• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.1-8
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• 2019

Network externalities are essentially dynamic in that the value consumers feel about a product is affected by the size of the existing customer base that uses that product. However, existing studies on network externalities analyzed the effects of network externalities in a static way, not dynamic. In this study, unlike previous studies, the impact of network externalities on price competition in a vertically differentiated market is dynamically analyzed. To this end, a two-period duopoly game model was used to reflect the dynamic aspects of network externalities. Based on the game model, the Nash equilibria for price, sales volume, and revenue were derived and numerically analyzed. The results can be summarized as follows. First, if high-end product has strong market power, the high-end product vendor takes almost all benefits of the network externality. Second, when high-end product has strong market power, the low-end product will take over most of the initial sales volume increase. Third, when market power of high-end product is not strong, it can be seen that the effects of network externalities on the high and low-end products are generally proportional to the difference in quality. Lastly, if there exists a strong network externality, it is shown that the presence of low-end product can be more profitable for high-end product vendor. In other words, high-end product vendor has incentive to disclose some technologies for the market entrance of low-end product, even if it has exclusive rights to the technologies. In that case, however, it is shown that the difference in quality should be maintained significantly.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.57-72
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• 2009

As development of information technologies, customer retention has been an important issue in the competing environment. A lot of researches focus on prediction of the churning customers and seeking their characteristics. However, relationships among customers or products have not been considered in existing researches. In this study, product networks are proposed and analyzed to investigate the differences of network characteristics of products purchased by potential churning customers and those of loyal customers. The product networks are constructed from real product purchase data collected from a Korean department store. We investigated the characteristic differences, such as the degree centrality, degree centralization, and density, of two product networks constructed by potential churning customers and the loyal customers. The results indicate that degree centrality, density and degree centralization of the product network of the loyal customers are higher than those of the potential churning customers. And the promotional products of the department store are resulted to be effective in attracting the loyal customers.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.89-101
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• 2019

Many researchers have been focused on designing beauty product recommendation system for a long time because of increased need of customers for personalized and customized recommendation in beauty product domain. In addition, as the application of the deep neural network technique becomes active recently, various collaborative filtering techniques based on the deep neural network have been introduced. In this context, this study proposes a deep neural network model suitable for beauty product recommendation by applying Neural Collaborative Filtering and Generalized Matrix Factorization (NCF + GMF) to beauty product recommendation. This study also provides an implementation of web API system to commercialize the proposed recommendation model. The overall performance of the NCF + GMF model was the best when the beauty product recommendation problem was defined as the estimation rating score problem and the binary classification problem. The NCF + GMF model showed also high performance in the top N recommendation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.159-166
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• 2018

The proliferation of information technologies made it possible to produce information products of different versions at much lower cost comparing to traditional physical products. Thus it is common for information product manufacturers to consider vertically differentiated product line for more profit through improved market coverage. Another salient characteristic of most information product is network externality. Existing researches dealing with vertical differentiation and network externality usually assumed oligopolistic market where vertically differentiated products are provided by competing companies, respectively. Moreover, they analyzed the essentially dynamic characteristic of network externality statically. In this study, different from the previous researches, the vertical differentiation strategy of a monopolistic company under network externality is dynamically analyzed. We used a two-period model to accommodate the dynamic feature of network externality. Based on the two-period model, the profit maximizing solutions are analyzed. The results showed that a monopolistic company has no incentive to differentiate products vertically when the network externality is absent. On the contrary, when the network externality exists, the monopolistic company can derive more profit by vertically differentiating the product line. It is also shown that, for more profit, the monopolistic company should keep the quality difference between the high quality product and the low quality product as greater as possible.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.750-755
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• 2014

Product shortage which causes backordering and/or lost sales cost is very popular in chemical industries, especially in commodity polymer business. This study deals with backordering cost in the supply chain optimization model under the framework of process-inventory network. Classical economic order quantity model with backordering cost suggested optimal time delay and lot size of the final product delivery. Backordering can be compensated by advancing production/transportation of it or purchasing substitute product from third party as well as product delivery delay in supply chain network. Optimal solutions considering all means to recover shortage are more complicated than the classical one. We found three different solutions depending on parametric range and variable bounds. Optimal capacity of production/transportation processes associated with the product in backordering can be different from that when the product is not in backordering. The product shipping cycle time computed in this study was smaller than that optimized by the classical EOQ model.

