• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.205-221
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• 2022

In this study, the authors investigated the effect of salesperson's Selling Influence Tactics (SIT) on customers' psychological obligation, trust in salespersons and repurchase intentions in the offline cosmetics purchase channel. In addition, we examined the moderating effect of service quality perceived by customers. To this end, a survey was conducted on 298 customers who had purchased cosmetics through the offline sales channel, and the authors conducted hypothesis testing through a structural equation model. As a result of the study, first, among salesperson's sales influence tactics, emotional appeal tactics (H1a), customer ingratiation tactics (H1d), and personal appeal tactics (H1e) were found to affect the psychological obligation of customers, and emotional appeal tactics (H2a), rational persuasion tactics (H2b), information provision tactics (H2c), and customer ingratiation tactics (H2d) were found to affect trust in salespeople. Third, it was found that the psychological obligation did not have a positive (+) effect on the customer's repurchase intention, and the customer's trust in the salesperson had a positive (+) effect on the repurchase intention. Third, perceived service quality showed a significant moderating effect between psychological obligation and repurchase intention, trust in salesperson and repurchase intention. In previous studies on salesperson's Selling Influence Tactics (SIT), many studies examined salesperson's Selling Influence Tactics (SIT) by specifying sub-variables in a limited way, and studies confirming marketing factors such as repurchase intention were also insufficient. Therefore, the results of the empirical research confirmed based on this study are expected to help the standard or direction of the salesperson's Selling Influence Tactics (SIT) in future studies. In addition, this study describes implications for providing help in employee education and management for small business owners who manage and operate offline cosmetics stores, and sales strategies that should be strategically established to improve perceived service quality for customers.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.9
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• pp.1583-1592
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• 2001

연구의 목적은 병행수입제도의 정책운용적인 관점에서 고가품 수입의 큰 비중을 차지 하고있는 럭셔리의류 브랜드를 중심으로 병행수입제도 도입 이후 현 시점에서 공정경쟁의 관점에서 병행수입의 문제점 추출과 대안을 도출함으로써 정책적 시사점을 제시하고자 한다. 연구 방법은 문헌조사와 in-depth 인터뷰를 실시하였다. 연구 결과 제도적 측면에서는 병행수입제도 도입 당시 병행수입과 직접 관련된 지적재산법과 독점규제법 사이에는 전용사용권자의 보호와 규제라는 입법적 목적이 서로 다른 이유로 병행수입 도입 여부에 대한 논란이 있는 것으로 나타났다 , 상표권 침해 기준의 모호, 세관절차에 있어서의 문제점 등이 추출되었다. 럭셔리의류 산업적 측면에서 병행수입제도는 수입물품의 가격인하를 선도하여 소비자 이익 증진에 기 여 하는 반면에 명품의 유통경로가 다양해짐에 따라 위조상품의 처벌실적 이 증가하고, 이월상품과overrun된 제품의 저가수입 등의 문제점이 야기되고 있다. 이에 대한 정책적 해결방안으로는 공정거래법의 개정을 통한 병행수입 허용기준의 보완, 지적재산권법 에 병행수입허용 규정 명시 , 소비자 관점에서의 병행수입 제품에 대한 정확한 정보의 전달 등의 내용 보완이 이루어져 야 한다. 본 연구의 시사점은 정책당국에게 경쟁조건의 Global Standard라는 견지에서 제도적 운용의 개선이 시급히 이루어져야 하는 논거와 토대를 제공하였다. 또한 전용사용권자와 병행수입업체에는 정보의 비대칭성에 의존한 판매전략을 지양하고 소비자 보호와 적 정 한 상표권 보호의 합리적 조화가 궁극적으로 생존의 핵심전략임을 인식시켜 줄 수 있는 계기를 마련해 주었다.가치관은 이들의 의복관심과 의복착용 그리고 구매에 지대한 영향을 미친다는 것을 알 수 있다.포제로 평가되었다. 모든 남자포제가 전통적, 순수한 이미지의 포제로, 철릭을 제외한 모든 포제가 단순한 이미지로 나타나 조선시대 남자 포제가 공통적으로 가지는 이미지는 단순하고 순수한 이미지를 가지고 있음을 알 수 있다. 3. 남자 포제의 시기별 복식이미지에서는 조선전기(철릭, 답호, 직령)의 포제는 관할성 요인이 높은 의례적인, 관할한, 특이한 이미지로 평가되었고 조선중기(도포, 창의)의 포제는 품위있는, 절제된, 풍성한 이미지로 평가되었으며, 조선후기(주의)의 포제는 활동적인, 단순한, 직선적인 이미지로 나타났다. 따라서 시대별 남자 포제의 이미지는 시대적 여건과 상황에 따라 변화되어 왔으며, 시대에 따라 추구하는 이미지가 달랐다는 것을 알 수 있이러한 논란의 해결방안으로 관세법 고시에 의한 우회적 도입을 결정한 정부의 방침에 대해 제도적 측면에서의 문제점으로 저작권 침해 기준의 결여다.lavonoid 함량의 판단지표로 활용될 수 있을 것으로 사료되었다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.480-489
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• 2020

