• Human Ecology Research
• /
• v.55 no.4
• /
• pp.351-362
• /
• 2017

Collaborative consumption (CC) occurs in organized systems or networks in which participants conduct sharing activities in the form of renting, lending, trading, bartering, and swapping of goods, services, transportation solutions, space, or money. Information and communications technologies (ICTs) that have emerged with CC. CC is expected to alleviate social problems such as hyper-consumption, pollution, and poverty by lowering the cost of economic coordination. In this study, we investigate the influence of educational experience and expected benefits of CC participation (intended to using and providing CC) of Adolescent Consumers. The subjects for the study were 418 high school students. Data was analyzed through frequency analysis, mean, standard deviation, t-test, ANOVA, Pearson's correlation, and hierarchical multiple regression analysis using SPSS Win 21.0. The results of this study are as follows. First, the significant positive relationship found between CC participants (intent to use and provide CC), educational experience (home education, school education, and mass media) and expected benefits (social benefit, economic benefit, enjoyment, community effect, and reputation). Second, enjoyment, mass media, reputation, social benefit, home education and school education values were variables that influenced the using participation intention for CC. Third, the major variables influencing the providing participation intention CC were home education, enjoyment, gender, community effect, and mass media values.

• Journal of Information Science Theory and Practice
• /
• v.8 no.3
• /
• pp.6-14
• /
• 2020

On information markets, we can identify different relations between sellers and their customers, with some users paying with money, some paying with attention, and others paying with their personal data. For the description of these different market relations, this article introduces the notion of arity into the scientific discussion. On unary information markets, customers pay with their money; examples include commercial information suppliers. Binary information markets are characterized by one market side paying with attention (e.g., on the search engine Google) or with personal data (e.g., on most social media services) and the other market side (mainly advertisers) paying with money. Our example of a ternary market is a social media market with the additional market side of influencers. If customers buy on unary markets, they know what to pay (in terms of money). If they pay with attention or with their personal data, they do not know what they have to pay exactly in the end. On n-ary markets (n greater than 1), laws should regulate company's abuse of money and-which is new-abuse of data streams with the aid of competition (or anti-trust) laws, and by modified data protection laws, which are guided by fair use of end users' attention and data.

• International journal of advanced smart convergence
• /
• v.10 no.4
• /
• pp.134-140
• /
• 2021

Big data has recently been used in various industries such as tourism, medical care, distribution, and marketing. And it is evolving to the stage of collecting real-time information or analyzing correlations and predicting the future. In the tourism industry, big data can be used to identify the size and shape of the tourism market, and by building and utilizing a large-capacity database, it is possible to establish an efficient marketing strategy and provide customized tourism services for tourists. This paper has begun with anticipation of the effects that would occur when big data is actively used in the tourism field. Because the method of use must have applicability and practicality, the spatial scope will be limited to Seosan, Chungcheongnam-do, and research will be conducted. In this paper, to improve the quality of tourism courses by collecting and analyzing the number of mention data and sentiment index data on social media, which reflect the tourist's interest, preference and satisfaction. Therefore, it is used as basic data necessary for the development of new local tourism courses in the future. In addition, the development of tourism courses will be able to promote tourism growth and also revitalizing the local economy.

• Journal of Distribution Science
• /
• v.19 no.6
• /
• pp.51-64
• /
• 2021

Purpose: A restaurant is a business of the physical distribution of food and beverages which includes intercultural interaction. This research paper aims to examine the influence of Japanese taste as a product advantage, determining service quality factor in the restaurant business, the e-CRM that involves technology, social media campaigns to reach the target market of young people, and customer satisfaction on customer loyalty in Sushi Tei restaurant. This study also intends to find direct and indirect influence between the variables. Research design, data and methodology: Quantitative research was conducted using a survey method by distributing 100 questionnaires to Sushi Tei customers in Jakarta, consisting of Gen Z and Millennials. Samples were taken at each outlet in the Jakarta area on weekend and weekdays by purposive sampling. Data was generated by path analysis Results: The results showed that Japanese taste, service quality, and e-CRM significantly influence customer satisfaction and customer loyalty. However, social media campaigns failed to influence customer loyalty. As a result, customer satisfaction has a significant influence on customer loyalty within Sushi Tei restaurant. Conclusion: The study also proposed significant implications to develop and maintain a relationship with the customer to gain satisfaction and loyalty in the food and beverage industry.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.43 no.3
• /
• pp.427-439
• /
• 2019

