본 논문은 캐나다 주별이민자추천프로그램(PNP)의 도입과 운영 과정을 다층거버 넌스(MLG) 틀을 통해 분석하고, 그 과정에서 나타난 정치적·사회적 합의의 특징을 탐구한다. PNP는 연방정부와 주정부 간의 협력적 거버넌스 구조 속에서 각 주가 경 제적·인구학적 필요에 맞추어 자율적으로 이민자를 선발할 수 있는 제도로, 캐나다 의 연방주의 체제 내에서 이민정책의 독특한 모델을 제시하고 있다. 본 연구는 PNP 관련 정책자료, 법령, 그리고 선행연구를 바탕으로 사례분석을 진행하 였으며, 특히 연방정부와 주정부 간의 협력 과정 및 지역사회와의 상호작용 을 중심으로 다층적 거버넌스 구조를 고찰하였다. 또한, 캐나다의 PNP 사 례를 독일, 호주, 일본 등의 중앙집중형 이민정책과 비교함으로써, 캐나다 사례가 다른 국가에 제공하는 시사점을 도출하였다. 결과적으로, PNP는 중앙집권적 이민정책의 한계를 보완할 수 있는 연방주의적 이민모델로, 각 주가 지역 특성에 맞는 이민자 선발을 통해 경제 활성화와 인구문제 해결에 기여하였다.

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor’s item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

This study examines options to revitalize a B2B textile trading platform, exploring user satisfaction and perceptions of the importance of several website features. Between June 8 and June 21, 2023, fashion studies majors and domestic fashion brand product planners were asked to use the website of an open B2B textile platform for 30 minutes and then evaluate its features by responding to a survey. The final sample for analysis wad comprised of 150 questionnaires. To analyze the key textile website features, a paired t-test, Importance-Performance Analysis (IPA), and multiple regression analysis were utilized. The analysis classified the key textile website features related to user importance and satisfaction into the following categories: convenience, appearance, product information, and uniqueness. An analysis investigation of the differences in importance and satisfaction for each website evaluation attribute found significant differences in 12 attributes. The IPA analysis revealed that attributes such as product reliability, quality, a convenient search function, and convenient page movement are highly important to users and garner high user satisfaction; these findings demonstrate the importance of maintaining these elements. Images on the main screen, the latest trend information, and product prominence attributes also garner high importance ratings, but result in low user satisfaction, which signifies extensive revision is required. Finally, user evaluation of the convenience, appearance, and product information of the website was found to affect user recommendation intention.

Food is essential for sustenance and reflects a country’s identity, making it crucial to identify the cultural needs for effectively localizing Korean food. This study surveyed 825 adults from four continents (eight countries) to examine their preferences, familiarity, and attitudes toward Korean food. Significant correlations(p< .001) were found between the familiarity and preference for Korean food, with variations observed across continents. Among the representative Korean food items, the average preference score was 4.67, and the purchase/recommendation intention score was 4.88. Seven items received above-average ratings (e.g., gogi-deopbap and kimchi-bokkeumbap), while some items showed high liking but low purchase/recommendation intention (e.g. dak-jjim and galbi-jjim). In addition, items such as gimbap and tteokbokki had high purchase/recommendation intention but low liking, and kimchi and vegetable foods etc. received low liking and purchase/recommendation intentions. In terms of the preferred meat according to the cooking method and seasoning, beef respondents preferred grilled · stir-fried and soup·stew·hot pot cooking methods, while pork or chicken respondents preferred grilled · stir-fried and frying methods. Soy sauce was the most preferred seasoning for all meat responses, followed by red pepper paste. These research findings provide fundamental data for developing Korean food products, segmented by continent.

The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.

본 연구는 IPA 기법을 이용하여 중국인 골프장 이용객의 골프장 선택속성 요인에 대한 재방문 의도 및 추천의도에 미치는 영향을 규명하는데 목적이 있다. 본 연구를 수행하고자 중국인 골프장 이용객 388명을 대상으로 설문조사를 실시하여 자료처리 및 IPA 분석을 실시한 결과, 다음과 같은 결론을 얻었다. 첫째 선택속성의 중요도는 직원 예절, 만족도는 식당 및 식음료 가격이 가장 높게 나타났다. 둘째, IPA 매 트릭스 분석 결과, 지속유지에는 8개, 집중개선은 14개, 낮은 우선순위는 5개, 과잉노력지양은 3개의 선택 속성이 분포하였다. 셋째 비용, 접근성, 코스시설, 부대시설, 캐디 전문성, 이용객 관리의 만족도가 재방문 과 추천 의도에 모두 영향을 미치는 것으로 나타났다. 본 연구는 중국인 골프장 이용객으로 한정하였기 때 문에 골프문화가 다른 국가에서 연구된 선행연구와는 그 결과를 비교하는데 있어 차이가 있을 수 있기 때 문에 문화의 다양성을 고려한 연구들이 향후에 진행되어야 할 것이다.

