• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.53-64
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• 2010

This study is aimed at identifying the association of change of travel frequencies with information and communications technologies, commuting behavior for 995 workers in the Korea Capital Region. The study surveyed total 995 commuters whose their individual character, commuting behavior, land use as well as ICTs. The measures of the commuting behavior was comprised of a main commuting mode, a use tern, total travel time, and those of land use was the distance from house/office to subway station, and those of ICTs was data and information collection, communication and leisure, online selling or purchases, finance and a civil application, cellular phone service using capacity and so on. The results indicate that commuting behavior, land use, and ICTs are positively associated to change of travel frequencies. Specifically, longer total travel time, or far from house/office to subway station, tend to reduce commuting behavior and collect data and information through internet

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.119-124
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• 2021

Today, both sides of modern culture are decisively invaded by digitalization. Authentication is considered to be one of the main components in keeping this process secure. Cyber criminals are working hard in penetrating through the existing network channels to encounter malicious attacks. When it comes to enterprises, the company's information is a major asset. Question here arises is how to protect the vital information. This takes into account various aspects of a society often termed as hyper connected society including online communication, purchases, regulation of access rights and many more. In this research paper, we will discuss about the concepts of MFA and KBA, i.e., Multi-Factor Authentication and Knowledge Based Authentication. The purpose of MFA and KBA its utilization for human.to.everything..interactions, offering easy to be used and secured validation mechanism while having access to the service. In the research, we will also explore the existing yet evolving factor providers (sensors) used for authenticating a user. This is an important tool to protect data from malicious insiders and outsiders. Access Management main goal is to provide authorized users the right to use a service also preventing access to illegal users. Multiple techniques can be implemented to ensure access management. In this paper, we will discuss various techniques to ensure access management suitable for enterprises, primarily focusing/restricting our discussion to multifactor authentication. We will also highlight the role of knowledge-based authentication in multi factor authentication and how it can make enterprises data more secure from Cyber Attack. Lastly, we will also discuss about the future of MFA and KBA.

• Knowledge Management Research
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• v.22 no.1
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• pp.269-286
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• 2021

As the un-tact and on-tact consumption culture has proliferated due to the impact of COVID-19, 'live commerce', a form of shopping while communicating with customers through real-time streaming broadcasting, is emerging in the commerce and distribution industry. Live commerce provides an environment where customers can get the convenience of online shopping and enjoy un-tact shopping more realistically while communicating with the broadcaster in real time, as if purchasing directly from an offline store. Therefore, purchases using live commerce are expected to increase further. In this study, based on the characteristics of live commerce, the main factors influencing the increase in purchase intention through live commerce were derived and their influences were verified. In particular, this study examined these factors in multiple dimensions with focusing on strong interactivity, realistic presence, and providing detailed reviews with high credibility for products as the features of live commerce. This research collected sample data from actual users of live commerce and empirically analyzed the significance of the factors influencing the purchase increase of live commerce, thereby providing implications for knowledge management in a newly changed commerce environment in the un-tact era.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.491-502
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• 2022

Voice technology allows consumers to make purchases through smart devices, and the interest in voice-driven conversational commerce has significantly expanded. In this study, we explored the continuance use intention of voice commerce, and the adoption of a value-attitude-behavior model. An online survey was conducted on 360 individuals who used an artificial intelligence assistant device in a voice commerce environment. We used Amos 23.0 and SPSS 25.0 for descriptive, confirmatory, and structural equation modeling analyses. These results indicated that functional value was the highest influencing variable on satisfaction of voice commerce, while social, emotional, and epistemic values significantly influenced it as well. Additionally, satisfaction of voice commerce significantly influenced the continuance use intention of voice commerce. These findings could help us understand the characteristics of voice commerce users and the diversity value in voice commerce environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.