Controversy and legal disputes over counterfeit fashion designs have recently arisen in the fashion industry. The purpose of this study is to examine cases of counterfeiting disputes over fashion designs, and how the perception of counterfeit fashion designs is fostered from the learner's point of view, suggesting implications for the counterfeiting problem. As a result of this study, first, counterfeiting disputes over fashion design started from a lack of utilization of the Design Protection Act and the ambiguity in counterfeit design criteria. Second, the negative perceptions of counterfeit designs were mainly about unethical behavior, inhibiting the growth of the fashion industry, and reducing consumers' willingness to buy the genuine article. Positive perceptions were mainly about the process of creation, the promotion of a developmental environment for the fashion industry, and the expansion of opportunities to promote new designs. The most common perception was the absence of clear criteria for judgments about counterfeiting. Third, the implications of the counterfeiting problem in fashion design require effective institutional improvement in the fashion industry, the establishment of standards to deal with counterfeiting, the development and practical introduction of education proposals regarding intellectual property rights, and changing the perception of counterfeiting in the fashion industry.

• The Korean Journal of Pesticide Science
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• v.10 no.3
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• pp.183-188
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• 2006

A nation-wide monitoring survey was performed to look into pesticide use pattern and amount on farmers' field after the national registration of pesticides from 2003 to 2004. Tow hundred ninety two leading farmers involved in the survey were chosen from main cultivating regions of targeted crops in consideration with area. Most farmers involved in the survey follow the safe use guideline of pesticides, while their information to select and making-decision for purchasing pesticides were done according to the recommendation of mainly pesticide market dealers or agricultural cooperative dealers. Used amount of pesticide per unit cropping area was surveyed as 5.51 on paddy, 3.91 on field cabbage, 2.93 on greenhouse cabbage, 0.76 on lettuce, 0.55 on spinach, 1.34 on perilla leaf, 4.89 on leek and 0.42 kg $ha^{-1}$ on young radish. As compound with those surveyed from 1999 to 2000, the pesticide amounts used for rice, lettuce and perilla leaf were reduced while that for cabbage was increased.

• Journal of Food Hygiene and Safety
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• v.36 no.4
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• pp.342-346
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• 2021

Cold brew coffee extracted from cold water for a long time has drawn public concern over hygiene. This study was carried out to investigate the microbiological contamination levels and caffeine contents in cold brew coffee. A total of 75 cold brew coffees were purchased from offline and online sources. As a result, the average number of bacteria in samples purchased online was 1.14 log CFU/mL (0-6.57 log CFU/mL), while bacteria were not detected in samples purchased offline. Therefore, stricter surveys are required to avoid the food contamination. However, Esherichia coli and nine types of foodborne pathogens were not detected in all samples. The average caffeine content of the samples was 1.6 mg/mL (384 mg/240 mL), so the caffeine almost reached to acceptable daily intake levels (400 mg for adults). However, ten products did not provide any precautions for consumer safety, so improvement of the system is needed. This monitoring data can contribute to the protection of consumer rights and improvement in the safety of cold brew coffee.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.127-133
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• 2021

In this study, an augmented reality-based make-up tool was built to analyze the user's face shape based on face-type reference model data and to provide virtual makeup by providing face-type makeup. To analyze the face shape, first recognize the face from the image captured by the camera, then extract the features of the face contour area and use them as analysis properties. Next, the feature points of the extracted face contour area are normalized to compare with the contour area characteristics of each face reference model data. Face shape is predicted and analyzed using the distance difference between the feature points of the normalized contour area and the feature points of the each face-type reference model data. In augmented reality-based virtual makeup, in the image input from the camera, the face is recognized in real time to extract the features of each area of the face. Through the face-type analysis process, you can check the results of virtual makeup by providing makeup that matches the analyzed face shape. Through the proposed system, We expect cosmetics consumers to check the makeup design that suits them and have a convenient and impact on their decision to purchase cosmetics. It will also help you create an attractive self-image by applying facial makeup to your virtual self.

