Journal of the Korea Academia-Industrial cooperation Society
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v.20
no.4
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pp.35-42
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2019
Nut fasteners that require ultra-precise control are required in the overall manufacturing industry including electronic products that are currently developing with the automobile industry. Important performance factors when tightening nuts include loosening due to insufficient fastening force, breakage due to excessive fastening, Tightening torque and angle are required to maintain and improve the assembling quality and ensure the life of the product. Nut fasteners, which are now marketed under the name Nut Runner, require high torque and precision torque control, precision angle control, and high speed operation for increased production, and are required for sophisticated torque control dedicated to high output BLDC motors and nut fasteners. It is demanded to develop a high-precision torque control driver and a high-speed, low-speed, high-response precision speed control system, but it does not satisfy the high precision, high torque and high speed operation characteristics required by customers. Therefore, in this paper, we propose a control technique of BLDC motor variable speed control and nut runner based on vector control and torque control based on coordinate transformation of d axis and q axis that can realize low vibration and low noise even at accurate tightening torque and high speed rotation. The performance results were analyzed to confirm that the proposed control satisfies the nut runner performance. In addition, it is confirmed that the pattern is programmed by One-Stage operation clamping method and it is tightened to the target torque exactly after 10,000 [rpm] high speed operation. The problem of tightening torque detection by torque ripple is also solved by using disturbance observer Respectively.
Sentiment represents one's own state through the process of change to stimulus, and emotion represents a simple psychological state felt for a certain phenomenon. These two terms tend to be used interchangeably, but their meaning and usage are different. In this study, we try to find out how it affects the helpfulness of reviews by classifying sentiment and emotion through online reviews written by online consumers after purchasing and using various products and services. Recently, online reviews have become a very important factor for businesses and consumers. Helpful reviews play a key role in the decision-making process of potential customers and can be assessed through review helpfulness. The helpfulness of reviews is becoming increasingly important in practice as it is utilized in marketing strategies in business as well as in purchasing decision-making issues of consumers. And academically, the importance of research to find the factors influencing the helpfulness of reviews is growing. In this study, Yelp.com secured reviews on restaurants and conducted a study on how the sentiment and emotion of online reviews affect the helpfulness of reviews. Based on the prior research, a research model including sentiment and emotions for online reviews was built, and text mining analyzes how the sentiment and emotion of online reviews affect the helpfulness of online reviews, and the difference in the effects on emotions It was verified. The results showed that negative sentiment and emotion had a greater effect on review helpfulness, which was consistent with the negative bias theory.
Click-Through Rate (CTR) prediction is a key function that determines the ranking of candidate items in the recommendation system and recommends high-ranking items to reduce customer information overload and achieve profit maximization through sales promotion. The fields of natural language processing and image classification are achieving remarkable growth through the use of deep neural networks. Recently, a transformer model based on an attention mechanism, differentiated from the mainstream models in the fields of natural language processing and image classification, has been proposed to achieve state-of-the-art in this field. In this study, we present a method for improving the performance of a transformer model for CTR prediction. In order to analyze the effect of discrete and categorical CTR data characteristics different from natural language and image data on performance, experiments on embedding regularization and transformer normalization are performed. According to the experimental results, it was confirmed that the prediction performance of the transformer was significantly improved when the L2 generalization was applied in the embedding process for CTR data input processing and when batch normalization was applied instead of layer normalization, which is the default regularization method, to the transformer model.
In order to secure sustainable financial resources and to attract more key supporters, today's art museums are trying to further develop individual sponsorship. The purpose of this study is to explore the direction of membership programs by considering membership as the first step for the development of individual supporters for art museums. Although it is not easy to generate substantial profits within a short period through membership, art museums can secure supporters who empathize with and participate in their mission and activities by developing individual supporters through membership. The new trends of support, which has emerged as a stream of "new philanthropy" since the 1990s, indicate that the needs and motivations of individual supporters are changing. This has great implications for the direction of the development and operation of membership programs at art museums. This study investigated the role, method, and direction of the development of individual supporters through membership by conducting a theoretical review and a case study on the membership programs and individual sponsors of art museums. In addition, the study analyzed the cases of the Cleveland Museum of Art, the Art Institute of Chicago, and the Museum of Modern Art in the United States, which have continuously attempted new approaches and improved membership programs based on a long history of membership operation and individual support, by centering on the new attributes of philanthropy, including participation and involvement, accountability, and transparency. Based on the results, implications and suggestions for Korean art museums were derived. Amid the lack of art museums' membership programs and academic research, this study has significance in exploring the direction and prerequisites for membership for the development of individual supporters.
