• The Journal of Society for e-Business Studies
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• v.2 no.1
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• pp.1-20
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• 1997

This study aims to suggest CALS implementation strategies and policies for information-based management of small and medium companies in Korea. At the turning point from traditional document-based management to recent digital-based one, it is well known that implementation of CALS concept is crucial for advancing business management of small and medium enterprises In order to attack the aim, this paper critically analyzes the empirical difficulties and obstacles of the current information-based management of small and medium companies in Korea. On the basis of the above analysis, this paper suggests the strategic plans and policies of CALS implementation for small and medium enterprises in Korea as follows. First, government should provide the supporting policies and proper system so that the large enterprise can be linked with small and medium companies for sharing necessary information. Second, similar enterprises should be integrated on the basis of information and automation evaluation. Third, implementation strategies and plans should be advanced on the basis of the informationalized phases with respect to the technology level of small and medium enterprises. For more efficient CALS implementation, this paper also proposes the following subsidiary policies. First, it is substantially important to publicize the nation-wide spreading of CALS mind. Second, it is strongly recommended to educate and train CALS specialist on a consistant basis. Third, government should support the enterprises by providing sufficient fund for CALS implementation. Fourth, the ideal CALS implementation models for small and medium enterprises should be developed. Fifth, the consulting and training program for CALS implementation should be established through ECRC (Electronic Commerce Resource Center). My study was based upon the enterprises' responses to the questionaires I made

• Journal of Information Technology Applications and Management
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• v.16 no.1
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• pp.1-20
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• 2009

Negotiation is a process of reaching an agreement on the terms of a transaction. such as price, quantity, for two or more parties. Negotiation tries to maximize the benefits for all parties concerned. instead of using human-based negotiation. the e-commerce environment provides such an environment as adopting automated negotiation. Thus. choosing agent technology is appropriate for an automatic electronic negotiation platform. since autonomous software agents strive for the best deal on behalf of the human participants. Negotiation agents need a clear-cut definition of negotiation models or strategies. In reality, most bargaining systems embody nearly one negotiation model. In this article. we present a mobile agent negotiation system with reusable negotiation strategies that allows agents to dynamically embody a user's favorite negotiation strategy which can be preinstalled as a component in the system. We develop a prototype system, which is fully implemented in compliance with FIPA specifications, and then. describe the benefits of using the system.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.97-117
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• 2020

Product evaluation criteria is an indicator describing attributes or values of products, which enable users or manufacturers measure and understand the products. When companies analyze their products or compare them with competitors, appropriate criteria must be selected for objective evaluation. The criteria should show the features of products that consumers considered when they purchased, used and evaluated the products. However, current evaluation criteria do not reflect different consumers' opinion from product to product. Previous studies tried to used online reviews from e-commerce sites that reflect consumer opinions to extract the features and topics of products and use them as evaluation criteria. However, there is still a limit that they produce irrelevant criteria to products due to extracted or improper words are not refined. To overcome this limitation, this research suggests LDA-k-NN model which extracts possible criteria words from online reviews by using LDA and refines them with k-nearest neighbor. Proposed approach starts with preparation phase, which is constructed with 6 steps. At first, it collects review data from e-commerce websites. Most e-commerce websites classify their selling items by high-level, middle-level, and low-level categories. Review data for preparation phase are gathered from each middle-level category and collapsed later, which is to present single high-level category. Next, nouns, adjectives, adverbs, and verbs are extracted from reviews by getting part of speech information using morpheme analysis module. After preprocessing, words per each topic from review are shown with LDA and only nouns in topic words are chosen as potential words for criteria. Then, words are tagged based on possibility of criteria for each middle-level category. Next, every tagged word is vectorized by pre-trained word embedding model. Finally, k-nearest neighbor case-based approach is used to classify each word with tags. After setting up preparation phase, criteria extraction phase is conducted with low-level categories. This phase starts with crawling reviews in the corresponding low-level category. Same preprocessing as preparation phase is conducted using morpheme analysis module and LDA. Possible criteria words are extracted by getting nouns from the data and vectorized by pre-trained word embedding model. Finally, evaluation criteria are extracted by refining possible criteria words using k-nearest neighbor approach and reference proportion of each word in the words set. To evaluate the performance of the proposed model, an experiment was conducted with review on '11st', one of the biggest e-commerce companies in Korea. Review data were from 'Electronics/Digital' section, one of high-level categories in 11st. For performance evaluation of suggested model, three other models were used for comparing with the suggested model; actual criteria of 11st, a model that extracts nouns by morpheme analysis module and refines them according to word frequency, and a model that extracts nouns from LDA topics and refines them by word frequency. The performance evaluation was set to predict evaluation criteria of 10 low-level categories with the suggested model and 3 models above. Criteria words extracted from each model were combined into a single words set and it was used for survey questionnaires. In the survey, respondents chose every item they consider as appropriate criteria for each category. Each model got its score when chosen words were extracted from that model. The suggested model had higher scores than other models in 8 out of 10 low-level categories. By conducting paired t-tests on scores of each model, we confirmed that the suggested model shows better performance in 26 tests out of 30. In addition, the suggested model was the best model in terms of accuracy. This research proposes evaluation criteria extracting method that combines topic extraction using LDA and refinement with k-nearest neighbor approach. This method overcomes the limits of previous dictionary-based models and frequency-based refinement models. This study can contribute to improve review analysis for deriving business insights in e-commerce market.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• The Journal of Information Technology
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• v.6 no.4
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• pp.147-162
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• 2003

