• The Journal of Asian Finance, Economics and Business
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• v.8 no.4
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• pp.939-948
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• 2021

The purpose of the research is to examine whether government regulation on Covid 19 pandemic has had a significant impact in economic sectors, particularly on consumer behavior. Thus there are three hypotheses, 1) viral marketing has an effect on online trust during the Covid-19 Pandemic Era, 2) viral marketing has an effect on impulse buying during the Covid-19 Pandemic Era, and 3) Viral marketing has an effect on impulse buying in the Covid-19 Pandemic Era through online trust. To test the hypotheses, questionnaires were distributed to 150 respondents, however, only 110 were selected due to incomplete data. There are 3 variables, namely viral marketing, online trust, and impulse buying, where online trust is also a mediating variable. Once the assumption test is completed, the researcher employs path analysis to test the hypotheses. The results are 1) there is an effect of viral marketing on online trust in the Covid-19 Pandemic Era, 2) There is no effect of viral marketing on impulse buying in the Covid-19 Pandemic Era, and 3) Viral marketing has an effect on impulse buying in the Covid-19 Pandemic Era through online trust. This means online trust succeed in mediating viral marketing-impulse buying relationship. The findings emphasized that the credibility of online trust enforce consumers in making buying decisions.

• Journal of Information Technology Applications and Management
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• v.28 no.2
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• pp.35-41
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• 2021

This study sought to find importance factors for the quality of Mongolian consumers' evaluation for fresh food through online purchase. To compare the priorities of factors determining the choice of service quality of online purchase for fresh food, this study used a decision model using the appropriate Analytic Hierarchy Process (AHP). Through a prior study, the main factors of quality were classified as delivery quality, product quality, marketing, and system quality, respectively According to the results of AHP the quality of deliver information and deliver duration time under delivery quality are the main factor, followed by hygiene and freshness of product quality were the next highest. When consumers purchase fresh food through an online market. they considered deliver information, delivery duration time, hygiene, freshness, and deliver cost as important factors.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.105-126
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• 2023

The domestic used car market continues to grow along with the used car online platform service. The used car online platform service discloses vehicle specifications, accident history, inspection history, and detailed options to service consumers. Most of the preceding studies were predictions of used car prices using vehicle specifications and some options for vehicles. As a result of the study, it was confirmed that there was a nonlinear relationship between used car prices and some specification variables. Accordingly, the researchers tried to solve the nonlinear problem by executing a Machine Learning model. In common, the Regression based Machine Learning model had the advantage of knowing the actual influence and direction of variables, but there was a disadvantage of low Cost Function figures compared to the Decision Tree based Machine Learning model. This study attempted to predict used car prices of six domestic brands by utilizing both vehicle specifications and vehicle options. Through this, we tried to collect the advantages of the two types of Machine Learning models. To this end, we sequentially conducted a regression based Machine Learning model and a decision tree based Machine Learning model. As a result of the analysis, the practical influence and direction of each brand variable, and the best tree based Machine Learning model were selected. The implications of this study are as follows. It will help buyers and sellers who use used car online platform services to predict approximate used car prices. And it is hoped that it will help solve the problem caused by information inequality among users of the used car online platform service.

• The Research Journal of the Costume Culture
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• v.21 no.1
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• pp.129-138
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• 2013

Consumers' perception of risk plays a major role in how they make online purchase decisions. Since online shopping is perceived to be riskier than in-store shopping, consumers engage in a variety of risk reduction strategies such as searching online for alternative products and alternative e-tailers. This study examines the influence of risk involvement on risk reduction strategies and customer satisfaction. It discusses three aspects of risk reduction strategies: time spent in making a purchasing decision, searching for alternative e-tailers, and searching for alternative products. Data from 294 female shoppers who had experience in purchasing fashion products online was analyzed. This study found that risk involvement had a positive influence on the time spent in making decisions, while the influence of risk involvement on searching for alternative retailers and alternative products was not significant. However, consumer satisfaction was negatively related to search for alternative retailers and positively related to risk involvement. This study provides a better understanding of customers' risk involvement and risk reduction strategies in online shopping. This information would be beneficial for marketers and retailers to reduce customer perception of risks and to promote online sales.

