• 복식문화연구
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• 제29권5호
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• pp.617-633
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• 2021

The purpose of this study is to examine emerging trends in fashion films and the impact of COVID-19 through analysis of Prada films produced before and after the initial spread of the COVID-19 pandemic. We selected 40 cases occurring prior to the pandemic, from June to December 2019, and 21 cases occurring since the outbreak, from June to December 2020. To identify relevant trends, we conduct a literature review and examine a range of case studies. First, travel restrictions and confronting activities currently inhibit production. Through our case study analysis, we identify nineteen cases in between before and after COVID-19. Secondly, Prada can be seen to mainly produce episodes and promotional films. Additionally, it develops content showcasing brand values in environmental, cultural, creative, and sport-related fields; intended audiences extend beyond the realm of fashion. Thirdly, a new film category began to develop after the outbreak of COVID-19, namely, narrative films utilizing virtual interactions. According to our analysis results, we expect film production that is increasingly facilitated by virtual communication, technology utilization, and online platforms to continue even after the resolution of COVID-19. New film categories will emerge, and we predict that the gap between the number of cases before and after COVID-19 will narrow.

• The Journal of Asian Finance, Economics and Business
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• 제8권4호
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• pp.43-54
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• 2021

The study evaluates India's technological advancement, which has created a range of opportunities for consumers to enter into digital payment space. Demonetization in India has forced all consumers and companies to embrace and create cashless digital payment platforms. The cashless economy scenario involves various factors for its adoption such as reach, availability and awareness. This study considers factors responsible for adopting new digital payment technologies in India's different regions across various consumers. The study includes descriptive statistics and variance analysis (ANOVA) to identify elements to achieve maximum satisfaction. The research collects data from 250 respondents living in India, experiencing digital payments and online transactions. The data is collected through a structured questionnaire and critically analyzed using statistical analysis. The data has been analyzed with no sectorial biases and tracked by creating real-time indications. The study uses various hypotheses after taking responses from a sample of respondents. Cronbach's Alpha analysis is also used to determine the validity and reliability of the data. The study illustrates the complete shift of consumer behavior from cash to a cashless economy. A certain number of factors are shown to directly influence the rate of such a shift toward digital transactions in India.

• The Journal of Asian Finance, Economics and Business
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• 제8권3호
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• pp.61-67
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• 2021

The covid-19 pandemic scenario caused the most extensive economic shocks the world has experienced in decades. Maintaining financial performance and economic stability is essential during the pandemic period. In these conditions, where movement is severely restricted, media consumption is considered to be increasing. The social media platform is one of the media online used by the public as a source of information and also expressing their sentiment, including individual investors in the capital market as social media users. Twitter is one of the social media microblogging platforms used by individual investors to share their opinion and get information. This study aims to determine whether microblogging sentiment investors can predict the capital market during pandemics. To analyze microblogging sentiment investors, we classified sentiment using the phyton text mining algorithm and Naïve Bayesian text classification into level positive, negative, and neutral from November 2019 to November 2020. This study was on 68 listed companies on the Indonesia stock exchange. A Vector Autoregression and Impulse Response is applied to capture short and long-term impacts along with a causal relationship. We found that microblogging sentiment investor has a significant impact on stock returns and volatility and vice-versa. Also, the response due to shocks is convergent, and microblogging investors in Indonesia are categorized as a "news-watcher" investor.

• 복식문화연구
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• 제28권6호
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• pp.830-844
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• 2020

The fourth Industrial Revolution, known as digital transformation, has made MZ generation to be the focus of the new consumer market, brought about the use of technological platforms a new consumption method. Currently, as various types of content collaboration are emerging that specifically targeting at the MZ generation. Content collaboration are considered an integration of content to create new values through co-existence and co-prosperity. This study identified the characteristics of collaboration of fashion brands from 2018 using literature and online news articles, and identified and classified through case studies of it determined movie content, game and virtual characters. By this research, it shown that collaboration with movie contents have increased the collaborative synergy by using the story in global media content. Collaboration with mobile games was generally used by young casual and sportswear brands. These brands which utilized characters from mobile games popular with to attract more teen consumers and strengthen brand awareness by adding values of high-technology and scarcity to the familiar images. In addition, collaboration with virtual characters has expanded value of the collaborative approach on expanding the range of advanced digital technology, from a promotional strategy during the distribution process through to the use of virtual models. As such, collaboration using the various types of content has developed beyond simple integration of identities among various areas, integrated products or brands that as a new value.