The technique of information-communication rapidly developed has made it possible for us to do business through Internet. Electronic commerce was increased rapidly by the explosive development of the inter and communication revolution. E-Commerce has created a fundamentally new way of conduction and will change drastically accepted ways of doing business. Normally international trade has been formulated in a way that exporters and importers meet face-to-face and contract and pay by letter of credits. For the global electronic commerce to vitalized, the outstanding matters should encourage the creation of infrastructure of information security and new models in the field of electronic payment systems, electronic commerce agreement for remedy, adapting electronic date interchange in transport documents and negotiability of electronic bills of lading. The payment systems such as electronic fund transfers, tradecard system and electronic letters of credits issued by SWIFT system permit the parties concerned(sellers, buyers ad service providers) to settle payment electronically. Still they are many limitations for complete international electronic transactions. The following measures have to be taken to vitalize electronic trade transactions. It is needed to acquire information security such as authenticity, integrity, non-repudiation and confidentiality. All kinds of documents need to be replaced by electronic date exchange and the legal structure of international convention, national law for electronic payment systems have to be completed. Also a detailed guide of the banking operation and developing rules for electronic letters of credits need to be provided to adopt eUCP rules for the electronic presentation of documents.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.2
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• pp.101-113
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• 2011

As information and communication technology advances driving the rapid evolution of e-commerce and e-business becomes on effective marketing means for both business and institutions, the industry deals with these changes and has a profound, positive impact on production and operations management. But the construction industry falls behind compared with other industries in productivity, informationalization, automatization and globalization. Especially even though the informationalization is inevitable in any developing society, a number of construction industries can hardly cope with it properly. As the construction industry gets informationalized and advanced, the introduction of PMIS (Project Management Information System) is making progress actively for the effective construction project control and productivity improvement in construction. With the government officials' political promotion and the rising desire for construction informationalization of enterprises, many are attempting to develop integrated systems or to replace a previous system with a more advanced alternative. Construction Industry, however, has the peculiar properties that the period for the completion of construction is limited and on top of that, most of its data processing systems are seen as being an early stage in the design of management information systems. As a result, there are extremely few applications in the construction industry. In this paper, we propose the PMIS system based on new model 4CM that improves on previous models.

• Asia Marketing Journal
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• v.18 no.4
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• pp.125-138
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• 2017

The goal of the study is to understand how consumers' constraint as opposed to utility structure gives rise to final decision when consumers purchase more than one variant of product at a time, i.e., horizontal variety seeking or multiple-discreteness. Purchase and consumption decision not only produces utility but also involves some sort of cognitive pressure. Past consumption or last purchase is likely to be linked to this burden we face such as concern for obesity, risk of harm, and guilt for mischief. In this research, the existence and the role of dynamic constraint are investigated through a microeconomic utility model with multiple dynamic constraint. The model is applied to the salty snacks data collected from field study where burden for spiciness serves as a constraint. The results are compared to the conventional multiple discreteness choice models of static constraints, and policy implications on price discounts is explored. The major findings are that first, one would underestimate the level of consumer preference for product offerings when ignoring the carry-over of the concern from the past consumption, and second, the impact of price promotion on demand would be properly evaluated when the model allows for the role of constraint as both multiple and dynamic. The current study is different from the existing studies in two ways. First, it captures the effect of 'mental constraint' on demand in formal economic model. Second, unlike the state dependence well documented in the literature, the study proposes the notion of state dependence in different way, via constraint rather than utility.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Asia pacific journal of information systems
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• v.12 no.2
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• pp.25-44
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• 2002

The internet-based electronic commerce is considered as a new strategic alternative to boost competitiveness for small and medium-sized enterprises(SMEs). However, very little research about them has been reported. Meanwhile, the cyber shopping shops in Korea are growing rapidly in their numbers, scales, and diversity of business models. The primary purpose of this study is to investigate the factors influencing outsourcing decisions in the implementation of small and medium-sized cyber shopping shops. Based on the previous studies on IS outsourcing, marketing channel, and their related theories, three areas of determinants(IT & organizational, product, marketing channel characteristics) were identified. Responses of 125 cyber shopping shops from e-mail survey indicate that IT capability, technical specificity of shop implementation, degree of product customization, average amount of order, marketing capability are negatively associated with outsourcing of cyber shopping shop implementation. The results also indicate that the outsourcing decision on systems implementation and marketing channel selection(direct/indirect selling) is performed separately.

• Asia pacific journal of information systems
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• v.28 no.3
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• pp.183-203
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• 2018

In the era of Web 2.0, social media advertising can simultaneously stimulate consumers' brand purchase intention and brand information sharing intention. Product sales and brand information diffusion are equally important for a company that conducts advertising. This study investigates how features of brand content influence social media advertising effectiveness by integrating the stimulus-organism-response model and classic advertising effectiveness models. An analysis of 267 survey questionnaires shows that brand content-related cues, including perceived uniqueness, perceived vividness, and perceived interactivity have significant effects on consumers' affective and cognitive involvement, which then affect their attitude toward brand content. As a result, the consumers' attitude toward the brand and their brand purchase intention, as well as their brand content sharing intention, are positively affected by attitude toward brand content. This study contributes to a better understanding of how social advertising works, which suggests that managers should effectively use social media to conduct advertising.