• Human Ecology Research
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• 제56권6호
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• pp.619-626
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• 2018

This study investigated the impact of the amount of time online on cyberbullying perpetration of middle school students as well as examined if the justice sensitivity (victim sensitivity and penetrator sensitivity) moderated the relationship between the amount of time online and cyberbullying perpetration. The participants in this study were 236 students (120 boys and 116 girls) from two middle schools located in Seoul and Incheon. The levels of cyberbullying perpetration and justice sensitivity were measured by scales developed by Campfield (2008) and Schmitt et al. (2010), respectively. The participants were also asked to report on how much time they spent online a day. The data were analyzed via descriptive statistics, hierarchical regression, and procedures mentioned by Baron and Kenny (1986). The results revealed that the more the students used the Internet, the more likely they were to become a cyberbullying perpetrator. However, such a tendency was observed only for the students who had a higher level of victim sensitivity, and not for those with a lower level of victim sensitivity. This suggested that victim sensitivity moderated the effect of the amount of time spent on the Internet on cyberbullying perpetration; but, penetrator sensitivity had no moderating effect.

• Industrial Engineering and Management Systems
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• 제1권1호
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• pp.64-72
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• 2002

In the field of plant maintenance, data that are gathered by sensors on multiple machines are handled and analyzed. Online or pseudo online data handling is required on such fields. When the data occurrence speed exceeds the data handling speed, multiple data should be handled at a time (batch data handling or pseudo online data handling). If l amount of data are received at one time following N amount of data, how to estimate the new parameters effectively is a great concern. A new simplified calculation method, which calculates the N data's weights, is introduced. Numerical examples show that this new method has a fairly god estimation accuracy and the calculation time is less than 1/10 compared with the case when the whole data are re-calculated. Even under the restriction calculation ability in the apparatus is limited, this proposed method makes the failure detection of equipments possible in early stages with a few new coming data. This method would be applicable in many data handling fields.

• 융합경영연구
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• 제9권2호
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• pp.1-10
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• 2021

Purpose: In this work, we examine several time series models to predict internet and mobile transaction amount in South Korea, whereas Jeong (2020) has obtained the optimal forecasts for online shopping transaction amount by using time series models. Additionally, optimal forecasts based on the model considered can be calculated and applied to the Corona 19 situation. Research design, data, and methodology: The data are extracted from the online shopping trend survey of the National Statistical Office, and homogeneous and comparable in size based on 46 realizations sampled from January 2007 to October 2020. To achieve the goal of this work, both multiplicative ARIMA model and Holt-Winters Multiplicative seasonality method are taken into account. In addition, goodness-of-fit measures are used as crucial tools of the appropriate construction of forecasting model. Results: All of the optimal forecasts for the next 12 months for two online shopping transactions maintain a pattern in which the slope increases linearly and steadily with a fixed seasonal change that has been subjected to seasonal fluctuations. Conclusions: It can be confirmed that the mobile shopping transactions is much larger than the internet shopping transactions for the increase in trend and seasonality in the future.

• 한국정보시스템학회지:정보시스템연구
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• 제26권4호
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• pp.327-353
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• 2017

Purpose Social media such as social network services, online forums, and customer reviews have produced a plethora amount of information online. Yet, the information deluge has created both opportunities and challenges at the same time. This research particularly focuses on the challenges in order to discover and track the service defects over time derived by mining publicly available online customer reviews. Design/methodology/approach Synthesizing the streams of research from text analytics, we apply two stages of methods of sentiment analysis and structural topic model incorporating meta-information buried in review texts into the topics. Findings As a result, our study reveals that the research framework effectively leverages textual information to detect, prioritize, and categorize service defects by considering the moving trend over time. Our approach also highlights several implications theoretically and practically of how methods in computational linguistics can offer enriched insights by leveraging the online medium.

• 한국의류학회지
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• 제36권3호
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• pp.282-296
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• 2012

This study identified the types of motives for reading consumer reviews of apparel products for online stores and classified shoppers into the groups based on motives. Data were collected from eleven Korean women by a focus group interview and from 313 females by an online survey. Respondents were in their 20s' and 30s' with significant experience reading consumer reviews of apparel products for online stores. The seven motives found by interviews were reduced to four types of motives by factor analysis: Right product choice and judgment of product value, risk reduction, saving time and money, and fun/killing time. The motive for the right product choice and judgment of product value was the highest and the motive for fun/killing time was the lowest. Consumers were classified into four groups based on motives: Utilitarian shoppers (25.8%), shopping-task oriented shoppers (36.8%), multiple-motive shoppers (19.7%), and moderate-motive shoppers (17.7%). There were significant differences among age groups and the amount of reading reviews posted on a product and the duration of reading reviews for online stores. In addition, managerial implications were developed.

