• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Service Research and Studies
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• v.10 no.1
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• pp.33-53
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• 2020

The purpose of this study is to empirically examine the causal relationship of CSR activities, customer trust and CCID, customer behavior intention(B.I) in the relationship between CSR activities and customer behavior intention(B.I) in the insurance industry, thereby enable top management of insurance company to take it in their consideration that CSR activity help link to customer behavioral intention by customer trust in them and CCID. To achieve the purpose of the study, the hypothesis was established based on preceding research and theoretical background regarding CSR, trust, CCID, behavioral intention(B.I). And this study conducted AMOS statistical analysis based on effective 526 survey data collected from insurance customers across country through online research company. The result of this empirical study is as follows. First, insurance company's CSR activity has a positive impact on customer's trust and CCID, but it did not have a direct significant effect on the customer's behavioral intention(B.I). Second, both customer's trust and CCID have a positive and significant effect on customer's behavioral intention. Third, we have also found that both Trust and CCID played a mediating role between CSR activity and B,I. Fourth, it was found that authenticity did not moderate the enfluence relationship between CSR activity and Trust, CCID. The result of this study shows that insurance company's active CSR activity increase customer trust, thereby create a sense of unity between the customer and the company, In addition, it shows that when CSR activities are mediated by customer trust and CCID, it could lead to customer behavioral intention(B.I) such as repurchasing and positive word-of-mouth activities. to others. The result of this study will contribute to the future research on CSR literature and the marketing strategy of insurance companies.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.73.4-74
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• 2021

한국천문연구원은 2017년 제1차 구술채록사업에 이어 2020년 제2차 사업을 진행하면서 최초로 원외 원로에 대한 구술채록을 시도하였다. 국가 대표 천문연구의 산실로서 연구원 존재 의의를 확립하기 위하여 원내 원로에 국한되었던 구술자 대상을 확장한 것이다. 그 첫 외부 구술 대상자로 방득룡 전임 노스웨스턴 천문학과 교수를 선정하여 2020년 7월부터 준비단계에 들어갔다. 방득룡 전(前)교수가 첫 번째 한국천문연구원 원외 인사 구술자로 선정된 이유는, 그가 우리나라 천문대1호 망원경 구매 선정에 개입한 서신(1972년)이 자료로 남아있었기 때문이다. 한국천문연구원에서 2017년에 수행한 제1차 구술채록사업에서 구술자로 참여한 오병렬 한국천문연구원 원로가 기증한 사료들은 대부분 연구원 태동기 국립천문대 구축과 망원경 구매 관련 자료였으며 이 가운데 1972년 당시 과학기술처 김선길 진흥국장에게 Boller and Chivesns(사(社))의 반사경을 추천한 방득룡 전(前)교수의 서신은 한국 천문학 발전사에서 중요한 사료였다. 연구진은 이 자료를 시작으로, 방득룡 전(前)교수의 생존 여부와 문서고의 공기록물들에서 그의 흔적을 찾아가기 시작했다. 놀랍게도 그는 실제 세계와 한국천문연구원 문서고 깊숙이 기록물들 모두에서 상존하고 있었다. 1927년생인 방득룡 전(前)교수, Dr. John D. R.은 미국 플로리다 한 실버타운에서 건강한 정신으로 생존하여 있었고 연구진의 인터뷰에 흔쾌히 응했다. 2020년 9월 16일에 한국천문연구원 본원 세종홀 2층 회의실에서 영상통신회의로 그와의 구술인터뷰가 진행되었다. 이 구술인터뷰는 원외 인사가 대상이란 점 외에도 방법적으로는 전형적인 대면 방식이 아닌 영상 인터뷰였다는 점에서 코로나 시대의 대안이 되는 실험적 시도였다. 현대 한국천문학 발전사의 재조명 측면에서도 의미가 있었다. 1960년대 초반부터 1992년 정년퇴임까지 30년을 미국 유수 대학교 천문학과 교수로 재직하며 활발한 활동을 해 온 한국계 천문학자가 우리나라 최초 반사망원경 구매 선정에 적극 개입하였던 역사는, 공문서 자료들과 서신 사료들에 이어 그의 육성으로 나머지 의구심의 간극이 채워졌다. 또 구술자 개인이 주관적으로 중요하다고 여기는 '기억'이 중요한 아카이빙 콘텐츠 확장의 단초가 될 수 있다는 것을 보여줌으로써 구술사 연구에 있어서도 중요한 관점을 주었다. 애초 연구진이 방득룡 전(前)교수의 공식 기록에서 아카이빙의 큰 줄기로 잡았던 것은 1948년 도미, 1957년 위스콘신 대학교 천문학 박사학위 취득, 1962년부터 노스웨스턴 대학(일리노이주 에반스턴)의 천문학 교수진, 1992년 은퇴로 이어진 생애였다. 그러나 그와의 구술 준비 서신 왕래와 구술을 통하여 알게 된 그가 인생에서 중요시 여겼던 지점은, 1948년 도미 무렵 한국의 전쟁 전 상황과 당시 비슷한 시기에 유학한 한국 천문학자들의 동태, 그리고 1957년부터 1962년까지 프린스턴 대학교에서 M. Schwarzschild 교수와 L. Spitzer 교수를 보조하며 Stratoscope Project를 연구하였던 경험이었다. 기록학적 의미에서도, 전자를 통해서 그와 함께 동시대 한국 천문학을 이끌었던 인재들의 맥락정보를 얻을 수 있었으며, 후자를 통해서는 세계 천문학사에 큰 영향을 미친 석학에 대한 아카이브 정보와의 연계 지점과 방득룡 전(前)교수의 연구 근원을 찾을 수 있었다. 이들은 추후 방득룡 콘텐츠 서비스 시에 AIP, NASM, Lyman Spitzer 콘텐츠, 평양천문대, 화천조경천문대, 서울대와 연세대, 그리고 한국천문연구원까지 연계되어 전 세계 폭넓은 이용자들의 유입을 유도할 수 있는 검색 도구가 될 수 있다. 이번 방득룡 구술사 연구에서 구술자 개인의 주관적인 소회가 공식 기록이 다가갈 수 없는 역사적 실체에 일정 부분 가까울 수 있다는 것, 그리고 이를 통하여 개인의 역사는 공동체의 역사로 확장될 수 있다는 사실을 발견할 수 있었다. 또 연구진은 방득룡 전(前)교수의 회상을 통하여 구술자 개인의 시각으로 한국과 미국 천문학계의 공동체 역사를 재조명할 수 있었고, 이것을 아카이브 콘텐츠 확장 서비스에 반영할 수 있다는 기대를 가지게 되었다. 무엇보다 이 연구를 통하여 다양한 주제의 아카이브로 연동될 수 있는 주제어와 검색도구를 구술자 개인의 회상으로부터 유효하게 도출할 수 있다는 것을 확인하였다. 그리고 향후 한국천문 구술아카이브의 확장을 통하여 보다 다양한 활용과 연구 재활용의 선순환이 가능하다는 것도 알 수 있었다. 이는 최근 기록학계에서 대두되고 있는 LOD(Linked Open Data)의 방향성과도 흡사하여 한국천문학 구술사연구의 차세대 통합형 기록관리의 미래모형을 기대케 하는 대목이다.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.17-35
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• 2015