Amid the explosive growth of various metaverse platforms, there is no unified indicator to measure, analyze, and evaluate based on customer experience. Therefore, the usability evaluation factors in metaverse were identified through a heuristic methodology and literature review, to evaluate the metaverse, a two-to three-dimensional virtual world platform. A measurable system was established by subdividing 20 items in 5 fields, including user control, information structure, design and content, and usage environment, derived through Delphi technique. Based on this, after experiencing the actual contents of major metaverse platforms such as Roblox and Zepeto, usability was evaluated and comparative verification was conducted. As a result, it was estimated that metaverse user experience could be improved as its utility was derived relatively high in terms of user control and content. This study constitutes a theoretical contribution by extending the usability evaluation system, which has been widely used in the field of service design, to the fields of extended reality and mixed reality. At the same time, it has practical key findings of providing basic judgment standards to stakeholders in the metaverse field, as well as policy implications for digital capability enhancement and industry revitalization.
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 Korea Entertainment Industry Association
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v.13
no.8
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pp.55-67
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2019
This study was surveyed the visitors who visited the festival for 4 days from August 01, 2019 to August 04, 2019 .For the questionnaire, 367 copies of valid questionnaires were used as the final analysis data in four beaches: Hakdong Black Pearl Beach, Gujora Beach, Wahyeon Beach, and Jicell Port. The results of this study are as follows. First, all three sub-factors of regional festival service quality, such as confidence, empathy, and credibility, had a positive effect on participation behavior. Second, the result of the moderating effect of the influence of involvement on the quality of service and visitor participation behavior of local festivals is based on the individual moderating effects of service quality and visitor participation behavior. There was a moderating effect. Visitors should be aware of the program, which is faithful to the festival's original purpose, and provide visitors with information about the festival quickly and accurately to feel the efficacy of participating in the festival. And The more satisfied the tourists who visited the local festivals, the more likely it will be to be a successful and successful festival. By speeding up, unexpected positive customer behavior will come from places that are not important.
This case illustrates a story of passionate and innovative new market development by Pulmuone, a fresh food provider in Korea. The company has been successfully developing a new market by introducing a (non-frying) fresh ramen, 'Jayeonun Masitda - The Nature Is Tasty' in the packaged ramen market dominated by fried ones. In this case, a detailed new market development process by Pulmuone will be investigated including; company overview, a new product development process, marketing strategy formulation, marketing mix implementation, market performance, and future directions. Pulmuone has been making efforts to create a new product category by marketing non-frying ramens since 1995, but with a modest success. In 2011, Pulmuone finally succeeded to develop an innovative product, 'Jayeonun Masitda' that brought more health and nutrition conscious consumers' attention in the ramen market. The company intended to change the current competitive structure in the ramen market, i.e., from the strength of taste and the amount of ingredient to fried/non-fried and the freshness of ingredient. By this new positioning, Pulmuone aimed to reshaping the ramen market into competition between healthy and unhealthy ramens. Pulmuone has been successful in developing a new market. Sales revenue of 'Jayeonun Masitda' has been continuously increasing, and customers are found to be highly satisfied with the product resulting in a high repeat purchase rate. The company's successful new market development can be attributed to a faithful new product development process, innovative technology, an appropriate positioning strategy, and consistent marketing communication. In addition, Pulmuone's eco-friendly corporate image and the organization's passion to grow are also important factors for success of this new market development.
Digital transformation and the COVID-19 pandemic have facilitated the rapid development and dissemination of non-face-to-face technologies such as self-service technologies (SSTs). This research investigates how motivation factors affect consumers' negative responses to digital shadow work (DSW) in SSTs which decreases their continuous use intention of SSTs. Also, we examine whether the grafication delay ability moderates the relationship between consumers' negative responses to DSW and their continuous use intention of SSTs. By an analysis of usable 450 user respondents via SmartPLS 4.0, perceived benefits was found to significantly influence consumers' negative responses to DSW. Also, consumers' negative responses to DSW can significantly decrease their intention to continue using SSTs. Further, the effect of negative responses to DSW on continuous use intention of SSTs is stronger in case of consumers with low gratification delay ability than in case of consumers with high gratification delay ability. The study findings contribute to providing some strategies for companies operating SSTs by examining the effects of consumer's responses to DSW and gratification delay ability on the continuous usage intention of SSTs.
Yihua Zhang;Qinglong Li;Ilyoung Choi;Jaekyeong Kim
Information Systems Review
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v.23
no.1
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pp.155-172
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2021
With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.
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