• The Journal of Information Systems
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• v.23 no.1
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• pp.29-52
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• 2014

As the advance of multi-platform and multi-channel online services, brokerages are now offering three representative online trading systems - HTS(Home Trading Systems), WTS(Web Trading Systems), MTS(Mobile Trading Systems). In this study we investigated and compared the impact of different systems on the performance of brokerages. Using the panel data of 29 brokerages of 4 periods, we empirically tested the impact of online trading systems and the commissions of trading services. We found out that there exist some differences between the impacts of online trading systems based on the platforms. HTS was identified as the main platform for online trading services. However the role of MTS was also significantly identified while WTS showed no significant impact on the brokerage performances. Commission also showed significant negative impact in case of HTS and MTS platforms. Finally, offering MTS was identified as the significant dummy variable influencing the performance of brokerages. The results provides some implication for the multi-platform strategy for online services.

• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Journal of Korea Trade
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• v.26 no.7
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• pp.145-166
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• 2022

Purpose - The field of online videos has seen rapid changes in information and communications technology (ICT) development. Despite active academic research on the use of online platforms, few studies have analyzed the relative importance among the factors determined. In this study, the relative importance of factors found in previous studies was identified for users of online video platforms in China and Korea. Through this, factors that should be considered first in research on online video use were derived. In addition, the quality level of online video platforms currently used in China and Korea was measured and used for analysis. The analysis results can provide information for companies to enter Chinese and Korean markets and also be useful to platform providers aiming to increase usage. Design/methodology - Among the factors of Online Video Usage identified in previous studies, 13 factors to be studied were selected through focus group interviews and hierarchized into 2 layers. For the analytic hierarchy process (AHP), each factor was designed as a pairwise comparison questionnaire. The survey included questions on the quality of online video platform currently in use. Data collection was conducted on 16 platforms in China and 11 platforms in Korea, and the relative importance of factors and user perspectives was compared and analyzed using importance performance analysis (IPA). In the analytical process, platforms were divided into over-the-top (OTT) group and Creator group according to the weight of user-generated content, and data analysis focused on these groups. Findings - As a result of AHP, China and Korea showed both "Fun" and "Interests" factors at the top, while the importance of the Entertainment factor "Vicarious satisfaction" was very different for China and Korea. "Relationship with content creators" was the most important factor in China, but it ranked the lowest in Korea. The IPA showed that the factors with high importance and performance were fun, interests, and easy accessibility for both China and Korea. In contrast, the factors that showed low performance compared to high importance in China were relationship with content creators, relationship with acquaintances/friends, and trustworthiness. As for Korea, vicarious satisfaction was observed; thus, this study has raised the need for academic and industrial interest in vicarious satisfaction. The results show that fun, interests, vicarious satisfaction, and easy accessibility of the platform are factors that must be included in further studies on online videos. Originality/value - Existing studies related to the use of online platforms have derived factors or focused on the influence relationship between factors and performance. However, few studies have analyzed the relative importance among the determined factors. This paper explores factors to be considered in future studies by deriving the relative importance between these factors from the perspective of users in China and Korea.

• 한국벤처창업학회:학술대회논문집
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• 2021.11a
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• pp.113-117
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• 2021

Due to the recent COVID-19 pandemic, SMEs are forced to converge themselves into online business to cope with the rapidly changing business environment. However, due to various difficulties of becoming an online business, most SMEs choose to use services provided by the online sales platform. These platforms offer valuable support such as DSS in return for the high amount of commission fee. We analyze a novel data set from Naver Corporation's 'Smart Store' platform with the quasi-experimental method (propensity score matching technique combined with the difference in differences analysis) to analyze DSS usage and SME's performance empirically. Our results suggest that DSS usage leads to an increase in SME's sales performance in means of sales frequency and sales amount. Additionally, we have found weak support that DSS usage enables SMEs to attract customers better than those not using DSS.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.630-646
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• 2022

This study examines online art platforms as a new type of informational technology and seeks to verify the impact of the user's perceived value and acceptance of new technologies on the behavioral intention about art platforms. For this, it was conducted to survey 489 users of the online art platform. Several emotional value, functional value, social value, and value for money were selected as perceived value variables, and performance expectancy, effort expectancy, and facilitating conditions were set as variables of new technology adaptation. As evident in the correlation with perceived values, emotional value and social value have a significant impact on all parameters of new technology adaptation, while functional values and value for money only affect facilitating conditions and behavioral intention, respectively. Furthermore, the impact of acceptance of new technologies confirms that performance expectancy and facilitating conditions affect behavioral intention. This study demonstrates the perceived value of online art platform users and the acceptance of new technologies. Therefore, it is expected that platform providers will be able to use it as primary data to understand and reflect user requirements.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.53-72
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• 2018

