• Journal of Information Technology Services
• /
• v.11 no.1
• /
• pp.59-81
• /
• 2012

Recently, in the research on online environment such as e-commerce, and internet banking, the conceptual discussion about trust and risk in an effort to explain a user's behavior is briskly underway. Most of the research on trust and risk are setting up causal relationship without clearly establishing the relationship between trust and risk. Accordingly this study conducted a meta-analysis in order to consider the relationship between trust and risk and to take a look at the difference in causal relationship. This study includes a total of 18 research papers which are setting up the causal relationship between trust and risk in online environment among the research papers published in domestic academic journals since 2000. Result of the meta-analysis, showed that the effect size was -0.367 in the path from trust to risk; -0.131 in the path from risk to trust; -0.276 in the bidirectional path between trust and risk. In addition, this study was able to confirm through literature review that there appeared a high effect of path in case where trust and risk were measured by an uni-dimensional concept than by a multi-dimensional concept.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.05a
• /
• pp.89-93
• /
• 2014

Reviews are very important for potential consumer' making choices. They are also used by manufacturers to find problems of their products and to collect competitors' business information. But someone write fake reviews to mislead readers to make wrong choices. Therefore detecting fake reviews is an important problem for the E-commerce sites. Support Vector Machines (SVMs) are very important text classification algorithms with excellent performance. In this paper, we propose a new incremental algorithm based on weight and the extension of Karush-Kuhn-Tucker(KKT) conditions and Convex Hull for online Review Spam Detection. Finally, we analyze its performance in theory.

• Asia-Pacific Journal of Business
• /
• v.15 no.1
• /
• pp.273-287
• /
• 2024

Purpose - This study conducts the co-occurrence analysis, one of the social network analysis using global OTA's online reviews and grades in order to understand the experience structure of foreign tourists visiting cutural tourism resources in Jeju, Korea. Design/methodology/approach - For this purpose, this study selects 6 cultural tourism resources in Jeju as the study sites, and collects qualitative review data (noun, adjectives, and verb) and quantitative grade data. Findings - The co-occurrence network analysis between words and grade of market and street shows that the grade of 5 appears the most simultaneous with pork, buy, lot, try, fresh, black, food, price, seafood, local, market, good, street, etc. and the grade of 1 connects with small, dish, better, taste, etc. And the co-occurrence network analysis between words and grade of tradition and folklore shows that the grade of 5 appears the most simultaneous with village, place, museum, visit, time, life, culture, women, diver, use, lot, etc. and the grade of 1 connects with minute, spend, room, recommend, honey, etc. Research implications or originality - The above research results are relevant in order to find out the core experience of foreign tourists using online review and grade generated by foreign tourists and use as the important information to develop the strategies related to the planning and management of cultural tourism resources.

• Asia pacific journal of information systems
• /
• v.30 no.4
• /
• pp.741-757
• /
• 2020

The outbreak of COVID-19 has changed our lifestyle. People spend much more time on YouTube, SNS and online shopping than before. Accordingly, the number of product review videos are steeply increasing in YouTube platform. When people watched the review videos, they might search additional information if they liked the videos. This study aims to investigate how the informativeness and the degree of attention gathering of product review videos influence on the product information sourcing intention and persuasion knowledge. We also try to find whether prior YouTube experience affects the relationship between the degree of attention gathering and persuasion knowledge. We conducted an online survey on 499 participants and analyzed using partial least square methods. Results show that 1) informativeness and the degree of attention gathering towards product review videos influence on the product information sourcing intention and user's persuasion knowledge. 2) Viewers' YouTube experiences moderate the increase of the viewers' persuasion knowledge caused by increasing the degree of viewers' attention gathering. This study implies that YouTube product review videos could be created in strategic manners. Also, it could be inferred that consumers' prior YouTube experiences may reduce negative potentials of the degree of attention gathering onto persuasion knowledge.

• International Commerce and Information Review
• /
• v.11 no.1
• /
• pp.219-238
• /
• 2009

The purpose of this study was to find out purchase motive factors working on our customers in online shopping mall industry developing day by day, and refer to previous literatures to analyze possible effects of those factors on Satisfaction, so that it could identify which purchase motive factors may have effects on Satisfaction.

