• Journal of Industrial Convergence
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• v.19 no.6
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• pp.73-79
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• 2021

The purpose of this study was to find out how the importance and satisfaction of social commerce selection factors perceived by social commerce users differ, and how social commerce selection factors affect repurchase intention. A survey was conducted for 17 days from September 1st to September 17th, 2021, and 316 copies were used for empirical analysis. As a result of the analysis, the selection factors of social commerce were divided into six factors: safety, convenience, economy, informativity, collectivity, and SNS relevance. IPA results were in the order of convenience in the first quadrant, relevance and informativity in the second quadrant SNS, collectivity in the third quadrant, stability in the fourth quadrant, and economy. In the relationship between social commerce selection factors and repurchase intention, convenience, economy, safety, and information among social commerce selection factors were found to have a significant influence on repurchase intention. As a result of this study, it was confirmed that if existing social commerce was important at an affordable price, convenience is more important for non-face-to-face commerce in the COVID-19 situation. It is considered important to have a system that can continuously identify and preemptively respond to users' shopping trends through IPA.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.2
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• pp.63-74
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• 2012

The purpose of this study was to find factors affecting satisfaction and intention to re-purchase fashion goods through social commerce. A questionnaire method was applied for 123 women aged from twenties to thirties, with buying experience in fashion goods through social commerce. Independent variables were service quality, fashion shopping orientation, and demographics. Factor analyses and multiple regression methods were used to analyze data. Factor analyses resulted in two factors for service quality and resulted in four factors for fashion shopping orientation. The results of multiple regression analyses showed that convenience & benefits and site layout factors of the service quality had significant impacts on satisfaction in fashion social commerce. Those two service quality factors, demographics like job, and satisfaction were shown significantly important to predict intention to re-purchase fashion goods on social commerce service. Intention to re-purchase was best explained in the model with satisfaction as an independent variable. Meanwhile, shopping orientation factors were not important in any model.

• Journal of Information Technology Services
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• v.12 no.2
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• pp.31-53
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• 2013

Web 2.0 has affected existing e-commerce and created a new business model of e-commerce, known as social commerce. Social commerce is a subset of e-commerce using social network services and is emerging as an important platform due to increased popularity of social networking services. This study focuses on analyzing the factors that influence the shopping value and intention to repurchase of social commerce users. Based on prior researches, we develop a research model, including individual characteristics of social commerce users (Collectivism, Price Sensitivity, Impulse Buying) and social commerce characteristics (Cost saving, Product Variety, Shopping Convenience). Furthermore, this study proposed the moderating effect of Perceived Security and the relationship between shopping value and intention to repurchase. To empirically validate, the data were collected from 220 social commerce users. The results indicated that individual characteristics (collectivism, price sensitivity, impulse buying) were positively related to hedonic shopping value. In addition, social commerce characteristics (cost saving, shopping convenience) were positively related to utilitarian value. The shopping value(hedonic and utilitarian) had a significant influence on intention to repurchase. The moderating effects of perceived security also was significant. Lastly, the implications for theory and practice are discussed.

• The Journal of Information Systems
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• v.20 no.4
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• pp.205-232
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• 2011

With the rapid growth of internet technology, social commerce has played an important and central role in the online shopping area. Thus, we focus on the factors that influence on the adoption of social commerce. This study analyzes the relationship between perceived risk and the shopping motive in social commerce, and investigates whether the shopping motive significantly impact the purchase intention of the social commerce. The perceived risk is comprised of social risk and psychology risk, and the shopping motive is formed from personal motive and social motive. Finally, we analysis a moderating effect of collectivism. The results indicated that the personal motive was negatively affected by the social risk and psychology risk, and social motive was negatively affected by the psychology risk. The social risk and psychology risk had negative effect on the purchase intention, and personal motive and social motive had positive effect on the purchase intention of the social commerce. Finally, low collectivism seems to have the negative effect of the purchase intention by the perceived risk. The implications of integrating perceived risk and shopping motive into the proposed social commerce adoption model are discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.293-295
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• 2015

This paper proposed have been the business card information to the computer when creating business card printing agency saved to a file, there is always the risk of personal information leakage. Application file organization information into the card, the name, phone number, email address information, such as is capable of easily accessible because it is not encrypted. This paper proposed it encrypts the information entered on the Business Card application file to automate the process of the card application and simplifying the business card application process minimizes the work of staff and linked directly to the print shop how to automatically delete the print file after the completion of business card printing and research.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used