This empirical study explored characteristics that affect the usefulness of online reviews, in the China e-commerce platform, and implemented multiple regressions to find factors that significantly influence on product sales, ultimately. Till now, prior studies have continuously revealed what factor affects usefulness of online review or product sales, only in respective terms. The point of our study is that we built two-level regression models, thereby being able to comprehensively analyze these two different targets. Before plunging into running regressions, we carefully collected 192,764 online review data for 200 products extracted from the Jingdong, the second biggest e-commerce platform in China. Also, we gathered "review sentimental scores" variable from each review and used that one as a core variable in our regression model, thus we were able to implement both quantitative and qualitative research. The evidences from the two-level regression models showed that the extent to which a product is experience good positively affects both usefulness of a review and product sales, again the usefulness of a review contributes to product sales in sequence. Also, the property of experience good has interaction effect on both for two-level regression models. Our main findings highlight the importance of role of online review to business performance of e-commerce firms.

• Fashion & Textile Research Journal
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• v.24 no.4
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• pp.386-398
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• 2022

This study was conducted to corroborate the factors that influence consumer characteristics and technology acceptance on online luxury platforms, which are rapidly emerging as distribution channels for luxury brands. To this end, the relationship between the degree of technology acceptance and behavioral intention of fashion consumers on online luxury platforms and the effect on specific factors such as age and gender was investigated to see if there was a difference in behavior and use behavior. A survey was conducted on Korean consumers between the age of 20 and 40 who have used online luxury platforms and then, a statistical analysis was conducted. As a result of the study, performance expectancy and facilitating conditions, hedonic motivation, price value, and habit were found to have a significant effect on platform behavioral intention, but effort expectancy and social influence did not have a significant effect. Additionally, both facilitating conditions and habit were found to have a directly significant effect on the platform use behavior, and it was confirmed that the platform behavior intention also had a significant effect on the use behavior. As a result of confirming the moderating effect of gender and age, there was no difference based on gender, but only the relationship between price value and behavioral intention was found to have a moderating effect. It is hoped that domestic online luxury platforms will grow into channels with distinct characteristics and continue to develop in the luxury market by utilizing specific affect factors of this study.

• Fashion & Textile Research Journal
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• v.25 no.3
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• pp.291-303
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• 2023

The purpose of this study is to investigate the impact of technical acceptance factors of Chinese MZ generation consumers' use of online fashion platforms on behavioral intentions and usage behavior, and to determine whether satisfaction plays an additional role in the relationship between behavioral intentions and usage behavior. The research results can be summarized as follows. First, among the technology acceptance factors that influence Generation MZ consumers when they use online fashion platforms, it was found that performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, price value, habit, and content quality significantly influence the behavioral intention to use online fashion platforms. Second, the facilitating conditions and the platform usage intention of Chinese Generation MZ consumers directly and significantly influence the usage behavior on online fashion platforms. In contrast, it was confirmed that habit does not significantly influence actual usage behavior. Third, this study confirmed the moderating effect of satisfaction on the relationship between behavioral intention and usage behavior on online fashion platforms. This research, which proposes a research model explaining the usage intention and behavior of online fashion platforms based on UTAUT2, integrating not only the organizational aspect but also the consumer aspect to empirically verify the usage intention and the final usage behavior from the general consumer's perspective, has academic significance.

• Journal of Platform Technology
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• v.11 no.3
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• pp.45-55
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• 2023

This paper presents a model that supports decision-makers in the Korean film industry to maximize the success of online movies. To achieve this, we collected historical box office movies and clustered them into types to propose a model predicting each type's online box office performance. We considered various features to identify factors contributing to movie success and reduced feature dimensionality for computational efficiency. We systematically classified the movies into types and predicted each type's online box office performance while analyzing the contributing factors. We used automated machine learning (AutoML) techniques to automatically propose and select machine learning algorithms optimized for the problem, allowing for easy experimentation and selection of multiple algorithms. This approach is expected to provide a foundation for informed decision-making and contribute to better performance in the film industry.