• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.39-50
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• 2018

This study aimed at investigating the features of m-commerce (mobility, perceived enjoyment, perceived personalization, and habit) that influence consumers' behavioral intents to adopt mobile shopping. To this end, it theorized on and examined the effects of the features of m-commerce that influence consumers' behavioral intents to adopt mobile shopping. The results showed that the most critical drives of m-commerce in mobile shopping are mobility, perceived personalization, and habit. And also we found that habit weakens (moderates) the effects of mobility on the consumers' behavioral intents to adopt mobile shopping. Habit has direct effects on the consumers' behavioral intents to adopt mobile shopping, and these effects are moderated by individual habit.

• Management & Information Systems Review
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• v.33 no.2
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• pp.63-79
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• 2014

This study is based on the previous studies on m-commerce features, found factors that affects reliability and user's intention. After that, it examined how these factors influence the relationship between reliability and user's intention. In addition, this study showed that some factors have different influence on Korean and Chinese users in terms of reliability and user's intention. The main results of this study are as follows: (1) Personal innovation attributed to reliability in both Korea and China. Personal innovation also attributed to user's intention in Korea. (2) Localization, reach ability, security, and convenience had different influence on use and reliability in the two countries. (3) And the influences between reliability and user's intention are all positive both in Korea and China. Based on the result of this empirical study, this study reveal some implications for the firms that running with mobile business in both Korea and China.

• International Journal of Advanced Culture Technology
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• v.8 no.3
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• pp.148-158
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• 2020

With the limited product information available in the m-shopping context, product-naming strategies affect consumer choices by expressing the key product features or the brand's images. Given the increasing dominance of the mobile commerce in consumption across various product categories, few studies have examined the role of product naming in consumer choices in the m-shopping. In filling the research gap, this study empirically analyzes the influence of the perceived diagnosticity of product names in m-shopping on consumer attitude towards the product. Moreover, the study tests the moderating influences of the individual characteristics of consumers (i.e., age, gender, and m-shopping experience) in the dynamics of the perceived diagnosticity impacting the product evaluations. The results of the study using an online survey reveal that the perceived diagnosticity of the product names significantly increases consumer attitude towards the product. Additionally, the moderating effects of gender, age, and m-shopping experience are all found significant: (1) The positive influence of the perceived diagnosticity of the product names is greater for males than for females. (2) The older the respondent, the more statistically significant the positive influence on diagnosticity. (3) The more respondents having m-shopping experience, the more positive the impact of the diagnosticity. Implications and limitations of the study are discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.599-604
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• 2005

This paper presents fuzzy rules to predict diagnosis of Wisconsin breast cancer with minimized number of feature in put using the neural network with weighted fuzzy membership functions (NEWFM) and the non-overlap area distribution measurement method. NEWFM is capable of self-adapting weighted membership functions from the given the Wisconsin breast cancer clinical training data. n set of small, medium, and large weighted triangular membership functions in a hyperbox are used for representing n set of featured input. The membership functions are randomly distributed and weighted initially, and then their positions and weights are adjusted during learning. After learning, prediction rules are extracted directly from n set of enhanced bounded sums of n set of small, medium, and large weighted fuzzy membership functions. Then, the non-overlap area distribution measurement method is applied to select important features by deleting less important features. Two sets of prediction rules extracted from NEWFM using the selected 4 input features out of 9 features outperform to the current published results in number of set of rules, number of input features, and accuracy with 99.71%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.538-561
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• 2020

Recommender Systems (RecSys) have a major role in e-commerce for recommending products, which they may like for every user and thus improve their business aspects. Although many types of RecSyss are there in the research field, the state of the art RecSys has focused on finding the user similarity based on sequence (e.g. purchase history, movie-watching history) analyzing and prediction techniques like Recurrent Neural Network in Deep learning. That is RecSys has considered as a sequence prediction problem. However, evaluation of similarities among the customers is challenging while considering temporal aspects, context and multi-component ratings of the item-records in the customer sequences. For addressing this issue, we are proposing a Deep Learning based model which learns customer similarity directly from the sequence to sequence similarity as well as item to item similarity by considering all features of the item, contexts, and rating components using Dynamic Temporal Warping(DTW) distance measure for dynamic temporal matching and 2D-GRU (Two Dimensional-Gated Recurrent Unit) architecture. This will overcome the limitation of non-linearity in the time dimension while measuring the similarity, and the find patterns more accurately and speedily from temporal and spatial contexts. Experiment on the real world movie data set LDOS-CoMoDa demonstrates the efficacy and promising utility of the proposed personalized RecSys architecture.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.611-612
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• 2006

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.

• The KIPS Transactions:PartC
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• v.10C no.3
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• pp.335-344
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• 2003

In accordance with the changes in the wireless communication environment, there has been a great need to satisfy the demand for diverse modes of information exchange. Various types of short-distance wireless communication technology have been developed and studied to meet this demand. Among them, Bluetooth and WLAN which has recently been acclaimed as the standard for short-distance wireless communication, has been the focus of many such studies. However, Bluetooth and WLAN has weaknesses in its security features when its in real services are applied to m-commerce. The purpose of this study is to propose techniques that affinity considers to item that is non-security enemy who is although there is no public secure division direct connection in peculiar environment of radio environment as well as limitation security enemy of short distance radio communication. Propose secure monitoring techniques for straggling device to user center also applying proposed way to Bluetooth and WLAN that are short distance communication representative technology based on item that is security enemy and item that is rain suity enemy.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.143-146
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• 2005

Many data mining techniques have been proved useful in revealing important patterns from large data sets. Especially, data mining techniques play an important role in a customer data analysis in a financial industry and an electronic commerce. Also, there are many data mining related research papers in a semiconductor industry and an automotive industry. In addition, data mining techniques are applied to the bioinformatics area. To satisfy customers' various requirements, each industry should develop new processes with more accurate production criteria. Also, they spend more money to guarantee their products' quality. In this manner, we apply data mining techniques to the production-related data such as a test data, a field claim data, and POP (point of production) data in the automotive parts industry. Data collection and transformation techniques should be applied to enhance the analysis results. Also, we classify various types of manufacturing processes and proposed an analysis scheme according to the type of manufacturing process. As a result, we could find inter- or intra-process relationships and critical features to monitor the current status of the each process. Finally, it helps an industry to raise their profit and reduce their failure cost.