• Knowledge Management Research
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• v.17 no.3
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• pp.227-248
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• 2016

Customer satisfaction dominates research on customer-firm performance relationships; however, with a few exceptions, the authors of most prior studies did not examine the possibility that an organizations' customer relationship management can increase its knowledge management. Building on previous literature of information processing theory and transaction cost perspective, this paper investigates the effect of various characteristics of customer relationship an organization cultivates on its own innovativeness. Specifically, we identify closeness, communication, sympathy as three critical components of managing customer relationship. Data from a multi-informant survey conducted to 442 organizations in Korean bank industry show that an organization's relationship with its customers has significant effects on its innovativeness. This study highlights the importance of customer relationship in terms of enhancing innovations, and helps to explain interactive effects among customer relationship, organizational learning, and innovativeness.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.109-128
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• 2010

The purpose of our research is to figure out the 'non-financial value' of consumers applying networks amongst consumer groups, the data-based marketing strategy to the analysis and delve into the ways for enhancing effectives in marketing activities by adapting the value to the marketing. To verify the authenticity of the points, we did the empirical test on the consumer group using 'the Essence Cosmetics Products' of high involvement that is deeply affected by consumer perceptions and the word-of-mouth activities. 1) The empirical analysis reveals the following features. First, the segmented market for 'Essence Consumer' is composed of several independent networks, each network shows to have the consumers that is high degree centrality and closeness centrality. Second, the result proves the authenticity of the non-financial value for boosting corporate profits by the high degree centrality and closeness centrality consumer's word-of-mouth activities. Lastly, we verify that there lies a difference in the network structure of 'Essence Cosmetics Market'per each product origin(domestic, foreign) and demographic characteristics. It does, therefore, indicate the need to consider the features applying mutually complementary for the network targeting.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.1
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• pp.187-198
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• 2015

The purpose of this research is to explore the characteristics of Korean Global Hidden Champions focused on the success factors of foreign hidden champions. For this, we investigated the application process of these success factors of foreign hidden champions to Korean Global Hidden Champions and analyzed characteristics of Korean Global Hidden Champions with the cases of 11 companies. This research shows that there are the success factors of foreign hidden champions such as leadership and goals, self production, high-performance employees, market focus, continuous innovation, closeness to customer, globalization. This research also shows that there are some differences in the individual application process of success factors to each company. The Korean SME's trying to achieve the position of global hidden champions should know the success factors of foreign hidden champions clearly and investigate the application process of these success factors to Korean Global Hidden Champions carefully so that they may apply these lessons to their management processes and activities.

• Journal of the Korean Society of Food Culture
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• v.13 no.4
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• pp.307-316
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• 1998

Vending machines play an important role of giving convenience and simplicity in modem life style. So they became an indispensible element in life of modern people. This study was peformed to investigate customer's actual status in use as well as the degree of satisfaction and requirement of food and beverage vending machines. The results of this study can be summarized as follows. 1. About the advantage of using the vending machines, respondents answered 'convenience' for 50.2% and 'closeness' for 33.6% of all the answers. About the dissatisfaction for vending machine, three factors of 'inappropriate taste, temperature. quantity' and 'unsanitary pakage material and food' were the main causes. 2. About the credit of food quality,48.6% of respondents answered' some what doubtful'.58.1% of respondents pointed out that they couldn't confide in freshness and shelf-life' 3. 48.2% of respondents agreed that vending machines would be needed more in the future. Respondents wanted lots of food to be served from vending machines. The foods which respondents wanted to be served from vending machines were noodle(30.8%), rice(19%), pastry(18.2%), bread(17.45) gruel(7.3%) and snack(7.3%).

• Korean Institute of Interior Design Journal
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• v.24 no.4
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• pp.3-13
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• 2015

For last decades, the interests and efforts to enhance healthcare facility users' experience is focused on improving facility environments for healing (Delvin, 2003) and servicescapes in order to meet the users' needs (Becker, 2008; Seunghee, 2011). In the emerging experience economy, customer want experiences and they're willing to pay for the experiences and memories not goods. (Pine, J. & Gillmore, J., 1999). It is important to identify what supports customer experiences and how they perceive the experiences in healthcare environments and it will provide important information for healthcare planners, managers, architects, and interior designers. This study examines the service user experience design elements from a User Experiences design perspective. It focuses on healthcare facilities as user experience elements and build up a conceptual framework that outlines service user experience design elements in healthcare facilities. Literature review and case studies were conducted to build the service user experience design elements according to affordance theory. Findings from this study shows that service user experience design elements were introduced and newly developed which can be categorized into three factors; 1) Functional experiences in the physical environments (safety, accessibility, self-directiveness), 2) emotional expression and cognitive experiences (identifiability/clarity, natural features/pleasant environment, aesthetic elements/playful space, media richness), 3) social relational experiences(closeness, privacy, communication with staff, integrated system). These service user experience design elements will help healthcare facility designers to understand what customer experiences, how they increase the satisfaction, and how they improve facilities for modeling the industry's best practices.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.99-114
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• 2011