This study investigated the semantic network based on the focus of the fashion image and SNS text utilized by global SPA brands on the last seven years in terms of the quantity and quality of data generated by the fast-changing fashion trends and fashion content-based media environment. The research method relocated frequency, density and repetitive key words as well as visualized algorithms using the UCINET 6.347 program and the overall classification of the text related to fashion images on social networks used by global SPA brands. The conclusions of the study are as follows. A common aspect of global SPA brands is that by looking at the basis of text extraction on SNS, exposure through image of products is considered important for sales. The following is a discriminatory aspect of global SPA brands. First, ZARA consistently exposes marketing using a variety of professions and nationalities to SNS. Second, UNIQLO's correlation exposes its collaboration promotion to SNS while steadily exposing basic items. Third, in the case of H&M, some discriminatory results were found with other brands in connectivity with each cluster category that showed remarkably independent results.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.238-246
• /
• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.10
• /
• pp.189-200
• /
• 2022

The gig economy offers many advantages, such as flexibility, variety, independence, and lower cost. However, there are also safety concerns, lack of regulations, uncertainty, and unsatisfactory services, causing people to voice their opinion on social media. This paper aims to explore the sentiments of consumers concerning gig economy services (Grab, Foodpanda and Airbnb) through the analysis of social media. First, Vader Lexicon was used to classify the comments into positive, negative, and neutral sentiments. Then, the comments were further classified into three machine learning algorithms: Support Vector Machine, Light Gradient Boosted Machine, and Logistic Regression. Results suggested that gig economy services in Malaysia received more positive sentiments (52%) than negative sentiments (19%) and neutral sentiments (29%). Based on the three algorithms used in this research, LGBM has been the best model with the highest accuracy of 85%, while SVM has 84% and LR 82%. The results of this study proved the power of text mining and sentiment analysis in extracting business value and providing insight to businesses. Additionally, it aids gig managers and service providers in understanding clients' sentiments about their goods and services and making necessary adjustments to optimize satisfaction.

• The Research Journal of the Costume Culture
• /
• v.29 no.4
• /
• pp.603-615
• /
• 2021

The aim of this study was to analyze the semantic network structure of keywords and the visual composition of images extracted from Instagram in relation to the multi-persona phenomenon with in fashion imagery, which has recently been attracting attention. To this end, the concept of a 'secondary character', which forms a separate identity from a 'main character' on various social media platforms as well as on the airwaves, was considered as the spread of multi-persona and #SecondaryCharacter on Instagram was investigated. 3,801 keywords were collected after crawling the data using Python and morphological analysis was undertaken using KoNLP. The semantic network structure was then examined by conducting a CONCOR analysis using UCINET and Netdraw to determine the top 50 keywords. The results were then classified into a total of 6 clusters. In accordance with the meaning and context of the keywords included in each cluster, group names were assigned : virtual characters, relationship with the main character, hobbies, daily record, N-job person, media and marketing. Image analysis considered the technical, compositional, and social styles of the media based on Gillian Rose's visual analysis method. The results determined that Instagram uses fashion images that virtualize one's face to produce multi-persona representation s that show various occupations, describe different types of hobbies, and depict situations pertaining to various social roles.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.3
• /
• pp.1339-1347
• /
• 2021

Indonesia has a population of more than 260 million, of which, by 2020, Generation Y is predicted to account for 70% of the total. With different birth years, Generation Y is the backbone of Indonesia's product purchasing. Generation Y is interested in establishing strong relationships with specific brands on social media. They are also interested in working with companies to design a product. Most Generation Y utilizes more than one electronic device and they are also brand loyal. Therefore, this study seeks to examine the effect of social media (i.e., e-WOM, online community, and online advertising) on brand image and loyalty in Generation Y in Indonesia. The sampling method employed was purposive sampling. A total of 150 respondents in the age range of 23-30 years were involved as the sample. Using multiple regression model in data analysis, this study proved that e-WOM, not only have a positive and significant effect on the brand image, but also on brand loyalty. Furthermore, online community also positively and significantly affects brand image and brand loyalty. Likewise, online advertising has a positive and significant effect on brand image and brand loyalty. This study's findings indicated that all the proposed hypotheses were well accepted.

• Genomics & Informatics
• /
• v.19 no.3
• /
• pp.21.1-21.5
• /
• 2021

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the coronavirus disease 2019 (COVID-19) pandemic, researchers have turned to more non-traditional sources of clinical data to characterize the disease in near-real time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present. However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations don't generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best-practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.