• ETRI Journal
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• 제34권2호
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• pp.288-291
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• 2012

In this letter, a new online anomaly detection approach for software systems is proposed. The novelty of the proposed approach is to apply a new semantic kernel function for a support vector machine (SVM) classifier to detect fault-suspicious execution paths at runtime in a reasonable amount of time. The kernel uses a new sequence matching algorithm to measure similarities among program execution paths in a customized feature space whose dimensions represent the largest common subpaths among the execution paths. To increase the precision of the SVM classifier, each common subpath is given weights according to its ability to discern executions as correct or anomalous. Experiment results show that compared with the known kernels, the proposed SVM kernel will improve the time overhead of online anomaly detection by up to 170%, while improving the precision of anomaly alerts by up to 140%.

• 한국컴퓨터정보학회논문지
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• 제25권9호
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• pp.203-212
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• 2020

본 연구는 외국인 학습자들이 경험한 비대면 온라인 학부 강의에 대한 인식을 분석하여 온라인 강의의 문제점을 파악하고 개선안을 제안하는 데 목적이 있다. 연구를 위해 A 대학과 B 대학에서 온라인 강의를 수강한 외국인 학부생들을 대상으로 온라인 강의에 대한 인식을 조사하여 분석하였다. 이를 통해 앞으로 한국 대학에서 진행될 온라인 강의의 설계 방향과 보완책, 그리고 나아갈 방향 등을 탐색해 보았다. 본 연구의 결과, E 캠퍼스를 통한 비실시간 강의는 강의를 반복해서 학습할 수 있고 집에서 강의를 들을 수 있는 점을 장점으로 인식하고 있었다. Zoom을 활용한 실시간 강의는 교수-학습자간 소통이 가능함을 장점으로 인식하고 있었다. 두 가지 유형의 강의 모두 과제가 많고 강의가 끝날 때까지 지속적으로 집중하기 어려움을 단점으로 인식하고 있었다. 또한 앞으로의 고려할 점으로 강의 내용의 양과 과제의 양, 그리고 강의 영상의 음질과 상태임을 알수 있었다. 평가 방식으로는 오프라인 평가보다는 온라인 평가를 선호하였으며 절대 평가보다는 상대 평가를 선호하는 것으로 나타났다. 본 연구의 결과가 각 대학의 온라인 강의의 설계 방향과 온라인 콘텐츠 개발에 작은 보탬이 되기를 바란다.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.193-197
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• 2021

Customers are increasingly attracted towards different e-commerce websites and applications for the purchase of products significantly. This is the reason the sellers are moving to different internet based services to sell their products online. The growth of customers in this sector has resulted in the use of big data analytics to understand customers' behavior in predicting the demand of items. It uses a complex process of examining large amount of data to uncover hidden patterns in the information. It is established on the basis of finding correlation between various parameters that are recorded, understanding purchase patterns and applying statistical measures on collected data. This paper is a document of the bottom-up strategy used to manage the selling price of a first-time product for maximizing profit while selling it online. It summarizes how existing customers' expectations can be used to increase the sale of product and attract the attention of the new customer for buying the new product.

• Management Science and Financial Engineering
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• 제20권2호
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• pp.27-31
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• 2014

This paper studies the assortment optimization problem in online retailing by using a multinomial logit model in order to take consumer choice behavior into account. We focus on two unique features of online purchase behavior: first, there exists increased amount of uncertainty (e.g., size and color of merchandize) in online shopping as customers cannot experience merchandize directly. This uncertainty is captured by the scale parameter of a Gumbel distribution; second, online shopping entails unique shopping-related disutility (e.g., waiting time for delivery and security concerns) compared to offline shopping. This disutility is controlled by the changes in the observed part of utility function in our model. The impact of changes in uncertainty and disutility on the expected profit does not exhibit obvious structure: the expected profit may increase or decrease depending on the assortment. However, by analyzing the structure of the optimal assortment based on convexity property of the profit function, we show that the cardinality of the optimal assortment decreases and the maximum expected profit increases as uncertainty or disutility decreases. Therefore, our study suggests that it is important for managers of online retailing to reduce uncertainty and disutility involved in online purchase process.

• 대한수학회지
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• 제50권4호
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• pp.879-897
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• 2013

Shannon entropy is one of the widely used randomness measures especially for cryptographic applications. However, the conventional entropy tests are less sensitive to the inter-bit dependency in random samples. In this paper, we propose new online randomness test schemes for true random number generators (TRNGs) based on the mutual information between consecutive ${\kappa}$-bit output blocks for testing of inter-bit dependency in random samples. By estimating the block entropies of distinct lengths at the same time, it is possible to measure the mutual information, which is closely related to the amount of the statistical dependency between two consecutive data blocks. In addition, we propose a new estimation method for entropies, which accumulates intermediate values of the number of frequencies. The proposed method can estimate entropy with less samples than Maurer-Coron type entropy test can. By numerical simulations, it is shown that the new proposed scheme can be used as a reliable online entropy estimator for TRNGs used by cryptographic modules.