• 아태비즈니스연구
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• 제13권3호
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• pp.449-461
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• 2022

Purpose - The purpose of this study to explore factors consumers prioritize when purchasing seafood online. The originality of the study lies on adopting AHP-based approach in analyzing prioritized purchasing factors of seafood online. Design/methodology/approach - A survey was conducted targeting Korean consumers who have purchased seafood online. AHP method was applied to rank factors consumers prioritize before making decision. Findings - First, product's factor ranked first among other high level factors including delivery service, seller, online platform. Second, sanitation, taste, country of origin ranked first, second, third respectively, within product's factors. Third, safe delivery, timeliness, information accuracy ranked first, second, third respectively, within delivery factors. Fourth, consumer reviews, consumer response ability, promotion ranked first, second, third within seller factors. Fifth, Personal information management system, credibility, user-friendliness ranked first, second, third, within online platform factors. Research implications or Originality - To activate seafood online market, it is crucial to assure consumers that the seafood is well managed in a sanitary way from the production site to table. Existing government programs such as seafood traceability system, HACCP, and cold-chain infrastructure needs improvement. Due to highly perishable characteristic of seafood, delivery factors matter when purchasing online. Online platforms needs to continue to improve delivery service. Seafood products are mostly not branded and without objective information about their properties. Creating quality classification and seafood brands are likely to help consumers chose seafood online.

• 한국IT서비스학회지
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• 제14권3호
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• pp.217-236
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• 2015

How does social interaction among investors affect decision-making in the online social lending platform? And what is the reason? In this study, in order to obtain the answer, we carried out case study research of Moneyauction and Popfunding, which are domestic online social lending platforms. We conducted interviews with managements of both social lending platforms and investors and analyzed statistical data including investment records, social interaction history between investors and lenders from both platforms. In addition, researchers performed direct participation and observation through the platforms as real investment members. As a result, we revealed that social interaction among investors has a material impact on the investment decision-making. Also we found that investors build trust by socially interacting with each other and this trust building leads to the investment decision making. Our findings confirm that social lending investors's decision-making process comply with the social embeddedness theory and imply that loan applicants must do their best efforts to display sincerity and truthfulness through their posting.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2022년도 추계학술대회
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• pp.58-58
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• 2022

With the development of advanced technology, automation of agricultural work is spreading. In association with the 4th industrial revolution-based technology, research on field smart farm technology is being actively conducted. A state-of-the-art unmanned automated agricultural production demonstration complex was established in Naju-si, Jeollanam-do. For the operation of the demonstration area platform, it is necessary to build a sophisticated, advanced, and intelligent field smart farming model. For the operation of the unmanned automated agricultural production demonstration area platform, we are building data on the growth of soybean for smart cultivated crops and conducting research to determine the optimal time for agricultural work. In order to operate an unmanned automation platform, data is collected to discover digital factors for water management immediately after planting, water management during the growing season, and determination of harvest time. A subsurface drip irrigation system was established for smart water management. Irrigation was carried out when the soil moisture was less than 20%. For effective water management, soil moisture was measured at the surface, 15cm, and 30cm depth. Vegetation indices were collected using drones to find key factors in soybean production prediction. In addition, major growth characteristics such as stem length, number of branches, number of nodes on the main stem, leaf area index, and dry weight were investigated. By discovering digital factors for effective decision-making through data construction, it is expected to greatly enhance the efficiency of the operation of the unmanned automated agricultural production demonstration area.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1516-1529
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• 2016

While global manufacturing is becoming more competitive due to variety of customer demand, increase in production cost and uncertainty in resource availability, the future ability of manufacturing industries depends upon the implementation of Smart Factory. With the convergence of new information and communication technology, Smart Factory enables manufacturers to respond quickly to customer demand and minimize resource usage while maximizing productivity performance. This paper presents the development of a big data analytics platform architecture for Smart Factory. As this platform represents a conceptual software structure needed to implement data-driven decision-making mechanism in shop floors, it enables the creation and use of diagnosis, prediction and optimization models through the use of data analytics and big data. The completion of implementing the platform will help manufacturers: 1) acquire an advanced technology towards manufacturing intelligence, 2) implement a cost-effective analytics environment through the use of standardized data interfaces and open-source solutions, 3) obtain a technical reference for time-efficiently implementing an analytics modeling environment, and 4) eventually improve productivity performance in manufacturing systems. This paper also presents a technical architecture for big data infrastructure, which we are implementing, and a case study to demonstrate energy-predictive analytics in a machine tool system.

• 유통과학연구
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• 제17권10호
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• pp.73-81
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• 2019

