Search Results (5)

According to the U.S. Department of Commerce, there is strong evidence of a significant bounce in global e-commerce sales following the paradigm shift caused by COVID-19 in everyday life. The global e-commerce market size is projected to reach $6.33 trillion in 2024, with the top five countries being China, the United States, the United Kingdom, Japan, and the Republic of Korea (hereafter, Korea). Among the top five global players, two countries, Korea and Japan, are particularly noteworthy because Korea has the lowest retail e-commerce sales among the top five countries despite a high penetration rate while Japan shows the lowest penetration rate while maintaining a similar level of retail e-commerce sales. The e-commerce industry in Korea and Japan is facing a new inflection point regarding sustainable growth and survival. However, the sustainability of e-commerce firms in Korea and Japan has not yet been investigated from a comparative perspective. In the context of an extremely challenging global business environment, this study focuses on a representative e-commerce firm from each country: Coupang in Korea and Rakuten in Japan. By examining these cases, this study provides important insights into sustainable management practices in the e-commerce industry from a comparative perspective. Full article

The Web has provided an increasing proportion of use as a medium for e-commerce in addition to various recommender systems. It can be used for analyzing recommendation system-based feedback (e.g., a form in which a user inputs their preferences for various items as numerical values into a specific evaluation system) to estimate customer interest; in addition, analyzing multi-modal types of feedback (e.g., product purchase traces, inquiry lists, inquiry times, and comments) with deep learning can also be used to estimate user interest. As many companies around the world promote their products through micro-influencers on the Web, related research has continued to predict the purchase conversion rate of the influencer through a variety of technologies. In this work, we present a multi-modal micro-influencer analysis scheme for a marketing maximization strategy. Our scheme uses the multi-modal data stored in Mecha Solution’s own shopping mall of Korea, as well as famous Korean Internet platforms, and Coupang, Naver, and Oliveyoung’s data such as article posting comments and statistics information. By extracting the main characteristics of the real article postings from real users as opposed to those from factitious influencers posting articles and comments and identifying articles other than advertisements, influencer scores are obtained, assuming that articles other than advertisements can further increase the purchase conversion rate. Based on influencer score, we propose a multi-modal micro-influencer analysis scheme that recommends influencers use content-based collaborative filtering and user-based collaborative filtering for items that the influencer has not yet reviewed. The experiment was implemented to prove that the proposed scheme successfully achieves this goal. Full article

The increasing use and distribution of chemicals are causing serious chemical accidents such as fires, explosions, and leaks during manufacturing and handling. In most cases, all risks caused by chemicals are classified as accidents due to defects in process facilities, human errors, and multi-cause accidents. Among chemical accidents caused by human errors, accurate analysis of accidents caused by the complex action of various types of human failures is required. Based on the accident investigation reports that occurred in South Korea from 2010 to 2017, chemical accidents caused by human failure were analyzed, and the fundamental causes were derived by classifying them into human error and violation. Human failure was analyzed according to the classification criteria for human failure of health and safety executive (HSE). As a result of the analysis, several types of human failure acted in combination to be a more significant cause of chemical accidents; incorrect application of process rules and procedures, inappropriate chemical information, lack of education, and defects in the current safety regulation were analyzed as the main causes. In addition, the cause of human errors was presented through case studies of chemical accidents in South Korea. Full article

A distributed trigger counting (DTC) problem is to detect w triggers in the distributed system consisting of n nodes. DTC algorithms can be used for monitoring systems using sensors to detect a significant global change. When designing an efficient DTC algorithm, the following goals should be considered; minimizing the whole number of exchanged messages used for counting triggers and even distribution of communication loads among nodes. In this paper, we present an efficient DTC algorithm, DDR-coin (Deterministic Detection of Randomly generated coins). The message complexity—the total number of exchanged messages—of DDR-coin is $O\left(n{log}_n(w/n)\right)$ in average. MaxRcvLoad—the maximum number of received messages to detect w triggers in each node—is $O\left({log}_n(w/n)\right)$ on average. DDR-coin is not an exact algorithm; even though w triggers are received by the n nodes, it can fail to raise an alarm with a negligible probability. However, DDR-coin is more efficient than exact DTC algorithms on average and the gap between those is increased for larger n. We implemented the prototype of the proposed scheme using NetLogo 6.1.1. We confirmed that experimental results are close to our mathematical analysis. Compared with the previous schemes—TreeFill, CoinRand, and RingRand— DDR-coin shows smaller message complexity and MaxRcvLoad. Full article

As e-commerce is rapidly expanding, efficient and competitive product delivery system to the final customer is highly required. Recently, the emergence of a smart platform is leading the transformation of distribution, performance, and quality in express delivery services, especially in the last-mile delivery. The business to consumer (B2C) through smart platforms such as Amazon in America and Coupang in Korea utilizes the differentiated delivery rates to increase the market share. In contrast, the small and medium-sized express delivery companies with low market share are trying hard to expand their market share. In order to fulfill all customer needs, collaboration is needed. This study aims to construct a collaboration model to maximize the net profit by considering the market density of each company. A Baduk board game is used to derive the last-mile delivery time function of market density. All companies in collaboration have to specialize the delivery items into certain service clustering types, which consist of regular, big sized/weighted, and cold items. The multi-objective programming model is developed based on max-sum and max-min criteria. The Shapley value and nucleolus approaches are applied to find the profit allocation. Finally, the applicability of the proposed collaboration model is shown through a numerical example. Full article