• Journal of Korean Society of Forest Science
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• v.108 no.1
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• pp.116-126
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• 2019

The production volume and amount of non-timber forest products in Korea has been on the increase for the past five years. In particular, the production amount of wild vegetables (edible mountain plants) is approximately KRW 400 billion as of 2017, accounting for 14 % of the total production amount of non-timber forest products. Among wild vegetables, especially the production volumes and amounts of bracken, saw-wort (Saussurea), and thistle have grown steadily. Nevertheless, severe price competition with cheap imports and little changes in the pattern of wild vegetable consumption may negatively affect the prices of domestic wild vegetables. This, in turn, can decrease the overall consumption of wild vegetables. Recently, however, consumers have preferred healthy food with increases in their income and interest in health. Therefore, now is a crucial time for the wild vegetable market. Accordingly, this study analyzed consumers' purchase and consumption behavior related to wild vegetables through a consumer survey to contribute to establishing various strategies and policies for promoting the consumption of these vegetables. Also, this study identified consumers' awareness and intention regarding wild vegetables by analyzing social big data. Different from previous studies, this study investigated consumers' awareness and intention by analyzing SNS social big data, as well as conducting a survey. The results of the study will help prioritize strategies and policies for boosting the consumption of wild vegetables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1101-1121
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• 2024

Thorough understanding of user demands and formulation of product development strategies are crucial in product design, and can effectively stimulate consumer behavior. Scientific categorization and classification of demands contribute to accurate design development, design efficiency, and success rates. In recent years, e-commerce has become important consumption platforms for smart wearable products. However, there are few studies on product design and development among those related to promoting platform product services and sales. Meanwhile, design strategies focusing on real consumer needs are scarce among smart wearable product design studies. Therefore, an empirical consumer demand analysis method is proposed and design development strategies are formulated based on a categorized interpretation of demands. Using representative smart bracelets from wearable smart products as a case, this paper classifies consumer demands with three methods: big data semantic analysis, KANO model analysis, and satisfaction analysis. The results reveal that analysis methods proposed herein can effectively classify consumer demands and confirm that differences in consumer demand categories have varying impacts on consumer behavior. On this basis, corresponding design strategies are proposed based on four categories of consumer demands, aiming to make product design the leading factor and promote consumer behavior on e-commerce platforms. This research further enriches demand research on smart wearable products on e-commerce platforms, and optimizes products from a design perspective, thereby promoting consumption. In future research, different data analysis methods will be tried to compare and analyze changes in consumer demands and influencing factors, thus improving research on impact factors of product design in e-commerce.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.105-114
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• 2016

With approximately 20 million transportation card data entries of the metropolitan districts being generated per day, application of the data to management and policy interventions is becoming an issue of interest. The research herein attempts a model of the possibility of dynamic demand change predictions and its purpose is thereby to construct a Dynamic Passengers Trip Assignment Model. The model and algorithm created are targeted at city rail lines operated by seven different transport facilities with the exclusion of travel by bus, as passenger movements by this mode can be minutely disaggregated through card tagging. The model created has been constructed in continuous time as is fitting to the big data characteristic of transport card data, while passenger path choice behavior is effectively represented using a perception parameter as a function of increasing number of transfers. Running the model on 800 pairs of metropolitan city rail data has proven its capability in determining dynamic demand at any moment in time, in line with the typical advantages expected of a continuous time-based model. Comparison against data measured by the eye of existing rail operating facilities to assess changes in congestion intensity shows that the model closely approximates the values and trends of the existing data with high levels of confidence. Future research efforts should be directed toward continued examination into construction of an integrated bus-city rail system model.

• Fashion & Textile Research Journal
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• v.12 no.6
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• pp.745-754
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• 2010

This study is to find return factors when modern consumers purchase goods from an internet shopping mall and then to analyze the characteristics of complaints act and information search behavior. Subjects of research are 245 men and women, who have experience with more than one return in internet fashion shopping mall, in their twenties. The data were analyzed by using Factor analysis, Cronbach's analysis, one-way ANOVA, Duncan test as a post identification, Pearson's correlation analysis and multiple regression analysis. The results of this study are that male and female consumers in their 20s are mainly aware of the return factors: impulse buying, product status, deliver service, service after purchase, hype and comfortableness. And complains behavior often conduct public action, private action, nonaction. Information search behaviors for risk reduction when they purchase are product comparison, oral information search, neutral marketing information search, and service information search. The return factor from the internet fashion shopping had the greatest impact on public action and deliver services factor was a big complaint. In addition, impulse buying & Hype affect private action and non-action is influenced by impulse purchase. The consumer types by the return factors in internet fashion shopping mall are classified into the return group by deliver service, the return group by complex factors, and the return group by product status. Furthermore, there are significant differences in complaining behavior among these groups. In the information search behavior for reduction of risk factors, the return group by complex factors did more active information search behavior than the other groups. The return group by deliver service searched oral information and the return group by product status explored the neutral marketing information.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.1-11
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• 2018

Recently, the volume of online shopping market continues to be fast-growing, that is important to provide customized service based on customer behavior evaluation analysis. The existing systems only provide analysis data on the profiles and behaviors of the consumers, and there is a limit to the processing in real time due to disk based mining. There are problems of accuracy and system performance problems to apply existing systems to web services that require real-time processing and analysis. Therefore, The system proposed in this paper analyzes the web click log streams generated in real time to calculate the concentration level of specific products and finds interested customers which are likely to purchase the products, and provides and intensive promotions to interested customers. And we verify the efficiency and accuracy of the proposed system.

