• Asia pacific journal of information systems
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• v.22 no.1
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• pp.53-77
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• 2012

Financial firms, especially large scaled firms such as KB bank, NH bank, Samsung Card, Hana SK Card, Hyundai Capital, Shinhan Card, etc. should be securely dealing with the personal financial information. Indeed, people have tended to believe that those big financial companies are relatively safer in terms of information security than typical small and medium sized firms in other industries. However, the recent incidents of personal information privacy invasion showed that this may not be true. Financial firms have increased the investment of information protection and security, and they are trying to prevent the information privacy invasion accidents by doing all the necessary efforts. This paper studies how effectively a financial firm will be able to avoid personal financial information privacy invasion that may be deliberately caused by internal staffs. Although there are several literatures relating to information security, to our knowledge, this is the first study to focus on the behavior of internal staffs. The big financial firms are doing variety of information security activities to protect personal information. This study is to confirm what types of such activities actually work well. The primary research model of this paper is based on Theory of Planned Behavior (TPB) that describes the rational choice of human behavior. Also, a variety of activities to protect the personal information of financial firms, especially credit card companies with the most customer information, were modeled by the four-step process Security Action Cycle (SAC) that Straub and Welke (1998) claimed. Through this proposed conceptual research model, we study whether information security activities of each step could suppress personal information abuse. Also, by measuring the morality of internal staffs, we checked whether the act of information privacy invasion caused by internal staff is in fact a serious criminal behavior or just a kind of unethical behavior. In addition, we also checked whether there was the cognition difference of the moral level between internal staffs and the customers. Research subjects were customer call center operators in one of the big credit card company. We have used multiple regression analysis. Our results showed that the punishment of the remedy activities, among the firm's information security activities, had the most obvious effects of preventing the information abuse (or privacy invasion) by internal staff. Somewhat effective tools were the prevention activities that limited the physical accessibility of non-authorities to the system of customers' personal information database. Some examples of the prevention activities are to make the procedure of access rights complex and to enhance security instrument. We also found that 'the unnecessary information searches out of work' as the behavior of information abuse occurred frequently by internal staffs. They perceived these behaviors somewhat minor criminal or just unethical action rather than a serious criminal behavior. Also, there existed the big cognition difference of the moral level between internal staffs and the public (customers). Based on the findings of our research, we should expect that this paper help practically to prevent privacy invasion and to protect personal information properly by raising the effectiveness of information security activities of finance firms. Also, we expect that our suggestions can be utilized to effectively improve personnel management and to cope with internal security threats in the overall information security management system.

• Journal of Distribution Research
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• v.11 no.4
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• pp.49-70
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• 2006

This study aims to identify the effect of information quality of CRM and salespersons' adaptive selling behavior(ASB) on their performance. For this research goal, the authors established research model and hypotheses which focus to the mediating role of quality of customer relationship. The results of empirical analysis show that information quality of CRM has no direct effect on salesperson performance and just indirectly influence to it via quality of customer relationship. Compared to this result, ASB directly and indirectly affects on performance. These results suggest that the most important thing to enhance salesperson performance is the maintenance of good relationship with customers.

• Journal of Distribution Science
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• v.18 no.8
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• pp.5-14
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• 2020

This study aims to examine the impact of characteristics of SNS content provided by airlines on customer satisfaction and to explore the effect of the personal characteristics of the customers which have a modulating effect on the relationship between content characteristics of SNS and customer satisfaction. The data were collected over a period of April 21. 2019. to July 21. 2019. for three months from the people who have experience using SNS provided by airlines. 225 samples were selected and used as final samples from a collection rate of 93.4 percent. Findings suggest that the characteristics of airline's SNS content which include a promotion, information, and communication showed a positive impact on customer satisfaction. The analysis of a modulating effect of individual characteristics on the relationship between the airline's SNS content characteristics showed that diversity pursuit propensity, innovation propensity, and information-seeking propensity control the relationship between characteristics of airline's SNS content which are publicity, information and customer satisfaction. And it was found that all three personal characteristics do not control the relationship between communication and customer satisfaction. Thus, airlines should use SNS more effectively to generate customer satisfaction and present marketing measures to help generate profits through consumer purchasing behavior. It is expected that the findings of this study will help airlines to utilize marketing implications for effective and practical marketing methods.

• Asia pacific journal of information systems
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• v.33 no.3
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• pp.737-767
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• 2023

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.113-125
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• 2019

Mobile social commerce is one of the fastest growing distribution channels in recent years. Therefore, it is important to understand customer's buying behavior in mobile social commerce in order to continuously grow in the competitive mobile social commerce market. To achieve the purpose of this study is to investigate how impulsive buying behaviors are applied in mobile shopping and how factors affect impulse purchasing in online shopping. In order to verify the hypothesis, we surveyed the customers who have experiences of using mobile social commerce and analyzed 280 valid data by Smart PLS 3.0. As a result, it was confirmed that consumers' innovation and purchasing experience influenced impulse purchase in mobile social commerce, and scarcity messages among information attributes affect impulse buying. Through this study, impulsive buying behavior which is a frequently analyzed variable in an online shopping context will be extended to the mobile shopping context. and it will provide practical implications for customer strategy establishment in mobile social commerce market.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.105-117
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• 2019

Current Android smartphones include about 25 to 35 essential applications(unerasable) related to function and operation of the android smartphone itself and about 30 optional applications(removable) provided by carriers, Google and manufacturers. These applications were not able to be removed until the announcement of the smartphone applications pre-install guide from the government in January 2014, so there were memory limitations in installing new applications, causing consumer complaints by consuming data during the auto-update process of the pre-installed applications. After the announcement, we were able to delete optional applications but the complaints about the data consumption still did not disappear. Therefore, in this paper, we carried out the customer survey and analyzed the behavior information such as how carriers are operating pre-installed applications and what kind of applications customer prefers and how many applications customer wants to be pre-installed. And we proposed how to configure pre-install applications on smartphone for customer needs.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.231-244
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• 2012

Consumer dissatisfaction and complaints make it difficult for online shopping mall to maintain existing customers and attract new customers, which may result in a direct profit loss. The primary purpose of this study is to investigate the relationship between customer dissatisfaction in terms of product-complaints and website complaints and complaint behaviors in terms of individual, public, and 3rd party complaint behaviors. Th results indicate that product-dissatisfaction is significantly related to the public and 3rd party complaint behaviors, but not the individual complaint behavior. Website dissatisfaction was not found to be significantly related to any complaint behavior. The moderate effect of sex and individual attitude on the relationship between customer dissatisfaction and complaint behaviors was also not considerable.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.81-96
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• 2007

The headline of advertisements holds the important position in advertisement recognition and receptivity. In this paper, we classified the headlines as several types with measuring the response effects for purpose of understanding the customer's behavior. Also we conducted the decision tree analysis and correspondence analysis. The decision tree shows the difference between responders and non-responders, and the correspondence analysis shows the relationship between the sales and the customer's demographic information.

• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.55-70
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• 2016

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.