• Title/Summary/Keyword: E commerce

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Time series and deep learning prediction study Using container Throughput at Busan Port (부산항 컨테이너 물동량을 이용한 시계열 및 딥러닝 예측연구)

  • Seung-Pil Lee;Hwan-Seong Kim
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2022.06a
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    • pp.391-393
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    • 2022
  • In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

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A study on the aspect-based sentiment analysis of multilingual customer reviews (다국어 사용자 후기에 대한 속성기반 감성분석 연구)

  • Sungyoung Ji;Siyoon Lee;Daewoo Choi;Kee-Hoon Kang
    • The Korean Journal of Applied Statistics
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    • v.36 no.6
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    • pp.515-528
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    • 2023
  • With the growth of the e-commerce market, consumers increasingly rely on user reviews to make purchasing decisions. Consequently, researchers are actively conducting studies to effectively analyze these reviews. Among the various methods of sentiment analysis, the aspect-based sentiment analysis approach, which examines user reviews from multiple angles rather than solely relying on simple positive or negative sentiments, is gaining widespread attention. Among the various methodologies for aspect-based sentiment analysis, there is an analysis method using a transformer-based model, which is the latest natural language processing technology. In this paper, we conduct an aspect-based sentiment analysis on multilingual user reviews using two real datasets from the latest natural language processing technology model. Specifically, we use restaurant data from the SemEval 2016 public dataset and multilingual user review data from the cosmetic domain. We compare the performance of transformer-based models for aspect-based sentiment analysis and apply various methodologies to improve their performance. Models using multilingual data are expected to be highly useful in that they can analyze multiple languages in one model without building separate models for each language.

Design Blockchain as a Service and Smart Contract with Secure Top-k Search that Improved Accuracy (정확도가 향상된 안전한 Top-k 검색 기반 서비스형 블록체인과 스마트 컨트랙트 설계)

  • Hobin Jang;Ji Young Chun;Ik Rae Jeong;Geontae Noh
    • Journal of Internet Computing and Services
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    • v.24 no.5
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    • pp.85-96
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    • 2023
  • With advance of cloud computing technology, Blockchain as a Service of Cloud Service Provider has been utilized in various areas such as e-Commerce and financial companies to manage customer history and distribution history. However, if users' search history, purchase history, etc. are to be utilized in a BaaS in areas such as recommendation algorithms and search engine development, the users' search queries will be exposed to the company operating the BaaS, and privacy issues will be occured. Z. Guan et al. ensure the unlinkability between users' search query and search result using searchable encryption, and based on the inner product similarity, they select Top-k results that are highly relevant to the users' search query. However, there is a problem that the Top-k results selection may be not possible due to ties of inner product similarity, and BaaS over cloud is not considered. Therefore, this paper solve the problem of Z. Guan et al. using cosine similarity, so we improve accuracy of search result. And based on this, we design a BaaS with secure Top-k search that improved accuracy. Furthermore, we design a smart contracts that preserve privacy of users' search and obtain Top-k search results that are highly relevant to the users' search.

LCL Cargo Loading Algorithm Considering Cargo Characteristics and Load Space (화물의 특성 및 적재 공간을 고려한 LCL 화물 적재 알고리즘)

  • Daesan Park;Sangmin Jo;Dongyun Park;Yongjae Lee;Dohee Kim;Hyerim Bae
    • Journal of Intelligence and Information Systems
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    • v.29 no.4
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    • pp.375-393
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    • 2023
  • The demand for Less than Container Load (LCL) has been on the rise due to the growing need for various small-scale production items and the expansion of the e-commerce market. Consequently, more companies in the International Freight Forwarder are now handling LCL. Given the variety in cargo sizes and the diverse interests of stakeholders, there's a growing need for a container loading algorithm that optimizes space efficiency. However, due to the nature of the current situation in which a cargo loading plan is established in advance and delivered to the Container Freight Station (CFS), there is a limitation that variables that can be identified at industrial sites cannot be reflected in the loading plan. Therefore, this study proposes a container loading methodology that makes it easy to modify the loading plan at industrial sites. By allowing the characteristics of cargo and the status of the container to be considered, the requirements of the industrial site were reflected, and the three-dimensional space was manipulated into a two-dimensional planar layer to establish a loading plan to reduce time complexity. Through the methodology presented in this study, it is possible to increase the consistency of the quality of the container loading methodology and contribute to the automation of the loading plan.

