• International Commerce and Information Review
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• v.13 no.3
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• pp.433-457
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• 2011

This study attempts to make a constructive contribution to the debate on which WTO rules accommodate trade measures for environmental purposes. Does trade undermine the regulatory efforts of governments. However, the theoretical dimensions are partly addressed on the several key questions. For instances, is economic integration through trade and investment a threat to the environment? to control pollution and resource degradation? Will economic grow driven by trade help us to move towards a sustainable use of the world's environmental resources? The growing world economy has been accompanied by environmental degradation including deforestation, losses in bio-diversity, global warming, air pollution, depletion of the ozone layer, overfishing and so on. The sheer number of us obviously put pressure on natural resources and ecological systems, and this pressure will counting to rise as we grow towards 10 billion in the next century. What is more, there is no indication that consumption per capita is slowing. The perceived costs of acting alone in terms of lost investments and jobs often take the stream out of regulatory initiatives. In the worst case scenario environmental community is fearful that international trade will magnify the effects of poor environmental polices in the world Generally, economic growth drive by trade may speed up the process of environmental degradation unless sufficient environmental safeguards are put in place. Under these circumstances, this paper attempts to make a constructive contribution to the study on which WTO rules accommodate trade measures for environmental purposes.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.1059-1066
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• 2003

In this paper, the magnetic sensing system is designed and implemented for the safe security in internet commerce system. When the payment is required inthe internet commerce system, the magnetic sensing system will get the information from a credit card without keyboard input and then encrypt and transmit the information to server. The credit card sensing system, which is proposed in this paper, is safe from keyboard hacking because it encrypts card information immediately in its internal chip and sends the information to host system. For the protection of information, the magnetic sensing system is basically based on a synchronous stream cipher cryptosystem which is related to a group of matrices. The size of matrices and the bits of keys for the best performances are determined for various cases. It is shown that for credit card payments. matrices of size 2 have good performance even at most 128bits keys with the consideration of inverse matrices. For authentication of general-purpose data, the magnetic sensing system needs more than 1.5KB data and in this case, the optimum size of matrices is 2 or 3 at more 256bits keys with consideration of inverse matrices.

• Journal of Information Technology Applications and Management
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• v.28 no.4
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• pp.59-78
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• 2021

Mobile Grocery Shopping (MGS) has become the New Normal as the COVID-19 pandemic has changed the way consumers shop. Drawing on the framework of Customer Journey Map (CJM), this study explores consumers' MGS by identifying specific stages of Customer Journey and comparing consumers' satisfaction between PC-based online and mobile shopping experiences at each stage throughout the journey. This study collected 562 responses from subjects who have mobile and PC-based grocery shopping experiences at the major domestic e-Commerce platforms. Independent t-test analysis showed that differences in satisfaction between mobile and online shopping experiences exist in 5 main stages and 16 sub-stages of CJM. The results of service and technological innovation mentioned in the actual industry report were seen as empirical results leading to continued use of MGS as well as customer satisfaction. The findings of this study contribute to the research stream on Customer Journey by adopting the structure of CJM and analyzing specific stages of the journey in the context of MGS. Managerial implications for mobile-based business practitioners are also discussed.

• Asia pacific journal of information systems
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• v.25 no.2
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• pp.265-288
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• 2015

IT convergence services, as the main stream of the digital age, are currently on their way to include the concept of Software as a Service (SaaS), where IT products and services are integrated as one. In particular, the recently introduced web-service-based SaaS is expected to be a more developed SaaS model. This new model provides greater influence on clients' job performances than its previous models, such as application service providers and the web-native phase. However, the effects of technology maturity on task performance have been overlooked in adoption and performance studies. Accordingly, this study introduces SaaS technology maturity as the exogenous technological characteristic influencing job performance. This study also examines the relationships among various SaaS-related performances according to the different levels of SaaS maturity. Results suggest that applying innovative technologies (such as SaaS), particularly when the technology reaches a certain level of maturity, is more helpful for managers in improving task-technology fit and job performance. This study makes an academic contribution by establishing and validating a performance model empirically with SaaS technology maturity perspectives.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.97-106
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• 2010