• Fashion & Textile Research Journal
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• v.24 no.4
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• pp.418-430
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• 2022

This study aims to analyze consumers' purchase decision-making process of buying avatar fashion items on the Metaverse platform. Drawing on the connection between the self-expression tendency of the MZ generation and that of avatars in the Metaverse, this study uses a qualitative research method to analyze how consumers express their self-image through the appearance of their avatars. Unlike previous studies on the clothing purchase decision-making process, this study shows that purchasing and consumption behavior involve the following six stages: recognizing desire, collecting information, evaluating alternatives, making purchases, evaluating the consumption, and post-purchase action-taking. In the first stage of the purchase decision-making process, consumers' desire arises with self-image expression and confirmation. In the second stage, consumers have a high tendency to shop in the best item category. In the alternative evaluation stage, consumers tend to seek items that match their highest standard while considering their personal preferences. In the fourth stage, when making actual purchases, unplanned purchase behavior often occurs along with an active practice of alternative evaluation. In the fifth stage, the evaluation of the consumption shows that consumers achieve satisfaction by applying a style to their avatars that they are unable to try in the real world. In the last stage, consumers often use their purchases to communicate their various styles with other online consumers. Therefore, we conclude that the online purchase decision-making process differs from the offline process as it is divided into six stages.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.3
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• pp.175-187
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• 2011

People could be effected by other's recommendations when they are in making decision to buy something. This phenomenon was called as 'word of mouth effect' and proved to be very significant to change consumer's attitude because of a lack of information about products or services what they needed. And also there are two kinds of views about Brand communication. One is that Brand communication would be weakening due to less cost to search information. the other is that Brand communication would be strengthen because of a lack of sensibility to product. Therefore, the purpose of this study is to examine the function of online word of month and the effect of brand communication by adopting a concept of BRQ. As The results, First, Online word of mouth have significantly effected on consumer's attitude even though those information are all texts and have been suspicious if it is true or not. Second, consumer brand relationship quality have a influence on consumer's attitude. In conclusion, This study would give implications for companies to build marketing strategies.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.733-741
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• 2012

In recent, many consumers visit the online shopping malls or price comparison sites to collect the information on the product category that they are interested in. However, the volumes of the data provided by such web sites are often too enormous, and significant number of consumers have trouble in making purchase decision based on the plethora of products and sellers. In this context, modern online shopping agents need to process the retrieved information in more intelligent way before providing them to the users. This paper proposes a novel approach for identifying the main price ranges hidden in a single product category. To this end, the price of an item in the category is represented as a row vector and k-means clustering analysis is applied to the price vectors to produce the clusters that consists of the product items with similar price vectors. Then, the main price ranges of the product category can be identified from the result of clustering analysis. In general, the price is one of the most important factors in the consumers' purchase decision, and the identified main price ranges will be helpful for the online shoppers to find appropriate items effectively.

• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.177-191
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• 2013

Social network has been expected to increase the value of social capital through online user interactions which remove geographical boundary. However, online users in social networks face challenges of assessing whether the anonymous user and his/her providing information are reliable or not because of limited experiences with a small number of users. Therefore. it is vital to provide a successful trust model which builds and maintains a web of trust. This study aims to propose a prediction method for the interpersonal trust which measures the level of trust about information provider in Facebook. To develop the prediction method. we first investigated behavioral research for trust in social science and extracted 5 antecedents of trust : lenience, ability, steadiness, intimacy, and similarity. Then we measured the antecedents from the history of interactive behavior and built prediction models using the two decision trees and a computational model. We also applied the proposed method to predict interpersonal trust between Facebook users and evaluated the prediction accuracy. The predicted trust metric has dynamic feature which can be adjusted over time according to the interaction between two users.

• Journal of Information Technology Services
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• v.4 no.2
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• pp.61-69
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• 2005

RFID is used for tracking systems in various business fields these days and these systems brought considerable efficiencies and cost savings to companies. Real-time based information acquired through RFID devices could be a valuable source of information for making decisions if it is combined with decision support tools like OLAP of a data warehouse that has originally been designed for analyzing static and historical data. As an effort of extending the data source of a data warehouse, RFID is combined with a data warehouse in this research. And OLAP is designed to analyze the dynamic real-time based information gathered through RFID devices. The implemented prototype shows that ubiquitous computing technology such as RFID could be a valuable data source for a data warehouse and is very useful for making decisions when it is combined with online analysis. The system architecture of such system is suggested.