• The Journal of the Korea institute of electronic communication sciences
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• v.2 no.2
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• pp.136-141
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• 2007

To produce eco marine products which is taking interests recently, the necessity of aquafarm tracing system is being emphasized. Considering the particularity of aquafarm, wireless communication environment is essential in order to manage aquafarm efficiently. The terminal which automatically collecting the environmental information and transferring it to server needs specified functions; input-output the information conveniently and search it visually. In this paper, we design specified terminal and its functions to monitor the environmental information at aquafarm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.383-386
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• 2005

This study proposes a case based reasoning system with two dimensional reduction techniques. In this study, vertical and horizontal dimensions of the research data are reduced through hybrid feature and instance selection process using genetic algorithms. We applied the proposed model to customer classification model which utilizes customers' demographic characteristics as inputs to predict their buying behavior for the specific product. Experimental results show that the proposed technique may improve the classification accuracy and outperform various optimized models of typical CBR system.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.61-68
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• 2021

The paper is based on data collected from the Amazon website (specific in the Handmade's Category) to understand and analyze Vietnamese artisans' business context. Data analysis is also applied to determine the factors that bring success Handmade products and compare products of the same industry among competitors to find out potential products. By collecting data from Amazon and analyzing the data, we extracted useful information for online business developers. Besides, the list of potential products in Handmade sector can be referred to improve the business and compete with competitors. This paper also proposes solutions to help Vietnamese products become more appealing to international customers on the Amazon website.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.6
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• pp.890-901
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• 2008

This study explores an initial framework for online product categorization by examining the relationships among Internet motivations, buying tendencies, and online purchase intentions for product categories. A total of 217 usable questionnaires were obtained from respondents in a southwestern state in the United States. A path model using a correlation matrix with maximum likelihood was estimated using LISREL 8.53. Findings indicated that Internet motivations consisted of four factors: Diversion, Economic, Information, and Social motivations. In addition, online products were classified into three categories based on purchase intentions: Sensory, Cognitive, and Search products. Estimated path model showed that diversion and economic motivations affected impulse buying tendency, whereas economic, information and social motivations influenced planned buying tendency in the online context. Also, the buying tendencies were significantly related to online purchase intentions for the product categories. Purchase intentions for sensory products were more strongly affected by impulse buying tendency, whereas purchase intentions for cognitive and search products were more strongly affected by planned buying tendency. Theoretical and managerial implications were discussed for devising an appropriate e-market strategy for specific product categories.

• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.224-243
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• 1997

The study examines the similarities of competitive strategy between fashion industry and high-tech industry through a case study of a Japanese maker. From the study, some implications are drawn for the Korean fashion industry. It is hoped that this, will help towards establishing a suitable competitive strategy for firms in this industry. In the fashion industry, the product life cycle is so short as to prompt a new product obsolete too quickly, and the extent of product differentiation is remarkably extensive compared to any other industry. Generally speaking, firms in this industry focus more of their resources on product development and marketing rattler than on production and they attempt to maneuver the speed when they are required to enhance their competitive edge. This is enabled through being, as one might expect, information- and technology- intensive as are high-tech industries. In this sense, that of the competitive strategy of a firm in fashion industry to be similar to high-tech industry. The Japanese firm Y has transformed itself a leading firm in fashion uniform segment. The firm could achieve this status by integrating each function needed for creating customer*s value, that is, product development, production and marketing within one Quick Response System. For this purpose, Y introduced a bundle of high-tech communication systems such as SPD, SDS, ATOM, NICS and so on. In this sense it can be said that Y was aware of what sort of competitive strategy was required in the industry. Implications for Korean firms is that, first, the magnitude of understanding the industry specific factors in establishing competitive strategy in the fashion industry, are speed, flexibility and systematic integration supported by high technology which are characteristic of high-tech industries. Secondly, as can be seen in the fact that Y emphasized logistics in its technological transformation, the significance of logistics control is a key to manipulating speed and flexibility in the industry. To sum up, those who have insight into above findings will be likely to keep their competitiveness in the industry not only in the Korean market but also in global market in the near future.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.103-128
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• 2013