• The Korean Journal of Food And Nutrition
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• v.37 no.3
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• pp.162-170
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• 2024

The domestic swine industry is currently facing a threat due to the recent increase in pork imports. This study aims to determine what factors influence consumers' intention to consume imported pork and suggest measures to support the domestic pork industry. To achieve this, we analyzed data from the Korea Rural Economic Institute's Food Consumption Behavior Survey using a binary logistic regression model. The results revealed that a higher intention to consume imported pork is linked to a higher intention to consume imported rice, purchasing meat online, frequent purchases of HMR, and procuring U.S. beef, especially among urban residents. On the other hand, a lower intention to consume imported pork is associated with a higher awareness of animal welfare certification, frequently dining out, and older age. Based on these findings, we propose the following response measures for the domestic swine industry: implementing educational programs, marketing, and advertising specifically targeting urban residents to improve their perception of domestic agricultural products; enhancing price competitiveness through distribution optimization; and developing policies to promote the use of domestic pork as an ingredient in processed foods.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.163-174
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• 2023

Purpose - This study examines the effect of consumers' motivations on the perception and purchase intentions of electric cars. Specifically, it empirically analyzes how moral motivations based on personal environmental values and norms in car usage and purchasing influence the perceived usefulness and purchase intentions of electric cars. Furthermore, it investigates whether the influence of moral motivations on perceived usefulness and purchase intentions varies according to the user's driving characteristics. Design/methodology/approach - An online survey was conducted with 234 respondents, by setting criteria for participants as car owners or primary car users within their households, ensuring the sample composition was not biased in terms of the presence or absence of experience with eco-friendly cars. Findings - The research findings indicate that perceived usefulness mediates the effect of consumers' moral motivations on their intention to purchase electric cars. The results of the moderating effect of driving distance on perceived usefulness revealed a significant interaction effect; however, there was no significant interaction effect on purchase intentions. Specifically, for individuals with shorter driving distances, as consumers' moral motivations increase, their perception of the usefulness of electric cars also increases. In contrast, for those with longer driving distances, the increase in perceived usefulness due to moral motivations shows a decreasing trend. Research implications or Originality - This study considered individual driving characteristics that previous research on electric vehicle adoption overlooked, and suggested that setting specific communicating points for electric cars according to driving distance levels might be effective. Lastly, it proposes directions for future research that motivations influencing eco-friendly vehicle purchases may differ based on driving characteristics

• Journal of Digital Convergence
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• v.11 no.12
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• pp.61-67
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• 2013

The B2B e-commerce assurance system supports e-commerce purchases by Credit Guarantee Fund. This process seeks to replace a variety of current systems, including B2C, the credit card payment method on B2B, 2001 Credit Guarantee Fund and the Bank, logistics, e-MP (Market Place), and Business-to-business e-MP (business-to-business electronic payment settlement system). Over the past 10 years of its operation, the e-MP service (B2B e-commerce electronic payment systems) has contributed much to the growth of SMEs. The development of business-to-business e-commerce transactions systems and limits have provided a stable purchasing platform, improving corporate competitiveness. However. the project-based scale of credit guarantee institutions has limitations. To overcome these limitations, we propose a new model of direct or indirect government support for small business e-MP projects. This new model will support the B2B electronic commerce by allowing it to directly involve guarantee institutions directly in B2B online transactions. Therefore, this study urges government backing of the SME based B2B online business model with e-MP service.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7494-7500
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• 2015

The purpose of this study is to investigate how the customer's attitudes and behavioral responses, depending on the persuasion tactics of salespeople in customer service and meeting point. The tactics of persuasion of customer acceptance and purchase depending on the type of salesperson with proven effectiveness of even comes out to investigate what results according to the purchase form and the customer that the customer consumes gender. Results First, the tactics of persuasion tactics of coercive tactics to mention the loss of a salesman showed that increasing the degree of acceptance than non-coercive tactics to help consumers buy the information provided above. Second, coercive tactics to adjust the effect with respect to the degree of acceptance of the purchase of helping consumers to purchase in the online form has been proven to be more effective than non-coercive tactics case. Third, adjusted for gender effects were proven to help women with respect to the degree of acceptance of a consumer purchases more effective than men. That is, in consumer contacts, the persuasion tactics of salespeople depending on the customer's acceptance and purchasing intention showed that coercive tactics has the more positive impact on online forms and women.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.