• International Journal of Computer Science & Network Security
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• 제21권1호
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• 복식문화연구
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• 제29권1호
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• pp.1-15
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• 2021

The Internet of Things (IoT) has gained enormous popularity in various fields of industry. An IoT shopping environment is considered an effective tool for convenient use by consumers. Perceived values (including convenience and privacy risks) of IoT shopping can be the main factors that influence consumers' shopping intentions. The current study proposed a research model based on a value-based adoption model, which integrated perceived benefit and sacrifice, shopping attitude, and shopping intention in an IoT shopping environment. As potential customers, participants in their 20s and 30s were recruited through a marketing research firm. Responses collected via an online questionnaire validated the proposed research model and hypothesis. The results confirmed significant, positive relationships between perceived benefit, including both remote control and access convenience, and consumers' positive attitudes toward IoT shopping. The association between perceived privacy risk and consumers' shopping attitudes was not significant. The indirect effects of two benefits of IoT shopping on shopping intention were also significant and positive. From a practical perspective, this study can help marketers and service providers manage their IoT shopping platforms or applications more effectively to attract consumers. The implications and limitations of this study are discussed. Directions for future research and development of IoT shopping environment are suggested.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1238-1249
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• 2020

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

• 트랜스-
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• 제13권
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• pp.91-116
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• 2022

웹 드라마는 인터넷 시대의 산물이라고 한다. 웹 드라마는 전통 텔레비전 드라마와는 다른 10대와 20대 젊은이들이 선호하는 일상적인 내용을 통해 인기를 끌고 주목을 받았다. 스낵 컬처로 인하여 많은 온라인 영상 플랫폼이 웹 드라마를 제작하기 시작했다. 웹 드라마는 스낵 컬처의 영향으로 서사가 종결되는 단막극 형태를 사용하고, 전통 텔레비전 드라마의 선형구조와 다른, 줄거리와 서사가 종결되는 단막극 형태의 영향을 받아 전통 텔레비전 드라마와는 다른 특성을 가진다. 본 연구는 한국에서 가장 큰 청춘 웹 드라마 제작회사 《플레이리스트 오리지널》의 청춘 웹 드라마를 통하여 서사가 종결되는 단막극 형태로 구성되는 청춘 웹 드라마의 특성을 살펴보고 청춘 웹 드라마의 서사가 종결되는 단막극 형태와 비선형 내러티브로 인해 나타나는 서사구조를 분석하고자 한다.

• 한국정보시스템학회지:정보시스템연구
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• 제31권2호
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• pp.197-216
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• 2022

Purpose In this study, in order to understand the effects of NTIS users' behavioral scientific behaviors and determinants, an integrated model of the UTAUT(Unified Theory of Acceptance and Use of Technology) model and TTF(Task-Technology Fit) model, which are frequently used MIS field, was presented and empirical analysis was conducted. Design/methodology/approach In this study, an online survey was conducted on researchers from organizations carrying out national R&D projects, institutions, universities, and dedicated management institutions and the collected data verified hypotheses established using the SPSS 25.0 statistical package and structural equation model using AMOS. Findings The results showed that NTIS users' business activities had a positive(+) effect on task-technology fit and task-technology fit had a positive(+) effect on performance expectation, effort expectation, and continuous intention-to-use intention. In addition, the performance expectation, effort expectation, and promotion conditions presented in research model had a positive(+) effect on the continuous intention-to-use. The research results derived through this study are expected to contribute substantially to subsequent research in the field related to information sharing platforms.

• Asian Journal for Public Opinion Research
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• 제10권2호
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• pp.123-146
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• 2022

This study aims to identify the effect of the relationship between human resources management (HRM) and total quality management (TQM) on improving employee performance. Several previous qualitative studies have stated that TQM and HRM are separate methods. This article describes a new method using a quantitative approach. This research is needed to fill the gap in the literature by empirically analyzing the relationship between HRM, TQM practices, and organizational performance. Data was collected quantitatively from 100 employees in Indonesia through questionnaires and online survey methods. The data collected were analyzed using structural equation modeling (SEM) with the Lisrel 8.5 system. TQM-oriented HRM is operationalized as a second-order latent variable measured by four factors (training, empowerment, teamwork, compensation). The findings support the validity of the TQM-oriented HRM model as a hierarchical, second-order latent construct and show a strong relationship with employee performance. The results of this study are different from previous studies, which showed that TQM and HRM are separate methods. The results of our research provide an academic and practical overview that TQM-oriented HRM can be used to help organizations build platforms for human resources policies aimed at improving employee performance.