• Journal of Korea Society of Industrial Information Systems
• /
• v.23 no.3
• /
• pp.97-116
• /
• 2018

Individuals depend considerably on gathering information from personal social networks rather than from commercial network channels or the mass media. Most academic journals that have examined this topic concentrate on online users' information-searching behaviours; however, this paper discusses online users' information-providing behaviour in the online community. The aim of this study is to investigate that online users' motivation to write reviews on Facebook and how the motivations affect users' information-providing behaviour. This study focusses on Facebook members' motivations that affect their review-writing behaviour. The fundamental theory for examining this topic is Vogt and Fesenmaier's (1998) 'information need'. This study modifies Vogt and Fesenmaier's (1998) theory for virtual communities through the development of each concept's measurement items, selecting the information need of four variables: functional, hedonic, innovation, and sign need. Among the four variables, sign need is the most important factor for Facebook users in the virtual environment. Through sign need, people indicate their status, personality form, and position, which significantly affects members' review-writing behaviour on Facebook.

• Journal of Information Technology Applications and Management
• /
• v.14 no.4
• /
• pp.97-120
• /
• 2007

Development of information technology and Internet brought big changes in information society. Quantity of information increased rapidly and various types of information were presented through diverse channels. This change brought an impact in electronic commerce environment. A large number of products are transacted in online market. And various search functions and product information are presented for supporting customer's decision making. This study examined the effect of external information on purchase decision-making in electronic commerce environment. An experiment was conducted to see the customer product review, unit sales, etc. on purchase decision-making process in online shopping mall based electronic commerce. As a result of study, external information referring to number of purchasing, positive product review, and reliability of information has a positive effect on purchase-decision. The significance of the study can be found in that it defined 1) external information has an effect on decision-making, 2) positiveness and reliability of product information showed that they have an influence on customer, and 3) when self opinion and other person's opinion are different, one is not satisfied with decision making process. The results of the study can be of practical use in the design and implementation of online shopping mall in electronic commerce.

• International Journal of Computer Science & Network Security
• /
• v.22 no.3
• /
• pp.396-404
• /
• 2022

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.

• Journal of Oriental Neuropsychiatry
• /
• v.34 no.1
• /
• pp.43-59
• /
• 2023

Objectives: Although a manual for a disaster medical support using Korean medicine doctors for disaster survivors has been developed, education programs for using the manual in disaster situations need to be developed. Thus, the purpose of this study was to analyze existing online education programs for disaster mental health to develop education programs for Korean medicine doctors. Methods: We conducted website searching for disaster mental health education programs using Google. Compositions, contents, hours, methods, costs, organizers, and targets of included educational programs were analyzed qualitatively. Results: After searching, eight websites among a total of 64 were included for the analysis. Lectures consisted of Psychological First Aid, Skills for Psychological Recovery, Self-Care, and Psychological Education after a disaster experience. Training hours for each program ranged from 30 minutes to 31 hours. All lectures were given only online. They could only be taken online. Free lectures were the most common ones. Most of them were for the general public. Conclusions: Findings of this study provide information regarding trends of online education programs for disaster mental health. Our information could be used for developing disaster trauma response education programs for Korean medicine doctors in the future.

• Asia pacific journal of information systems
• /
• v.31 no.4
• /
• pp.472-490
• /
• 2021

The detection of products with manipulated reviews has received widespread research attention, given that a truthful, informative, and useful review helps to significantly lower the search effort and cost for potential customers. This study proposes a method to recognize products with manipulated online customer reviews by examining the sequence of each review's sentiment, readability, and rating scores by product on randomness, considering the example of a Russian online retail site. Additionally, this study aims to examine the association between brand awareness and existing manipulation with products' reviews. Therefore, we investigated the difference between well-known and unknown brands' products online reviews with and without manipulated reviews based on the average star rating and the extremely positive sentiment scores. Consequently, machine learning techniques for predicting products are tested with manipulated reviews to determine a more useful one. It was found that about 20% of all product reviews are manipulated. Among the products with manipulated reviews, 44% are products of well-known brands, and 56% from unknown brands, with the highest prediction performance on deep neural network.