• Journal of Information Technology Services
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• v.7 no.3
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• pp.1-30
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• 2008

This paper proposes an integrated model of customer loyalty in the context of Internet shopping based on a review of two competing perspectives - transactional and relational views. The research model suggests that the usability and trust associated with an Internet shopping site are key determinants of customer loyalty. In this paper, factors such as promotion, ease of use/navigation, and purchase facilitation are posited as major determinants of usability. Trust, on the other hand, is assumed to be influenced by the quality of communication, social shopping service, and safety level associated with an Internet shopping site. This paper also asserts that the lack of consideration for individual differences is one of the key reasons for the inconsistent and mixed research findings in user acceptance literature. In this regard, the elaboration likelihood model (ELM) is considered to be appropriate referent theory as it may theoretically explain why a particular information technology (IT) related message has varying influences on different adopters. The research model comprising 11 hypotheses was derived from and validated through a survey involving 271 university students. The partial least square(PLS) method was used to test the suitability of the research model and its hypotheses. Overall, the results suggest that the usability and trust associated with an Internet shopping site play an important role in acquiring loyal customers. In particular, the user's Internet shopping involvement is found to moderate the relationship between trust and customer loyalty.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.231-244
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• 2012

The purpose of this study was to substantially analyze the relation between the success factors of quality management and performance. For this purpose, the success factors of quality management - CEO's leadership, customer-centeredness, process management, training, full participation of employees, and relationship with vendors - were identified as independent variables and performance was identified as dependent variable to substantially analyze the automotive parts industry. In result, the findings can be summarized into the following: First, for the relation between the success factors of quality management and financial performance, CEO's leadership, customer-centeredness, training, and full participation of employees were statistically significant, but process management and relationship with vendors were not statistically significant. Second, for the relation between the success factors of quality management and non-financial performance, CEO's leadership, customer-centeredness, full participation of employees, and relationship with vendors were statistically significant, but process management and training were not statistically significant. Third, it was also found that, among the success factors of quality management, CEO's leadership, customer-centeredness, full participation of employee should be considered more than any other variables to achieve performance. Based on the above findings, it was concluded that 'CEO's leadership' and 'customer-centeredness' had an influence on both financial and non-financial performances and were relatively more influential than other individual factors.

• Journal of Information Technology Applications and Management
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• v.22 no.4
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• pp.105-126
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• 2015

In this study, we investigated the impact of the information richness and reachness on the mobile purchasing and the moderating effect of individual characteristics. We examined the information richness and reachness through SNS information and Push notification of smartphone, respectively. As the moderating variables, we adopted customer's value orientation and innovativeness. In the main-effect model with no moderating variable, both of information richness and reachness showed significant effects on the perceived value of products and purchasing channel. Especially, the impact of information richness was more significant on product awareness, while the impact of information reachness was more significant on channel awareness. In the interaction-effect model with moderating variables, customer's value orientation showed significant moderating effect on the impact of perceived product value. However, customer's innovativeness did not show the significant moderating effect on the impact of perceived channel value. It implies that the impact of information reachness applies to the majority of customers, regardless of her [his] innovativeness. Therefore the organizations might be able to use Push notification to activate the customer's mobile purchasing.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.3
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• pp.274-282
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• 2002

This paper discusses a multi-dimensional analysis for Customer Relationship Management (CRM). For this, We propose a decision-making methodology which employs three analysis models. The first model is a three-dimension positioning map to derive a strategy which achieves the Process Value Line (PVL). The second model is the web-shape analysis model to visibly understand the individual based on the customer CSI (Customer Satisfactory Index) data. The third model which supports the web-shape analysis model, is the relative satisfactory analysis model. It considers a satisfaction level after purchasing against before purchasing. Then we perform overall analysis based on the three analysis models to provide marketing strategies to decision makers.

