• IE interfaces
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• v.17 no.1
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• pp.56-70
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• 2004

Expanding product variety and high customer service provision place an enormous burden on demand forecasting and the matching of supply with demand in a supply chain. Postponement of product differentiation has been found to be powerful means to improve supply chain performance in the presence of increasing product variety. Delaying the point of product differentiation implies that the process would not commit the work-in-process into a particular finished product until a later point. This paper reviews the recent analytical models that quantify the value of delayed product differentiation. We conclude the literature review by summarizing and synthesizing the economic evaluation of the postponement and outline directions for future research.

• International Commerce and Information Review
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• v.7 no.1
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• pp.75-94
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• 2005

With most major full service banks having launched transactional Internet banking, attention is shifting to the realities of managing the Internet channel as a profitable component of an overall delivery strategy. In addition to examining Case Study and Internet Bank Model. Services of Internet Banking available through the Internet are as follows. 1. credit card loans, personal loans. 2. high-yield financial products. 3. insurance products. 4. securities products. 5. Case study of Foreign Internet Banking(ING, BNP, HSNC, City Bank). The study reviewed fields, including financial services, customer service, Website formation and design, convenience of use and system safety, Internet Banking Model, and many related areas. Internet Banking earned high marks in most fields. This study review focuses on the following: Understanding and meeting consumer expectations for us ability, site performance and functionality. Integrating the Internet channel into overall marketing, product delivery and customer service strategies. Strategies to increase customer satisfaction with Internet Banking and to attract new Internet bankers. therefore this study review activity model concretion of Internet Banking Model and Case Study.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Korean Management Science Review
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• v.19 no.1
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• pp.189-196
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• 2002

Data clustering is often one of the first steps in data mining analysis. It Identifies groups of related objects that can be used as a starling point for exploring further relationships. This technique supports the development of population segmentation models, such as demographic-based customer segmentation. This paper Purpose to present the development of two phase hierarchical clustering algorithm for group formation. Applications of the algorithm for product-customer group formation in customer relationahip management are also discussed. As a result of computer simulations, suggested algorithm outperforms single link method and k-means clustering.

• Information Systems Review
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• v.21 no.1
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• pp.61-90
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• 2019

From the digital perspective, customer journey and customer experience management are emerging as important issues for companies. While digital customer experience has become more important due to the recent surge in online sales of the home appliance products, customers' experience in online is not differentiated as offline-focused traditional methods are maintained. This study aims to analyze the characteristics and mutual influences of customer experiences at each stage of online purchase journey, and to explore the effects on the product repurchase intention, focusing on online purchasers of home appliance which are high-involvement products. As a result, both cognitive and affective experiences of the research phase directly affect satisfaction, whereas affective experience at the purchasing stage indicated indirect effects through cognitive experience. The experience of the research phase positively affects the next phase, the purchasing experience, and the experience of the purchasing phase leads to the intention to repurchase the product. However, it is also found that, depending on the choice of online channels, the experience of research phase may affect the product repurchase intention than the purchase experience.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.129-142
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• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.19-33
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• 2009

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through the reviews. Product Reviews are results expressing customer's sentiments and thus are divided into positive reviews and negative ones. However, as the number of reviews in on-line shopping increases, it is inefficient or sometimes impossible for users to read all the relevant review documents. In this paper, we present a sentiment analysis algorithm for automatically classifying subjective opinions of customer's reviews using opinion mining technology. The proposed algorithm is to focus on product reviews of on-line shopping, and provides summarized results from large product review data by determining whether they are positive or negative. Additionally, this paper introduces an automatic review analysis system implemented based on the proposed algorithm, and also present the experiment results for verifying the efficiency of the algorithm.

• Management & Information Systems Review
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• v.12
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• pp.17-35
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• 2003

In the customer-centered era, financial institutions have understood the importance of Customer Relationship Management(CRM), and heavily invested into building the required technology infrastructure more than ever. In a competitive environment that are changing fast, knowledge management is necessary. To know customers' needs and desire, we have to approach their environment and mind, and the method by estimating in terms of supposing or imitating. Applying customers' knowledge is effective and will come up with a stepping-stone to get rid of threatening factors by having competitiveness in a competitive environment and extending and changing the corporation. This purpose, the study has identified knowledge-oriented infra that corporations know and customer relations by conducting a poll of local corporations and have presented motives that can effectively carry out knowledge-based customer relations. To gain competitive advantage, these Institutions need to understand their customers' potential value to find out more and to recognize the significant changes of customer. Then the CRM implementation will help Financial Institutions move to more of a sales culture away from product and closer to the customer.

• Korean Management Science Review
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• v.19 no.2
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• pp.205-220
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• 2002

As the business structure of many industries changes under IT progress and internet economy, the customer information has emerged a key factor in setting up the management policy. The customer has come to replace the product as a central figure in business competition. The domestic life insurance market has also experienced the rapid structural changes in IT time. The competition in the insurance industry to maintain the existing membership and to attract the new members gets stronger under such a new business circumstance. Accordingly, it is necessary for an individual insurance company to develop a systematic marketing plan, based on the customer information, to be competitive in the market. Unlike other studies in which customer characteristics are neglected, this study attempts to utilize the customer information by applying the data mining technique, and then suggests an efficient marketing strategy that could prevail in the competitive business environment.

• Smart Media Journal
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• v.2 no.2
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• pp.20-27
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• 2013

The concept of social media is top of the agenda for many business executives and decision makers, as well as consultants try to identify ways where companies can make profitable use of applications such as Netflix, Flixster. The social media is playing an increasingly important role as the information sources for customers making product choices etc. With the flourish of Web 2.0 technology, customer reviews are becoming more and more useful and important information resources for people to save their time and energy on purchasing products that they want. This paper proposes the Bayesian Probabilistic Classification algorithm to mine the social media review, and evaluates it by different splits and cross validation mechanism from the real data set. The explored study experimental results show the robustness and effectiveness of proposed approach for mining the social media review.