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

본 연구는 최근 기업체에서 많이 사용하고 있는 세무회계서비스에 대한 추천 의향을 조사하여 그 결과를 분석하는 것이다. 특히 비용이나 시간적 측면에서 상대적으로 어려움을 겪는 100인 이하의 소규모 기업체를 대상으로 하여 해 당 기업체들에 더 나은 서비스가 되기 위한 방향성을 찾는 것을 목적으로 한다. 이를 위하여 100명의 기업체 관계자 대상으로, 회사 근무자 규모, 직급, 사업자 유형 등 사업체 기본 정보는 물론 이용 중인 세무회계서비스 형태, 서비스에 대한 추천 점수, 점수에 대한 이유, 기타 세무회계서비스 관련 의견 등을 조사한다. 특히 추천 점수는 단순 만족도를 묻는 일반적인 고객의 만족도 조사보다 고객의 의견을 파악하는데 더 효과적이라고 알려진 NPS(Net Promoter Score) 방식을 사용함으로써 더 효과적인 결과를 얻고자 한다. 조사 결과 추천도에 대한 NPS 점수는 -33점으로 나왔으며 이는 일반적인 NPS 점수 평가 기준을 참고할 때 낮은 편에 해당하여 세무회계서비스에 대한 개선이 필요하다는 것을 알 수 있었다. 더 구체적으로는 비추천 점수를 준 응답자들의 의견에서 불편하지도 편하지도 않고 그냥 무난해서 도움 이 되는지 잘 모르겠다, 차별성이 없으며 대안도 특별히 없다 등의 의견이 있었음을 볼 때 비추천 점수를 높이기 위해서는 차별적인 서비스가 필요하다는 결론을 얻을 수 있었다. 본 조사는 100인 이하의 기업체 관계자를 대상으로 추천 도 중심으로 조사한 것으로 이후에는 기업체 규모와 조사 항목을 더 다양하게 한 조사 진행이 추가로 필요하다.

본 논문은 지각된 가치가 적용된 관광 행동의도 정보를 이용한 지능형 클라우드 환경에서의 관광추천시스템을 제안한다. 이 제안 시스템은 관광정보와 관광객의 지각적 가치가 행동의도에 반영되는 실증적 분석 정보를 와이드 앤 딥러닝 기술을 이용하여 관광추천시스템에 적용하였다. 본 제안 시스템은 다양하게 수집할 수 있는 관광 정보와 관광객이 평소에 지각하고 있던 가치와 사람의 행동에서 나타나는 의도를 수집 분석하여 관광 추천시스템에 적용하였다. 이는 기존에 활용되던 다양한 분야의 관광플랫 폼에 관광 정보, 지각된 가치 및 행동의도에 대한 연관성을 분석하고 매핑하여, 실증적 정보를 제공한다. 그리고 관광정보와 관광객의 지각적 가치가 행동의도에 반영되는 실증적 분석 정보를 선형 모형 구성요소와 신경만 구성요소를 합께 학습하여 한 모형에서 암기 및 일반화 모두를 달성할 수 있는 와이드 앤 딥러닝 기술을 이용한 관광추천 시스템을 제시하였고, 파이프라인 동작 방법을 제시하였다. 본 논문에서 제시한 추천시스템은 와이드 앤 딥러닝 모형을 적용한 결과 관광관련 앱 스토어 방문 페이지 상의 앱 가입률이 대조군 대비 3.9% 향상했고, 다른 1% 그룹에 변수는 동일하고 신경망 구조의 깊은 쪽만 사용한 모형을 적용하여 결과 와이드 앤 딥러닝 모형은 깊은 쪽만 사용한 모형 대비해서 가입률을 1% 증가하였다. 또한, 데이터셋에 대해 수신자 조작 특성 곡선 아래 면적(AUC)을 측정하여, 오프라인 AUC 또한 와이드 앤 딥러닝 모형이 다소 높지만 온라인 트래픽에서 영향력이 더 강하다는 것을 도출하였다.