Impact of Small Business Entrepreneurs' Absorptive Capacity of Participating in Digital Platform on Market Response: The Moderating Effect of Vicarious Learning and Experiential Learning (디지털 플랫폼 참여 소상공인의 흡수역량이 시장 반응성에 미치는 영향에 대한 연구: 대리 학습과 경험적 학습의 조절 효과 분석)

  • Juhee, Kim;Youngshin, Kim
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.17 no.6
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    • pp.115-125
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    • 2022
  • As the digital economy has emerged as a means of building a new business order and creating new values, the number of small business owners participating in digital platforms is gradually increasing. This study aims to check whether small business owners participating in the digital platform are being helped to properly respond to the market environment and establish and implement strategies necessary for growth through learning within the platform. To this end, this study attempted to examine the effect of the absorptive capacity of small business owners using e-commerce platforms on market orientation and the moderating effect of vicarious learning and experiential learning, which are two types of learning within the platform. As a result of verifying the hypothesis through the survey, it was found that the absorption capacity of small business owners using digital platforms positively affected their market orientation. In addition, as a result of the moderating effect analysis, it was found that vicarious learning within the platform strengthens the relationship between absorptive capacity and market orientation. This result implies that small business owners can not only prepare for market uncertainties through indirect learning (vicarious learning) but also establish strategies to provide products and services that meet the market's needs. On the other hand, the effect of experiential learning was found to lower market orientation, which means that previous business experiences can rather lower attention to the environment. The significance and implications of the study were presented.

Analysis of Global Entrepreneurship Trends Due to COVID-19: Focusing on Crunchbase (Covid-19에 따른 글로벌 창업 트렌드 분석: Crunchbase를 중심으로)

  • Shinho Kim;Youngjung Geum
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.18 no.3
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    • pp.141-156
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    • 2023
  • Due to the unprecedented worldwide pandemic of the new Covid-19 infection, business trends of companies have changed significantly. Therefore, it is strongly required to monitor the rapid changes of innovation trends to design and plan future businesses. Since the pandemic, many studies have attempted to analyze business changes, but they are limited to specific industries and are insufficient in terms of data objectivity. In response, this study aims to analyze business trends after Covid-19 using Crunchbase, a global startup data. The data is collected and preprocessed every two years from 2018 to 2021 to compare the business trends. To capture the major trends, a network analysis is conducted for the industry groups and industry information based on the co-occurrence. To analyze the minor trends, LDA-based topic modelling and word2vec-based clustering is used. As a result, e-commerce, education, delivery, game and entertainment industries are promising based on their technological advances, showing extension and diversification of industry boundaries as well as digitalization and servitization of business contents. This study is expected to help venture capitalists and entrepreneurs to understand the rapid changes under the impact of Covid-19 and to make right decisions for the future.

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Intention to Participate Crowdfunding based on Trust and Perceived Risk: An Exploratory Study with Comparison between Korea and Austria (이용자의 신뢰와 위험인지에 따른 크라우드펀딩(Crowdfunding) 참여의도: 한국과 오스트리아 탐색적 비교 연구)

  • JiHyun Lee;SangAh Park;DongBack Seo
    • Information Systems Review
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    • v.22 no.1
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    • pp.125-146
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    • 2020
  • With the penetration of the Internet and e-commerce, a 'crowdfunding' has emerged as a new way of financing. Crowdfunding has the advantage for a person to able to a simple way to finance her/his an innovative product or service from crowd. However, the success rate for crowdfunding projects is less than half. In this study, we introduce social exchange theory to explore the impact of trust and perceived psychological risk on the intention to participate in a crowdfunding website. Different from previous studies that have focused on a crowdfunding creator, we consider two different perspectives of a project creator and a project supporter. In addition, we compare perceptions of crowdfunding in different cultural contexts by conducting survey in two different countries Korea and Austria. Result shows that trust in recommendation and trust in website have different impacts on the intention to participate from two different perspectives. It also shows that perception of the quality and transparency of information provided by crowdfunding website has greater impact on trust in Korea than that in Austria. In case of perception of psychological risk, it has a negative impact on Austria's intention to create or support a project. On the other hand, it has relatively small impact on the intention to support and does not affect the intention to create a project in Korea.

A Study of Deep Learning-based Personalized Recommendation Service for Solving Online Hotel Review and Rating Mismatch Problem (온라인 호텔 리뷰와 평점 불일치 문제 해결을 위한 딥러닝 기반 개인화 추천 서비스 연구)

  • Qinglong Li;Shibo Cui;Byunggyu Shin;Jaekyeong Kim
    • Information Systems Review
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    • v.23 no.3
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    • pp.51-75
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    • 2021
  • Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

Research on Overheating Prediction Methods for Truck Braking Systems (화물차의 제동장치에서 발생하는 과열 예측방안 연구)

  • Beom Seok Chae;Young Jin Kim;Hyung Jin Kim
    • Smart Media Journal
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    • v.13 no.6
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    • pp.54-61
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    • 2024
  • Recently, due to the increase in domestic and international online e-commerce platforms and the increase in container traffic at domestic ports, the operating ratio of large trucks has increased, and the number of truck fires is continuously increasing. In particular, spontaneous combustion is the most common cause of truck fires. Various academic approaches have been attempted to prevent truck fires, but due to the lack of research on the spontaneous tire ignition phenomenon that occurs during braking, this research directly designed and manufactured an experimental device to establish an environment similar to the braking system of a truck. A non-contact temperature sensor was installed on the brake device of the experimental device to collect temperature data generated from the brake device. Based on the data collected from the temperature sensor of the brake device and the temperature sensor on the tire surface, the ARIMA model among the time series prediction models was used to Appropriate parameters were selected to suit the temperature change trend, and as a result of comparing and analyzing the measured and predicted data, an accuracy of over 90% was obtained. Based on this, a plan was proposed to reduce the rate of fires in trucks by providing real-time warnings and support for truck drivers to respond to overheating phenomena occurring in the braking system.