Can eBay.com's fee structure coordinate the channel? It's a critical strategic problem in e-commerce operations and an interesting research hypothesis as well. eBay's fees include three parts: monthly subscription fee, insertion fee, and final value fee (i.e., a revenue sharing portion), which represent a generic form of revenue sharing fee structure between the retailer and the vendor in a supply chain. This research deals with such a channel consisting of a price-setting vendor who sells products through eBay's marketplace exclusively to the end customers. The up- and down-stream channel relationship is consignment-based revenue sharing. We use a game-theoretic approach with assumption of the retailer (i.e., eBay.com) being a Stackelberg-leader and the vendor being a follower. The Stackelberg-leader decides on the terms of revenue sharing contract (i.e., fee structure), and the follower (vendor) decides on how many units to sell and the items' selling price. This study formulates several profit-maximization models by considering the effects of the retail price on the demand function. Under such settings, we show that eBay's fee structure can improve the channel efficiency; yet it cannot coordinate the channel optimally.

• Journal of Information Technology Services
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• v.23 no.2
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• pp.63-81
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• 2024

With the advancement of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), the metaverse has emerged as a transformative model across various sectors, offering a three-dimensional virtual world where activities mirroring the real world occur. This study delves into the significant factors influencing fashion brand companies' investments in the metaverse, an evolved concept from Virtual Reality (VR) that extends beyond gaming to include real-life activities through avatars. This study highlights the surge in virtual fashion engagements, as evidenced by increased avatar updates and purchases of digital fashion items on platforms like Roblox. Luxury brands are steadily entering the metaverse indicating a new revenue stream within the fashion industry. This study employs a mixed-methods approach, integrating text mining and interviews to identify key factors for fashion companies considering metaverse investments. By proposing strategies based on these findings, this study not only enriches academic discourse in fashion, e-commerce, and information systems but also serves as a guideline for fashion companies aiming to navigate the burgeoning digital market, contributing to the generation of new revenue streams in the fashion sector.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.35-46
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• 2010

Sequential pattern mining is one of the essential data mining tasks, and it is widely used to analyze data generated in various application fields such as web-based applications, E-commerce, bioinformatics, and USN environments. Recently data generated in the application fields has been taking the form of continuous data streams rather than finite stored data sets. Considering the changes in the form of data, many researches have been actively performed to efficiently find sequential patterns over data streams. However, conventional researches focus on reducing processing time and memory usage in mining sequential patterns over a target data stream, so that a research on mining more interesting and useful sequential patterns that efficiently reflect the characteristics of the data stream has been attracting no attention. This paper proposes a mining method of sequential patterns over data streams with a gap constraint, which can help to find more interesting sequential patterns over the data streams. First, meanings of the gap for a sequential pattern and gap-constrained sequential patterns are defined, and subsequently a mining method for finding gap-constrained sequential patterns over a data stream is proposed.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.29-37
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• 2006

Due to the spread of mobile communication with the development of computer network, nowadays various types of multimedia data play an important role in many areas such as entertainments, culture contents, e-commerce or medical science. But for the real application of these data, the security in the course of saving or transferring them through the public network should be assured. In this sense, many encryption algorithm have been developed and utilized. Nonetheless, most of them have focused on the text data. So they may not be suitable to the multimedia application because of their large size and real time constraint. In this paper, a chaotic map has been employed to create a symmetric stream type of encryption scheme which may be applied to the digital images with a large amounts of data. Then an efficient algebraic encryption algorithm based on the elementary operations of the Boolean matrix and image data characteristics.

• The KIPS Transactions:PartC
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• v.8C no.5
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• pp.585-596
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• 2001

In this paper, a new stream cipher algorithm based on the chaos theory is proposed and is applied to a Web security system. The Web security system is composed of three parts： certificate authority (CA), Web client, and Web server. The Web client and server system include a secure proxy client (SPC) and a secure management server (SMS), respectively, for data encryption and decryption between them. The certificate is implemented based on X.509 and the RSA public key algorithm is utilized for key creation and distribution to certify both the client and server. Once a connection is established between the client and server, outgoing and incoming data are encrypted and decrypted, respectively, using one of the three cipher algorithms： chaos, SEED, and DES. The proposed chaos algorithm outperforms the other two conventional algorithms in processing time and complexity. Thus, the developed Web security system can be widely used in electronic commerce (EC) and Internet banking.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.85-107
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• 2019

Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.