• Journal of Korean Society for Quality Management
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• v.41 no.4
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• pp.637-648
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• 2013

Purpose: The objective of this study is to provide a methodology that can facilitate efficient development of a product portfolio by utilizing Korean Standards(KS) networks. Methods: A case study on a steel manufacturing company is provided. Social network analysis h as been conducted on KS network and KS certification information of the company. Core test standards of a company have been identified. The core standards, then, used to construct a product-standard network of a corresponding industry. Results: As a result of analyzing product-standard networks, a product portfolio of a company has been developed. It has been shown that the candidate product portfolio is a cost-effective alternative in terms of standard maintenance cost. Conclusion: By using social network analysis, standards information can be used to support new product development process.

• Knowledge Management Research
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• v.14 no.3
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• pp.87-100
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• 2013

As the external knowledge networks of firms have become more and more important to their product and service innovations, many global leading companies such as P & G, IBM, and Samsung Electronics have formulated and implemented their open innovation strategy. This study attempts to empirically analyze the effects of CEOs' supports for external knowledge networks, external knowledge network-oriented cultures and inter-organizational knowledge management systems as the major antecedents to external knowledge networks by using the data based on organizational members' perceptions about them. Based on 847 samples collected from employees in three companies in the medical, the construction and the IT service industries, this study performed a structural equation modeling (SEM) analysis about the effects of the antecedents to the external knowledge networks on product and service innovations through Partial Least Squares (PLS). The empirical findings of this study show that CEOs' supports for external knowledge network positively influence product and service innovations, partially mediated by external knowledge network-oriented cultures and inter-organizational knowledge management systems. And they also show that external knowledge network-oriented cultures and inter-organizational knowledge management systems have a positive effect on product and service innovations, respectively, partially mediated by external knowledge networks. With these new findings, academic and practical implications are discussed.

• Asia pacific journal of information systems
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• v.26 no.4
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• pp.477-488
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• 2016

New technology development capability plays a key role in making small- and medium-sized enterprises (SMEs) increase their innovation performance, such as in product or process innovation. To examine the influencing factors of SMEs' new technology development capability, this study empirically analyzes the mediating effects of SMEs' process design capability and product interior design capability on the positive association between their external information network diversity and new technology development capability. This study performs the ordinary least squares regression on a sample of 2,000 South Korean SMEs. Results reveal that SMEs' process design capability fully mediates, and product interior design capability partially mediates the positive association between the external information network diversity and new technology development capability.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.3
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• pp.556-571
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• 2020

This study predicts consumer preference for social clothing at work, excluding uniforms using the self-product congruence theory that also establishes a model to predict the preference for recommended products that match the consumer's own image. A total of 490 Korean male office workers participated in this study. Participants' self-image and the product images of 20 apparel items were measured using nine adjective semantic scales (namely elegant, stable, sincere, refined, intense, luxury, bold, conspicuous, and polite). A model was then constructed to predict the consumer preferences using a neural network with Python and TensorFlow. The resulting Predict Preference Model using Product Image (PPMPI) was trained using product image and the preference of each product. Current research confirms that product preference can be predicted by the self-image instead of by entering the product image. The prediction accuracy rate of the PPMPI was over 80%. We used 490 items of test data consisting of self-images to predict the consumer preferences for using the PPMPI. The test of the PPMPI showed that the prediction rate differed depending on product attributes. The prediction rate of work apparel with normative images was over 70% and higher than for other forms of apparel.