Purpose - The purpose of this study was to examine the effect of the heuristic type of consumers affecting purchase decision making and the intention of shopping value in their relationship to derive mobile social commerce purchase promotion plans. Research design, data, and methodology - A research model was constructed to relate the mediating effect of shopping value between heuristic types and purchase intentions. A total of 233 valid questionnaires were used for analysis for users using mobile social commerce. The statistical program used SPSS 24.0 and AMOS 24.0, and correlation analysis, regression analysis, and 3-step parametric regression analysis were used for the analysis. Results - The results of the analysis showed that representativeness heuristics, availability heuristics, adjustment heuristics, and affect heuristics had a statistically significant effect on the utilitarian value and the hedonic value. On the other hand, affect heuristics among the heuristic types were found to have the greatest influence not only on the utilitarian value but also on the hedonic value. The two types of shopping value were found to be partially mediated between representativeness heuristics and purchase intentions, between adjustment heuristics and purchase intentions, and fully mediated between availability heuristics and purchase intentions, affect heuristics and purchase intentions. Conclusions - These findings suggest that mobile social commerce companies should check in advance how consumer heuristic types affect purchase intentions. In particular, affect heuristics are caused by consumers' emotional mood such as mood or external stimulus being more important to decision making than rational decision making. Therefore, the result of this study suggests that it can be an important factor to secure the competitiveness that the potential customers who access to use mobile social commerce can feel enough fun and enjoyment in the platform provided by the company. It is also worth paying attention to the utilitarian and hedonic values perceived by consumers. This is because the judgment regarding the economic, convenience and important information provided by the mobile social commerce users affects the purchase intention through the trust of the information, past use, and shopping experience displayed on the mobile social commerce platform.

• 제어로봇시스템학회논문지
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• 제20권5호
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• pp.557-562
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• 2014

It is well known that solar tracking systems can increase the efficiency of exiting solar panels significantly. In this paper, a hybrid solar tracking system has been developed by using both astronomical estimates from a GPS and the image processing results of a camera vision system. A decision making process is also proposed to distinguish current weather conditions using camera images. Based on the decision making results, the proposed hybrid tracking system switches two tracking control methods. The one control method is based on astronomical estimates of the current solar position. And the other control method is based on the solar image processing result. The developed hybrid solar tracking system is implemented on an experimental platform and the performance of the developed control methods are verified.

• 정보화정책
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• 제26권1호
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• pp.46-61
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• 2019

4차 산업 혁명은 산업도 교육도 대변혁을 예고하고 있다. 본 연구는 부동산 산업을 위한 교육플랫폼 제안연구로써 부동산의 최유효활용을 위해 Smart Space를 구현하는 인재교육을 목적으로 부동산 산업 인재들이 갖추어야 할 기본 RETech(Real Estate Technology)을 학습할 수 있는 SSEP(Smart Space EduPlatform)을 제안하였다. 우선, SSEP의 생태계는 지속가능성이 확보될 수 있는 기부시스템, 콘텐츠 제작도구 및 학습참여도구 등 다양한 기술적 기능, 학습자 교수자 조력자 형태의 자유로운 학습행위체계로 움직인다. 다음으로 SSEP의 서비스는 학습범주 즉, 계획 및 설계, 의사결정, 관리, 경제, 건설, 설비 6개 범주 하에 17개 중요한 RETech 강의학습 서비스와 PBL(Project-Based Learning)기반의 교육과정서비스를 제공한다. 강의서비스는 동영상 학습 콘텐츠, 부가학습자료, 학습관리 서비스가 제공되고 교육과정서비스는 교수자 워크숍, 학습자 모집 및 등록 관리, 교육과정운영 서비스들이 제공된다.

• 한국산업정보학회논문지
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• 제27권4호
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• pp.127-136
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• 2022

지속적인 기술 발전으로 더욱 다양한 구조의 플랫폼이 등장하고 있다. 기존 연구에서는 기술과 혁신을 기반으로 한 새로운 구조가 양면 시장으로 영향을 미칠 것으로 예측하고 있다. 탈중앙화 신원 확인(DID) 플랫폼은 블록체인 기술을 기반으로 한 새로운 플랫폼으로, 본 연구에서는 양면 시장의 관점에서 그 중요성을 평가하였다. 분석 네트워크 프로세스(Analytic Network Process)를 이용하여, IT, 플랫폼, 블록체인 전문가를 대상으로 쌍대비교 설문을 진행하였고, 일관성 비율 값이 0.1 이하인 데이터를 선별하여 총 12개의 데이터를 대상으로 분석하였다. 연구결과는 서비스 품질, 정책 지원, 개방성, 불확실성 순으로 중요도를 보였다. 본 연구는 블록체인 및 DID 플랫폼 기반 비즈니스 기업에게 전략적 의사결정을 개발하는데 유용한 정보로 활용될 수 있을 것으로 기대된다.

• 한국기후변화학회지
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• 제9권1호
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• pp.1-12
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• 2018

There is an emphasis on the importance of adaptation against to climate change and related natural disasters. As a result, various climate information with different time-scale can be used for science-based climate change adaptation policy. From the aspects of Global Framework for Climate Services (GFCS), various time-scaled climate information in Korea is mainly produced by Korea Meteorological Administration (KMA) However, application of weather and climate information in different application sectors has been done individually in the fields of agriculture and water resources mostly based-on weather information. Furthermore, utilization of climate information including seasonal forecast and climate change projections are insufficient. Therefore, establishment of the Cooperation Center for Application of Weather and Climate Information is necessary as an institutional platform for the UIP (User Interface Platform) focusing on multi-model ensemble (MME) based climate service, seamless climate service, and climate service based on multidisciplinary approach. In addition, APCC Integrated Modeling Solution (AIMS) was developed as a technical platform for UIP focusing on user-centered downscaling of various time-scaled climate information, application of downscaled data into impact assessment modeling in various sectors, and finally producing information can be used in decision making procedures. AIMS is expected to be helpful for the increase of adaptation capacity against climate change in developing countries and Korea through the voluntary participation of producer and user groups within in the institutional and technical platform suggested.

• 한국멀티미디어학회논문지
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• 제21권5호
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• pp.617-625
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• 2018

Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.