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase for a certain period of time. Developing precise forecasting models are considered important since corporates can make strategic decisions on new markets based on future demand estimated by the models. Many studies have developed market growth curve models, such as Bass, Logistic, Gompertz models, which estimate future demand when a market is in its early stage. Among the models, Bass model, which explains the demand from two types of adopters, innovators and imitators, has been widely used in forecasting. Such models require sufficient demand observations to ensure qualified results. In the beginning of a new market, however, observations are not sufficient for the models to precisely estimate the market's future demand. For this reason, as an alternative, demands guessed from those of most adjacent markets are often used as references in such cases. Reference markets can be those whose products are developed with the same categorical technologies. A market's demand may be expected to have the similar pattern with that of a reference market in case the adoption pattern of a product in the market is determined mainly by the technology related to the product. However, such processes may not always ensure pleasing results because the similarity between markets depends on intuition and/or experience. There are two major drawbacks that human experts cannot effectively handle in this approach. One is the abundance of candidate reference markets to consider, and the other is the difficulty in calculating the similarity between markets. First, there can be too many markets to consider in selecting reference markets. Mostly, markets in the same category in an industrial hierarchy can be reference markets because they are usually based on the similar technologies. However, markets can be classified into different categories even if they are based on the same generic technologies. Therefore, markets in other categories also need to be considered as potential candidates. Next, even domain experts cannot consistently calculate the similarity between markets with their own qualitative standards. The inconsistency implies missing adjacent reference markets, which may lead to the imprecise estimation of future demand. Even though there are no missing reference markets, the new market's parameters can be hardly estimated from the reference markets without quantitative standards. For this reason, this study proposes a case-based expert system that helps experts overcome the drawbacks in discovering referential markets. First, this study proposes the use of Euclidean distance measure to calculate the similarity between markets. Based on their similarities, markets are grouped into clusters. Then, missing markets with the characteristics of the cluster are searched for. Potential candidate reference markets are extracted and recommended to users. After the iteration of these steps, definite reference markets are determined according to the user's selection among those candidates. Then, finally, the new market's parameters are estimated from the reference markets. For this procedure, two techniques are used in the model. One is clustering data mining technique, and the other content-based filtering of recommender systems. The proposed system implemented with those techniques can determine the most adjacent markets based on whether a user accepts candidate markets. Experiments were conducted to validate the usefulness of the system with five ICT experts involved. In the experiments, the experts were given the list of 16 ICT markets whose parameters to be estimated. For each of the markets, the experts estimated its parameters of growth curve models with intuition at first, and then with the system. The comparison of the experiments results show that the estimated parameters are closer when they use the system in comparison with the results when they guessed them without the system.