Collaborative Filtering (CF) suffers from two major problems:sparsity and cold-start recommendation. This paper focuses on the cold-start problem for new customers with no purchase records and the sparsity problem for the customers with very few purchase records. For the purpose, we propose a method for the new customer recommendation by using a combined measure based on three well-used centrality measures to identify the customers who are most likely to become neighbors of the new customer. To alleviate the sparsity problem, we also propose a hybrid approach that applies our method to customers with very few purchase records and CF to the other customers with sufficient purchases. To evaluate the effectiveness of our method, we have conducted several experiments using a data set from a department store in Korea. The experiment results show that the combination of two measures makes better recommendations than not only a single measure but also the best-seller-based method and that the performance is improved when applying the hybrid approach.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.449-455
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• 2002

This paper considers that advanced planning and scheduling (APS) in manufacturing and the efficient purchasing where each customer order has its due date and multi-suppliers exit We present a Make-To­Order Supply Chan (MTOSC) model of efficient purchasing process from multi-suppliers and APS with outsourcing in a supply chain, which requires the absolute due date and minimized total cost. Our research has included two states. One is for efficient purchasing from suppliers: (a) selection of suppliers for required parts; (b) optimum part lead­time of selected suppliers. Supplier selection process has received considerable attention in the business­management literature. Determining suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. These influence factors can be divided into quantitative and qualitative factors. In the first level, linguistic values are used to assess the ratings for the qualitative factors such as profitability, relationship closeness and quality. In the second level a MTOSC model determines the solutions (supplier selection and order quantity) by considering quantitative factors such as part unit price, supplier's lead-time, and storage cost, etc. The other is for APS: (a) selection of the best machine for each operation; (b) deciding sequence of operations; (c) picking out the operations to be outsourcing; and (d) minimizing makespan under the due date of each customer's order. To solve the model, a genetic algorithm (GA)-based heuristic approach is developed. From the numerical experiments, GA­based approach could efficiently solve the proposed model, and show the best process plan and schedule for all customers' orders.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Korean Society for Quality Management
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• v.44 no.2
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• pp.389-408
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• 2016

Purpose: This study was designed to analyze the behavioral change of knowledge structures and the trends of research topics in the quality management field. Methods: The network structure and knowledge structure of the words were visualized in map form using co-word analysis, cluster analysis and strategic diagram. Results: Summarizing the research results obtained in this study are as follows. First, the word network derived from co-occurrence matrix had 106 nodes and 5,314 links and its density was analyzed to 0.95. Average betweenness centrality of word network was 2.37. In addition, average closeness centrality and average eigenvector centrality of word network were 0.01. Second, by applying optimal criteria of cluster decision and K-means algorithm to word co-occurrence matrix, 106 words were grouped into seven clusters such as standard & efficiency, product design, reliability, control chart, quality model, 6 sigma, and service quality. Conclusion: According to the results of strategic diagram analysis over time, the traditional research topics of quality management field related to reliability, 6 sigma, control chart topics in the third quadrant were revealed to be declined for their study importance. Research topics related to product design and customer satisfaction were found to be an important research topic over analysis periods. Research topic related to management innovation was emerging state and the scope of research topics related to process model was extended to research topics with system performance. Research topic related to service quality located in the first quadrant was analyzed as the key research topic.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.159-182
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• 2009

We construct product networks from the retail transaction dataset of an off-line department store. In the product networks, nodes are products, and an edge connecting two products represents the existence of co-purchases by a customer. We measure the quantities frequently used for characterizing network structures, such as the degree centrality, the closeness centrality, the betweenness centrality and the centralization. Using the quantities, gender, age, seasonal, and regional differences of the product networks were analyzed and network characteristics of each product category containing each product node were derived. Lastly, we analyze the correlations among the three centrality quantities and draw a marketing strategy for the cross-selling.