant impact on the development of realistic media. Currently, theme parks are attracting countless majors and officials related to realistic media through metaverse. As it is attracting attention in such a new market, we look forward to seeing a lot of research and experiments in the project.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.127-146
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• 2023

Recently, digital advertising has shifted towards delivering messages through short ads of less than 15 seconds, and on social media, ads need to convey the message within 5 seconds before consumers skip them. Although the length of advertisements has decreased, advancements in artificial intelligence algorithms and big data analysis have made it possible to deliver personalized messages that cater to consumers' interests. In this changing landscape, the importance of delivering tailored messages through short and efficient ads is increasing. In this study, we examined the effects of message framing as part of effective message delivery. Specifically, we examined the differences in the effects of two framings, "health" and "environment," for vegan products. The growing consumer interest in health and the environment has elevated the interest in vegan products, and the vegan market is expanding rapidly. Consumers purchase vegan products not only for personal health benefits but also due to their ethical responsibility towards the environment, which can be considered ethical consumption. Previous research has not shown the differences in the effects between health and environment message framings, and the research has been limited to vegan food products. This study investigates the differences in the effects of health and environment message framings using a dish soap product category. By identifying which advertising messages, either health or environment, are more effective in promoting vegan products, this study provides insights for companies to enhance their message framing strategies effectively.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.419-437
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• 2023

As the number of thin filers in Korea surpasses 12 million, there is a growing interest in enhancing the accuracy of assessing their credit default risk to generate additional revenue. Specifically, researchers are actively pursuing the development of default prediction models using machine learning and deep learning algorithms, in contrast to traditional statistical default prediction methods, which struggle to capture nonlinearity. Among these efforts, Graph Neural Network (GNN) architecture is noteworthy for predicting default in situations with limited data on thin filers. This is due to their ability to incorporate network information between borrowers alongside conventional credit-related data. However, prior research employing graph neural networks has faced limitations in effectively handling diverse categorical variables present in credit information. In this study, we introduce the Transformer embedded Graph Convolutional Network (TeGCN), which aims to address these limitations and enable effective default prediction for thin filers. TeGCN combines the TabTransformer, capable of extracting contextual information from categorical variables, with the Graph Convolutional Network, which captures network information between borrowers. Our TeGCN model surpasses the baseline model's performance across both the general borrower dataset and the thin filer dataset. Specially, our model performs outstanding results in thin filer default prediction. This study achieves high default prediction accuracy by a model structure tailored to characteristics of credit information containing numerous categorical variables, especially in the context of thin filers with limited data. Our study can contribute to resolving the financial exclusion issues faced by thin filers and facilitate additional revenue within the financial industry.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.635-648
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• 2023

In the current situation of social distancing due to COVID-19, the use of one-person hair salons, which are safer than franchise hair salons, is emerging again. One-man hair salons provide personalized services at high prices, so they can be said to be an industry that required high quality services than franchise hair salons. Despite these characteristics, many studies on hair salon services are focused on franchise hair salons. Therefore, this study was conducted through empirical analysis with the purpose of finding out how the service quality of a one-man hair salon, which required high service quality, affects behavioral intention through the coexistence of customer satisfaction and dissatisfaction. As a result of this study, it was found that the pleasure of consumers in one-person hair salons increases the intention to revisit the most. Those pleasure showed that it is more important to respond and empathize with consumers as well as its expertise of hair designers than the appearance of beauty salons. Through this study, the characteristics of a one-man hair salon were examined and significant results were found.

• Journal of Service Research and Studies
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• v.13 no.1
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• pp.159-175
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• 2023

Mass production has long been the most important production paradigm in establishing a company's strategy as a method of producing various products. However, mass production cannot now be the most important paradigm as companies' competitive environment and consumer needs diversify. In particular, consumers' needs are becoming more diverse and rapidly changing, making it difficult for companies to respond to consumers' needs. Mass customization is the most notable paradigm reflecting this trend, and mass customization aims to produce a variety of products tailored to the needs of customers at a low cost. In this study, the theory and concept of a product architecture system were used to specify a method of realizing mass-customized services, and a case study was conducted focusing on the internal furniture model of a camping car. In particular, unlike previously when companies developed product platforms and modules focusing on productivity, a method of developing and configuring product platforms and modules was suggested by reflecting consumer requirements first, and its effectiveness was considered. As a result of the study, it was confirmed that it was effective in replacement, recyclability, line-up, and chargeability by designing through internal factors of the product architecture system and verifying the effectiveness of the results with external factors. It is expected that further empirical research will be led through a design process using a product architecture system in the future.

• Knowledge Management Research
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• v.24 no.1
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• pp.73-97
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• 2023

Recently, the MyData market has been growing as the importance of data and issues related to personal information protection have drawn much attention together. MyData refers to the concept of guaranteeing an individual's right to personal information and providing and utilizing one's data according to individual consent. MyData service providers can combine and analyze customer information to provide personalized services. In the early days, the MyData business was activated mainly by private companies and the financial industry, but recently, public institutions are also actively taking advantage of MyData. Meanwhile, the importance of an individual's intention to provide MyData for the success of MyData businesses continues to increase, but research related to this is lacking. Moreover, existing studies have been mainly conducted on individual benefits of MyData; there are not enough studies in which both public benefit and perceived risk factors are considered at the same time. In this regard, this study intends to derive factors affecting the intention to provide MyData based on the privacy calculus model, examine their influencing mechanism, and further verify the moderating effects of individual capabilities and institutional type. This study can find academic significance in that it expanded and demonstrated the privacy calculus model in the context of MyData providing intention. In addition, the results of this study are expected to offer practical guidelines for developing and managing new services in MyData businesses.

• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.74-99
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• 2011

This study aims to introduce conversion franchising strategy by utilizing the case of Nadle-Gagae. The case study of Nadle-Gagae shows that conversion franchising to Nadel-Gagae increases sales, the number of customer visits or visiting rates, and the level of satisfaction of store-owner and customer. This implies that conversion franchising benefits conversion franchising company, store-owner, and customer; it can be conducted as a competitive edge or strategy. However, it is limited to conclude that conversion franchising strategy will apply to all general franchising companies by only analysizing the case of Nadle-Gagae, because the business was initiated by government agency or governmental policy. Therefore, the franchising management should consider more conditions or circumstances related to franchising industry.

• The Journal of Bigdata
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• v.6 no.2
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• pp.17-28
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• 2021

Current development of technologies related to 4^th industrial revolution and the pandemic of COVID-19 lead the rapid expansion of e-marketplace. The level of competition among several companies gets increased by introducing different strategies. To cope with the current change in the market and satisfy the customers who request the better delivery service, the new concept, fulfillment, has been introduced. It makes the leadtime of process from order picking to delivery reduced and the efficiency improved. Still, the efficiency of operation in fulfillment centers constrains the service level of the entire delivery process. In order to solve this problem, several different approaches for demand forecasting and coordinating supplies using Bigdata, IoT and AI, which there exists the trivial limitations. Because it requires the most lead time for operation and leads the inefficiency the process from picking to packing the ordered items, the logistics service providers should try to automate this procedure. In this research, it has been proposed to develop the efficient plans to automate the process to move the ordered items from the location where it stores to stage for packing using AGV and AMR. The efficiency of automated devices depends on the number of items and total number of devices based on the demand. Therefore, the result of simulation based on several different scenarios has been analyzed. From the result of simulation, it is possible to identify the several factors which should be concerned for introducing the automated devices in the fulfillment centers. Also, it can be referred to make the optimal decisions based on the efficiency metrics.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.