Electronic Word-of-Mouth in B2C Virtual Communities: An Empirical Study from CTrip.com (B2C허의사구중적전자구비(B2C虚拟社区中的电子口碑): 관우휴정려유망적실증연구(关于携程旅游网的实证研究))

  • Li, Guoxin;Elliot, Statia;Choi, Chris
    • Journal of Global Scholars of Marketing Science
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    • v.20 no.3
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    • pp.262-268
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    • 2010
  • Virtual communities (VCs) have developed rapidly, with more and more people participating in them to exchange information and opinions. A virtual community is a group of people who may or may not meet one another face to face, and who exchange words and ideas through the mediation of computer bulletin boards and networks. A business-to-consumer virtual community (B2CVC) is a commercial group that creates a trustworthy environment intended to motivate consumers to be more willing to buy from an online store. B2CVCs create a social atmosphere through information contribution such as recommendations, reviews, and ratings of buyers and sellers. Although the importance of B2CVCs has been recognized, few studies have been conducted to examine members' word-of-mouth behavior within these communities. This study proposes a model of involvement, statistics, trust, "stickiness," and word-of-mouth in a B2CVC and explores the relationships among these elements based on empirical data. The objectives are threefold: (i) to empirically test a B2CVC model that integrates measures of beliefs, attitudes, and behaviors; (ii) to better understand the nature of these relationships, specifically through word-of-mouth as a measure of revenue generation; and (iii) to better understand the role of stickiness of B2CVC in CRM marketing. The model incorporates three key elements concerning community members: (i) their beliefs, measured in terms of their involvement assessment; (ii) their attitudes, measured in terms of their satisfaction and trust; and, (iii) their behavior, measured in terms of site stickiness and their word-of-mouth. Involvement is considered the motivation for consumers to participate in a virtual community. For B2CVC members, information searching and posting have been proposed as the main purpose for their involvement. Satisfaction has been reviewed as an important indicator of a member's overall community evaluation, and conceptualized by different levels of member interactions with their VC. The formation and expansion of a VC depends on the willingness of members to share information and services. Researchers have found that trust is a core component facilitating the anonymous interaction in VCs and e-commerce, and therefore trust-building in VCs has been a common research topic. It is clear that the success of a B2CVC depends on the stickiness of its members to enhance purchasing potential. Opinions communicated and information exchanged between members may represent a type of written word-of-mouth. Therefore, word-of-mouth is one of the primary factors driving the diffusion of B2CVCs across the Internet. Figure 1 presents the research model and hypotheses. The model was tested through the implementation of an online survey of CTrip Travel VC members. A total of 243 collected questionnaires was reduced to 204 usable questionnaires through an empirical process of data cleaning. The study's hypotheses examined the extent to which involvement, satisfaction, and trust influence B2CVC stickiness and members' word-of-mouth. Structural Equation Modeling tested the hypotheses in the analysis, and the structural model fit indices were within accepted thresholds: ${\chi}^2^$/df was 2.76, NFI was .904, IFI was .931, CFI was .930, and RMSEA was .017. Results indicated that involvement has a significant influence on satisfaction (p<0.001, ${\beta}$=0.809). The proportion of variance in satisfaction explained by members' involvement was over half (adjusted $R^2$=0.654), reflecting a strong association. The effect of involvement on trust was also statistically significant (p<0.001, ${\beta}$=0.751), with 57 percent of the variance in trust explained by involvement (adjusted $R^2$=0.563). When the construct "stickiness" was treated as a dependent variable, the proportion of variance explained by the variables of trust and satisfaction was relatively low (adjusted $R^2$=0.331). Satisfaction did have a significant influence on stickiness, with ${\beta}$=0.514. However, unexpectedly, the influence of trust was not even significant (p=0.231, t=1.197), rejecting that proposed hypothesis. The importance of stickiness in the model was more significant because of its effect on e-WOM with ${\beta}$=0.920 (p<0.001). Here, the measures of Stickiness explain over eighty of the variance in e-WOM (Adjusted $R^2$=0.846). Overall, the results of the study supported the hypothesized relationships between members' involvement in a B2CVC and their satisfaction with and trust of it. However, trust, as a traditional measure in behavioral models, has no significant influence on stickiness in the B2CVC environment. This study contributes to the growing body of literature on B2CVCs, specifically addressing gaps in the academic research by integrating measures of beliefs, attitudes, and behaviors in one model. The results provide additional insights to behavioral factors in a B2CVC environment, helping to sort out relationships between traditional measures and relatively new measures. For practitioners, the identification of factors, such as member involvement, that strongly influence B2CVC member satisfaction can help focus technological resources in key areas. Global e-marketers can develop marketing strategies directly targeting B2CVC members. In the global tourism business, they can target Chinese members of a B2CVC by providing special discounts for active community members or developing early adopter programs to encourage stickiness in the community. Future studies are called for, and more sophisticated modeling, to expand the measurement of B2CVC member behavior and to conduct experiments across industries, communities, and cultures.