This study analyzed what premium features significantly affect customer satisfaction and their recommendation, and what factors significantly affect product attributes. In the process, first, the loyalty program and the customer compensation program were studied to determine the impact of the customer satisfaction and recommendation. The study analyzed that quality and design of product properties had significant effects on all factors, but the brand was not significantly affected. Second, while superiority, differentiation and scarcity of luxury items are significant to customer satisfaction but superiority is only significant in relation to recommendation intention. Third, the preceding study shows that the customer compensation program has a significant impact on sales growth, but the study found that it was not for imported luxury car customers. Fourth, if the royalties program is low in awareness, it has been analyzed that the scarcity and customer satisfaction relationships among luxury goods have been adjusted. On the contrary, if there is a high level of awareness, it is analyzed that there is a control effect customer satisfaction and differentiation among luxury brands. In the conclusion, in order to satisfy customers at the import luxury car market, the differentiation of luxury goods by standard index must be strengthened and the brand must be strengthened among the attributes of the product. In addition, by raising awareness of the royalties program, the relationship between differentiation and customer satisfaction can be enhanced.

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The ‘cold-start’ problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.

This study was conducted to survey the perception and preferences of customers that have dined at Korean restaurants in China and investigate the importance and performance level of quality attributes, customer satisfaction, revisit intention and recommendation intention. The survey was conducted January 31~March 1, 2016 in China. The 293 questionnaires (97.7%) were analyzed using SPSS(Ver. 23.0) and AMOS(Ver. 21.0). Results of this study are as follow: Customers that dined at a Korean restaurant in China were composed of 157 women and 136 men. Regarding the reason for preferring Korean cuisine, taste, hygiene and nutritional value of Korean food were the most significant quality factors. Regarding complaints about Korean food, Chinese people placed much emphasis on freshness of ingredients when dining out, based on the majority of complaints about ingredients that were not fresh. The main reason for leftover food were personal eating habits and that of customers revisiting food taste and nutrition. Path model among customer satisfaction, revisit intention and recommendation intention revealed the factor of menus and attributes of menu items regarding customer’s age that had an impact on customers’ satisfaction, and association with customers’ satisfaction, revisit intention and recommendation intention as well.

This study was conducted to examine the effects of Chinese perceptions of quality attributes on customer’s satisfaction, revisit intention and recommendation intention for coffee shops in Beijing, China. Subjects of this study included 200 customers who had visited a coffee shop at least once during the last year. Statistical analyses were performed using SPSS v23.0 and AMOS v21.0. In this study, the majority of customers visited a coffee shop once or twice a week with friends. Respondents preferred tall-sized warm coffee in the store. The coffee shop quality attributes of were derived from five exploratory factors identified upon analysis of 30 observational variables. It was important to maintain and strengthen the quality attributes of coffee shops in this area because IPA(Importance Performance Analysis) analysis showed that “Doing great, keep it well” part was a desirable area because it had high importance and performance. Finally, path analysis revealed that customer satisfaction was influenced by employee attitude and affected revisit intention and recommendation intention.

With the increased interest in the quality of life of modern people, the implementation of the five-day working week, the increase in traffic convenience, and the economic and social development, domestic and international travel is becoming commonplace. Furthermore, in the past, there were many cases of purchasing packaged goods of specialized travel agencies. However, as the development of the Internet improved the accessibility of information about the travel area, the tourist is changing the trend to plan the trip such as the choice of the destination. Web services have been introduced to recommend travel destinations and travel routes according to these needs of the customers. Therefore, after reviewing some of the most popular web services today, such as Stubby planner (http://www.stubbyplanner.com) and Earthtory (http://www.earthtory.com), they were supposed to be based on traditional Traveling Salesman Problems (TSPs), and the travel routes recommended by them included some practical limitations. That is, they were not considered important issues in the actual journey, such as the use of various transportation, travel expenses, the number of days, and lodging. Moreover, although to recommend travel destinations, there have been various studies such as using IoT (Internet of Things) technology and the analysis of cyberspatial Big Data on the web and SNS (Social Networking Service), there is little research to support travel routes considering the practical constraints. Therefore, this study proposes a new mathematical model for applying to travel route recommendation service, and it is verified by numerical experiments on travel to Jeju Island and trip to Europe including Germany, France and Czech Republic. It also expects to be able to provide more useful information to tourists in their travel plans through linkage with the services for recommending tourist attractions built in the Internet environment.