When a consumer needs a product or service and multiple sellers are available online, the process of selecting a seller to buy online from is complex since the process involves many behavioral dimensions that have to be taken into account. As a part of this selection process, consumers may set minimum trust expectation that can be used to screen out less trustworthy sellers. In the previous research, the level of consumers' trust expectation has been anchored on two important factors: product involvement and perceived risk. Product involvement refers to the extent to which a consumer perceives a specific product important. Thus, the higher product involvement may result in the higher trust expectation in sellers. On the other hand, other related studies found that when consumers perceived a higher level of risk (e.g., credit card fraud risk), they set higher trust expectation as well. While abundant research exists addressing the relationship between product involvement and perceived risk, little attention has been paid to the integrative view of the link between the two constructs and their impacts on the trust expectation. The present paper is a step toward filling this research gap. The purpose of this paper is to understand the process by which a consumer chooses an online merchant by examining the relationships among product involvement, perceived risk, trust expectation, and intention to buy from an e-tailer. We specifically focus on the mediating role of perceived risk in the relationships between enduring product involvement and the trust expectation. That is, we question whether product involvement affects the trust expectation directly without mediation or indirectly mediated by perceived risk. The research model with four hypotheses was initially tested using data gathered from 635 respondents through an online survey method. The structural equation modeling technique with partial least square was used to validate the instrument and the proposed model. The results showed that three out of the four hypotheses formulated were supported. First, we found that the intention to buy from a digital storefront is positively and significantly influenced by the trust expectation, providing support for H4 (trust expectation ${\rightarrow}$ purchase intention). Second, perceived risk was found to be a strong predictor of trust expectation, supporting H2 as well (perceived risk ${\rightarrow}$ trust expectation). Third, we did not find any evidence of direct influence of product involvement, which caused H3 to be rejected (product involvement ${\rightarrow}$ trust expectation). Finally, we found significant positive relationship between product involvement and perceived risk (H1: product involvement ${\rightarrow}$ perceived risk), which suggests that the possibility of complete mediation of perceived risk in the relationship between enduring product involvement and the trust expectation. As a result, we conducted an additional test for the mediation effect by comparing the original model with the revised model without the mediator variable of perceived risk. Indeed, we found that there exists a strong influence of product involvement on the trust expectation (by intentionally eliminating the variable of perceived risk) that was suppressed (i.e., mediated) by the perceived risk in the original model. The Sobel test statistically confirmed the complete mediation effect. Results of this study offer the following key findings. First, enduring product involvement is positively related to perceived risk, implying that the higher a consumer is enduringly involved with a given product, the greater risk he or she is likely to perceive with regards to the online purchase of the product. Second, perceived risk is positively related to trust expectation. A consumer with great risk perceptions concerning the online purchase is likely to buy from a highly trustworthy online merchant, thereby mitigating potential risks. Finally, product involvement was found to have no direct influence on trust expectation, but the relationship between the two constructs was indirect and mediated by the perceived risk. This is perhaps an important theoretical integration of two separate streams of literature on product involvement and perceived risk. The present research also provides useful implications for practitioners as well as academicians. First, one implication for practicing managers in online retail stores is that they should invest in reducing the perceived risk of consumers in order to lower down the trust expectation and thus increasing the consumer's intention to purchase products or services. Second, an academic implication is that perceived risk mediates the relationship between enduring product involvement and trust expectation. Further research is needed to elaborate the theoretical relationships among the constructs under consideration.

• Information Systems Review
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• v.8 no.1
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• pp.181-201
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• 2006

As the Internet spreads, many people have interests in e-CRM and product recommender systems, one of e-CRM applications. Among various approaches for recommendation, collaborative filtering and content-based approaches have been investigated and applied widely. Despite their popularity, traditional recommendation approaches have some limitations. They require at least one purchase transaction per user. In addition, they don't utilize much information such as demographic and specific personal profile information. This study suggests new hybrid recommendation model using two data mining techniques, association rule and classification, as well as intelligent agent to overcome these limitations. To validate the usefulness of the model, it was applied to the real case and the prototype web site was developed. We assessed the usefulness of the suggested recommendation model through online survey. The result of the survey showed that the information of the recommendation was generally useful to the survey participants.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.77-85
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• 2010

As various convergency products have been actively developed, the study on user interface has been conducted a lot, and for more specific direction, users' experience-oriented user interface from user-oriented studies is recently developed. Such a tendency aiming to focus on product development to express users' emotion, the next step in the user-oriented development had difficulties in an objective approach, so the data based on previous users' experiences were presented as the basic data to establish user interface design process with grounds and design direction, and therefore it is available to show more specific and objective grounds. From this perspective, such psychological variables showing users' experiences like age and job are studied through surveys of users at the development of products, and products according to the variables are released. On the other hand, the products considering psychological difference distinguishing users' experiences as the cultural cap are not progressed yet. Despite the understanding of cultural difference, its decisive grounds are hard to distinguish like age, and job. Therefore, the cognitive concept about how to design menu information structure according to accident types that can be considered regarding user interface design among theoretical backgrounds about cultural difference. As the category according to the range of things among accident types, it is divided into analytical type and relational type to conduct a test on similarity and relations about the representative digital TV's menu information of the convergency product. As the result, analytical type and relational type showed difference and this study aims to use menu information concept considering this difference as explanatory variables of the users' experience-oriented development.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.