• The Journal of Economics, Marketing and Management
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• v.7 no.3
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• pp.1-12
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• 2019

Purpose - This study aims to investigate the major capabilities of airline cabin crew to improve the performance of the organization by identifying how they are affected by turnover and customer orientation. Research, design, data, and methodology -The survey participants were limited to all airline cabin crew members in Korea to look at the component measurement items. To verify the validity of the questionnaire, the final questionnaire for this survey was prepared by modifying and supplementing the questionnaire by analyzing factors and validating the questionnaire through reliability verification Results - The analysis on the impact of personal capacity of the airline cabin crew on turnover revealed that some factors had an effect of the positive and the personal capacity of the airline cabin crew has a statistically positive effect on the customer orientation relationship, which is a sub-factor of the cabin crew Conclusions -The capacity of the cabin crew of the airline was defined and the components were established as technical capacity, knowledge capacity and expertise capacity. It was found that the intangible performance of the individual capabilities and customer orientation were very closely related. Airline cabin crew have verified the importance of good talent selection and capacity development training, which are essential requirements for securing the airline's competitiveness.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• Journal of Information Technology Applications and Management
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• v.14 no.4
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• pp.199-219
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• 2007

This research analyze the impact factors that affect the PI resistance of securities brokerage salespersons during Process Innovation in securities companies focusing on the broker's individual personality. After reviewing some related literatures, a survey was conducted at a domestic securities company with the derived factors from a focus group of securities salesmen with over 10 year work experience. The results show that broker's individual propensity to innovation, individual customer relationship and individual flexibility are closely related to the PI resistance. By controlling such factors for salespersons, securities companies can boost the ability to meet and control the changing situation and management innovation.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.33
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• pp.167-190
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• 2007

The aim of the study is to investigate the factors which decide an airport service quality and the differentiation of perception on the airport service quality between airport users and airport authority. For the purpose of this study, the determinants of airport service quality were factors analyzed on the basis of service marketing concept. In order to identify an airport service quality dimension, the writer conducted mail survey and individual interview from the Korean freight forwarders, the 3PL entities, integrators and the airport operation authority. The result of this study is summarized as follows. 1. The dimen measurement was confirmed as a superior method to dimension of service competitiveness.sions of airport service quality consist of five factors; tangibles, reliability, responsiveness, assurance, empathy. 2. There are notable differences in cognition of airport service quality between airport authority's perception and airport customer, and between airport customer's perception and expectation.

• The Journal of Information Systems
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• v.26 no.4
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• pp.203-225
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• 2017

Purpose This paper tired to identify the consumers' tendency with individual features and social influence as the independent variables because of the importance of identifying the various needs of financial consumers in accordance with the expansion of new financial demands. Furthermore, this paper included the relative attraction of Fintech and switching cost from mobile banking to Fintech as the variables. Design/methodology/approach To analyze the empirical study, data was collected online by conducting the questionnaire survey with 247 individuals who had used the Fintech service. The study results would provide us with the understandings of the factors influencing on the customer satisfaction and switching value recognized by consumers in case of switching from the existing mobile banking to the Fintech service. Findings The results would provide useful implications to the academic and the practical fields including the Fintech enterprises expanding the activation of Fintech industry.

• ETRI Journal
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• v.33 no.5
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• pp.720-730
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• 2011

Opinion mining involves the analysis of customer opinions using product reviews and provides meaningful information including the polarity of the opinions. In opinion mining, feature extraction is important since the customers do not normally express their product opinions holistically but separately according to its individual features. However, previous research on feature-based opinion mining has not had good results due to drawbacks, such as selecting a feature considering only syntactical grammar information or treating features with similar meanings as different. To solve these problems, this paper proposes an enhanced feature extraction and refinement method called FEROM that effectively extracts correct features from review data by exploiting both grammatical properties and semantic characteristics of feature words and refines the features by recognizing and merging similar ones. A series of experiments performed on actual online review data demonstrated that FEROM is highly effective at extracting and refining features for analyzing customer review data and eventually contributes to accurate and functional opinion mining.