• Smart Media Journal
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• v.10 no.4
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• pp.54-62
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• 2021

As the volume of online e-commerce transactions increases, items diversify and the classification becomes complicated. E-commerce platforms that specialize in dealing only in one area are emerging, and the area is diversifying. Three problems were identified by researching the craft online e-commerce platform, one of the various types of professional e-commerce platforms. First of all, although craft materials are diversified and complex on the platform, the existing craft e-commerce system is fragmented in structure to categorize complex crafts, making it difficult to accurately present search results that meet various criteria. Second, although appearance is the main reason for purchasing artifacts, it is rare for users to categorize them according to appearance, so they have to judge and filter each work directly. Finally, the language entered when searching for artifacts by non-technical experts is not reflected in the language used to categorize artifacts in the taxonomic system, so the language used for searching is highly accurate. Therefore, the purpose of this study is to add and consider complex attributes in the field of technology to meet the search criteria. Properties to be added must include the main appearance in the search for artifacts. In addition, the government aims to develop a taxonomic system that can reflect non-experts' search languages in the search of works through artificial intelligence natural language processing technology.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.23-43
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• 2016

As a result of the growth of Internet data and the rapid development of Internet technology, "big data" analysis has gained prominence as a major approach for evaluating and mining enormous data for various purposes. Especially, in recent years, people tend to share their experiences related to their leisure activities while also reviewing others' inputs concerning their activities. Therefore, by referring to others' leisure activity-related experiences, they are able to gather information that might guarantee them better leisure activities in the future. This phenomenon has appeared throughout many aspects of leisure activities such as movies, traveling, accommodation, and dining. Apart from blogs and social networking sites, many other websites provide a wealth of information related to leisure activities. Most of these websites provide information of each product in various formats depending on different purposes and perspectives. Generally, most of the websites provide the average ratings and detailed reviews of users who actually used products/services, and these ratings and reviews can actually support the decision of potential customers in purchasing the same products/services. However, the existing websites offering information on leisure activities only provide the rating and review based on one stage of a set of evaluation criteria. Therefore, to identify the main issue for each evaluation criterion as well as the characteristics of specific elements comprising each criterion, users have to read a large number of reviews. In particular, as most of the users search for the characteristics of the detailed elements for one or more specific evaluation criteria based on their priorities, they must spend a great deal of time and effort to obtain the desired information by reading more reviews and understanding the contents of such reviews. Although some websites break down the evaluation criteria and direct the user to input their reviews according to different levels of criteria, there exist excessive amounts of input sections that make the whole process inconvenient for the users. Further, problems may arise if a user does not follow the instructions for the input sections or fill in the wrong input sections. Finally, treating the evaluation criteria breakdown as a realistic alternative is difficult, because identifying all the detailed criteria for each evaluation criterion is a challenging task. For example, if a review about a certain hotel has been written, people tend to only write one-stage reviews for various components such as accessibility, rooms, services, or food. These might be the reviews for most frequently asked questions, such as distance between the nearest subway station or condition of the bathroom, but they still lack detailed information for these questions. In addition, in case a breakdown of the evaluation criteria was provided along with various input sections, the user might only fill in the evaluation criterion for accessibility or fill in the wrong information such as information regarding rooms in the evaluation criteria for accessibility. Thus, the reliability of the segmented review will be greatly reduced. In this study, we propose an approach to overcome the limitations of the existing leisure activity information websites, namely, (1) the reliability of reviews for each evaluation criteria and (2) the difficulty of identifying the detailed contents that make up the evaluation criteria. In our proposed methodology, we first identify the review content and construct the lexicon for each evaluation criterion by using the terms that are frequently used for each criterion. Next, the sentences in the review documents containing the terms in the constructed lexicon are decomposed into review units, which are then reconstructed by using the evaluation criteria. Finally, the issues of the constructed review units by evaluation criteria are derived and the summary results are provided. Apart from the derived issues, the review units are also provided. Therefore, this approach aims to help users save on time and effort, because they will only be reading the relevant information they need for each evaluation criterion rather than go through the entire text of review. Our proposed methodology is based on the topic modeling, which is being actively used in text analysis. The review is decomposed into sentence units rather than considering the whole review as a document unit. After being decomposed into individual review units, the review units are reorganized according to each evaluation criterion and then used in the subsequent analysis. This work largely differs from the existing topic modeling-based studies. In this paper, we collected 423 reviews from hotel information websites and decomposed these reviews into 4,860 review units. We then reorganized the review units according to six different evaluation criteria. By applying these review units in our methodology, the analysis results can be introduced, and the utility of proposed methodology can be demonstrated.