• The Research Journal of the Costume Culture
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• v.31 no.3
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• pp.310-329
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• 2023

To compete with the growth of fashion shopping platforms in the online fashion market, general shopping platforms have begun to expand their product categories to include fashion items. This research examines the characteristics that influence consumers' trust in each of these platforms and their intention to reuse them. Applying the concept of platforms, this study also distinguishes between general shopping platforms and fashion shopping platforms and compares their characteristics. This study surveyed 788 consumers in their 20s and 30s with experience in using general shopping platforms or fashion shopping platforms (389 and 399 respondents, respectively). SPSS was used to conduct frequency analysis, factor analysis, and cross-tabulations, and AMOS was used to conduct confirmatory factor analyses and structural equation analyses. The results were as follows: platform reputation, shopping convenience, and interactivity all influenced consumer trust. For fashion shopping platforms, the product quality factor significantly improved consumer trust. However, for general shopping platforms, the product quality factor only influenced reuse intentions to reuse and did not contribute to improving trust. Platform reputation and information offering have influenced reuse intentions for both shopping platforms. Regardless of the type of shopping platform, platform reputation has influenced reuse intentions and consumer trust, and platform esthetics didn't have affect consumer trust and consumers' reuse intentions. Consumer trust influenced the intention to reuse on both platforms.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.401-406
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• 2020

This study is an analysis of the user experience of online hobby class platform, focusing on the art field, which reduces space-time constraints and lowers accessibility. To this end, I investigated cases of online hobby class platforms and conducted surveys to measure user experience for university students and office workers in their 20s and 30s in Korea. As a result, we found the gap between the elements satisfying the user experience and the current domestic service, and suggested a way to narrow it down. This study is meaningful in that it proposed ways to further promote online hobby class platforms in the future leisure culture market. It is hoped that this study will be used as data on the user experience of future online hobby platforms and further help discuss practical activation measures.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.4
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• pp.685-702
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• 2021

This study aims to discover how the perceived attributes of luxury fashion platforms affect consumer trust and satisfaction as well as online word-of-mouth intention. Based on a literature review, this study derived four dimensions of perceived attributes: brand assortment size, exclusivity, convenience, and personalization. The paper presents findings from an online survey targeting 359 consumers in their 20s to 30s who had recent experience with luxury fashion platforms. Based on the collected data, a structural model equation analysis was performed using AMOS 22.0 and SPSS 26.0. The findings illustrated that brand assortment size, exclusivity, and personalization had positive effects on consumers' platform trust. In addition, brand assortment size and convenience had a positive impact on satisfaction. Overall, the findings of the study illustrate that perceived attributes of luxury fashion platforms have a significant impact on consumers' platform trust and satisfaction and online word-of-mouth intentions. This study reveals that consumers' trend orientation moderates the effects of consumer attitude and behavioral intention. The academic practice of this study has laid the foundation for understanding mechanisms of marketing strategies by providing the characteristics of platforms in the luxury fashion industry.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.147-161
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• 2022

Video on demand (VOD) platforms provide immersive, inspiring, and commercial-free binge watching experiences. Recently, the number of these platform users increased dramatically as users can enjoy various contents without physical and time constraints during COVID-19. However, such platforms do not provide sufficient video censorship services while there is a strong need. In this study, we investigated the users' desire for video censorship when choosing and watching movies on VOD platforms, and how video censorship can be applied to different types of scenes to increase the censoring effect without diminishing the enjoyment. We first conducted an online survey with 98 respondents to identify the types of discomfort while watching sexual, violent, or drug-related scenes. We then conducted an in-depth online interview with 18 participants to identify the effective video filtering types and regions for each of the three scenes. Based on the findings, we suggest implications for designing a censor application for videos that contain uncomfortable scenes.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.8
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• pp.117-126
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• 2021

The increasing number of retrenchments because of the current global pandemic, Covid-19, has led many to shift to the digital economy, especially among the low-income group (B40) in Malaysia. Crowdsourcing is the collection of information, opinions, or work from a group of people, usually sourced via the Internet. Fueled by the development of Internet-based platforms that provided its technological foundation, and the need for an agile and uniquely skilled workforce, crowdsourcing has grown from the grassroots, with a burgeoning body of research investigating its many aspects. However, very few studies examined crowd workers' motivation for continuous participation on online crowdsourcing platforms. Thus, this paper aims to explore the technological factors that facilitate B40's group motivation in Malaysia to continue to participate in online crowdsourcing platforms. This paper employed a qualitative approach, using a semi-structured interview. The thematic analysis method was used to decode the data extracted from the interview transcript. The finding of this study identified four main themes and seven sub-themes: (1) Technology efficacy, (2) Platform Management: client-worker management, safety net, payment mechanism, (3) Platform Design: UI design, rating feature and (4) Infrastructure: Internet connection, technology infrastructure. This study can provide a guideline for managing crowdsourcing practices in Malaysia, especially for the crowdsourcing platform developer.

• Journal of Information Science Theory and Practice
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• v.12 no.1
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• pp.39-59
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• 2024

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites.

• Asia pacific journal of information systems
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• v.32 no.4
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• pp.945-963
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• 2022

With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information-both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.305-318
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• 2022

Abusive behaviors have become a common issue in many online social media platforms. Profanity is common form of abusive behavior in online. Social media platforms operate the filtering system using popular profanity words lists, but this method has drawbacks that it can be bypassed using an altered form and it can detect normal sentences as profanity. Especially in Korean language, the syllable is composed of graphemes and words are composed of multiple syllables, it can be decomposed into graphemes without impairing the transmission of meaning, and the form of a profane word can be seen as a different meaning in a sentence. This work focuses on the problem of filtering system mis-detecting normal phrases with profane phrases. For that, we proposed the deep learning-based framework including grapheme and syllable separation-based word embedding and appropriate CNN structure. The proposed model was evaluated on the chatting contents from the one of the famous online games in South Korea and generated 90.4% accuracy.

• Journal of Information Technology Services
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• v.14 no.3
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• pp.217-236
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• 2015

How does social interaction among investors affect decision-making in the online social lending platform? And what is the reason? In this study, in order to obtain the answer, we carried out case study research of Moneyauction and Popfunding, which are domestic online social lending platforms. We conducted interviews with managements of both social lending platforms and investors and analyzed statistical data including investment records, social interaction history between investors and lenders from both platforms. In addition, researchers performed direct participation and observation through the platforms as real investment members. As a result, we revealed that social interaction among investors has a material impact on the investment decision-making. Also we found that investors build trust by socially interacting with each other and this trust building leads to the investment decision making. Our findings confirm that social lending investors's decision-making process comply with the social embeddedness theory and imply that loan applicants must do their best efforts to display sincerity and truthfulness through their posting.