• Smart Media Journal
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• v.5 no.2
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• pp.84-89
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• 2016

For a place-recommendation model based on user's behavior and multi-attribute attitude in this thesis. We focus groups that show similar patterns of visiting restaurants and then compare one and the other. We make use of The Fishbein Equation, Pearson's Correlation Coefficient to calculate multi-attribute attitude scores. Furthermore, We also make use of Preference Prediction Algorithm and Distance based method named "Euclidean Distance" to provide accurate results. We can demonstrate how excellent this system is through several experiments carried out with actual data.

• Journal of Internet Computing and Services
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• v.17 no.5
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• pp.151-160
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• 2016

The purpose of this research is to investigate Korean popular attitudes and social perceptions of 'sharing economy' terminology at the current moment from a creative or socio-economic point of view. In Korea, this study discovers and interprets the objective and tangible annual changes and patterns of sociocultural collective intelligence that have taken place over the last five years by applying text mining in the big data analysis approach. By crawling and Googling, this study collected a significant amount of time series web meta-data with regard to the theme of the sharing economy on the world wide web from 2010 to 2014. Consequently, huge amounts of raw data concerning sharing economy are processed into the value-added meaningful 'word clouding' form of graphs or figures by using the function of word clouding with R programming. Till now, the lack of accumulated data or collective intelligence about sharing economy notwithstanding, it is worth nothing that this study carried out preliminary research on conducting a time-series big data analysis from the perspective of knowledge management and processing. Thus, the results of this study can be utilized as fundamental data to help understand the academic and industrial aspects of future sharing economy-related markets or consumer behavior.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.30-37
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• 2018

The social media data and the broadcasting data related to onion as well as agri-food consumer panel data were collected and investigated if the amount of money spent to purchase onion in year 2014 when onion price plunged latest were correlated with the frequencies of onion-related keywords in the social media data and the broadcasting programs because onion price in year 2018 is expected to plunge due to overproduction and there has been needs to analyze impacts of social media and broadcasting program on onion purchase in the previous similar events, and identify potential factors that can promote onion consumption in advance. What we identified from our study include a) broadcasting news programs mentioning words "onion," were correlated with onion purchase with 3 - 6 weeks in advance; b) broadcasting entertainment programs mentioning words "onion and health," were correlated with onion purchase with 11 weeks in advance; c) blog mentioning words "onion and efficacy," were correlated with onion purchase with 5 weeks in advance. Our study provided a case on how social media and broadcasting programs could be analyzed for their effects on consumer purchase behavior using big data collection and analysis in the field of agriculture. We propose to use the findings from the study may be applied to promote onion consumption.

• Journal of Convergence Society for SMB
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• v.6 no.4
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• pp.31-36
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• 2016

User authentication is the first step to network security. There are lots of authentication types, and more than one authentication method works together for user's authentication in the network. Except for biometric authentication, most authentication methods can be copied, or someone else can adopt and abuse someone else's credential method. Thus, more than one authentication method must be used for user authentication. However, more credential makes system degrade and inefficient as they log on the system. Therefore, without tradeoff performance with efficiency, this research proposed user's behavior based authentication for secure communication, and it will improve to establish a secure and efficient communication.

• The Research Journal of the Costume Culture
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• v.17 no.1
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• pp.90-104
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• 2009

If people want to project a successful personal style, hair style might play a big role as well as clothing. The purpose of this study was to observe hair behavior according to demographic variables, and to analyze correlation between desired hair image and clothing image sought. Questionnaires were distributed to 600 females aged $20s{\sim}40s$, using a convenient sampling method. Only 556 reliable questionnaires were selected for statistical analysis. Correlation r, ANOVA and Chi-square were used to analyze the data, using the SPSS program. There was a significant association between hair style and occupation. Career woman preferred roll straight perm treated hair, students preferred general perm treated hair and full time house wives preferred general wave perm treated. The medium layer cut was the most preferred cut style. People want to give different accents in hair styling with occupation and age variable. Certain desired clothing image had a high correlation with desired hair style image. Those who people want to express sexy, and bohemian image through clothing, they also want to create those image in hair styling with a high correlation(r=0.683, r=0.704).