• Knowledge Management Research
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• v.18 no.3
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• pp.147-162
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• 2017

As social media started to work as important communication tools, social media users have tried to manage their image, identity, and impression through social media. Social media service providers have been interested in providing various functions effectively disclosing users' emotion, such as posting, commenting, and sharing content; on the other hand, relatively few efforts have been made to provide social media functions for information suppression. In this study, therefore, we attempt to examine the relationship between Facebook users' personality and impression management behaviors. Personal traits of users including public self-consciousness, positive self-expression, and honest self-expression were considered as independent variables. Impression management behaviors are composed of two variables, which are suppression and projection. The survey was conducted, targeting 230 Facebook users. The research findings show that public self-consciousness and positive self-expression are positively associated with information suppression while both positive and honest self-expression is positively associated with information projection.

• Knowledge Management Research
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• v.18 no.3
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• pp.63-80
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• 2017

Big Data environment is established by accumulating vast amounts of data as users continuously share and provide personal information in online environment. Accordingly, the more data is accumulated in online environment, the more data is accessible easily by third parties without users' permissions compared to the past. By utilizing strategies based on data-driven, firms recently make it possible to predict customers' preferences and consuming propensity relatively exactly. This Big Data environment, on the other hand, establishes 'Dataveillance' which means anybody can watch or control users' behaviors by using data itself which is stored online. Main objective of this study is to identify the relationship between Dataveillance and users' online privacy protection behaviors. To achieve it, we first investigate perceived online service efficiency; loss of control on privacy; offline surveillance; necessity of regulation influences on users' perceived threats which is generated by Dataveillance.

• Journal of Environmental Health Sciences
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• v.46 no.4
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• pp.388-397
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• 2020

Objectives: The aim of this study was to assess the association between the use of heated tobacco products (HTP) and suicidal behaviors such as suicidal ideation, suicide planning, and suicide attempts among adolescents in South Korea. Methods: The study used a cross-sectional design. Data was from the 15th Korea Youth Risk Behavior Web-based Survey (KYRBS) performed in 2019 by the Ministry of Education, Ministry of Health and Welfare, and Korean Center for Disease Control and Prevention. Heated tobacco product use was used as a main independent variable and suicide-related behaviors such as suicidal ideation, planning, and attempts were applied as dependent variables. Out of 60,100 students, 57,303 responded. Results: HTP use significantly increased the odds of a suicide attempt and suicide planning among adolescents. After controlling for confounders, when comparing 'current HTP users' with 'never HTP users', the adjusted odds ratio (OR) was 1.78 (95% confidence interval (CI): 1.38-2.30) for suicide attempts among adolescents. After controlling the confounders, comparing 'current HTP users' with 'never HTP users', the adjusted OR was 1.36 (95% CI: 1.06-1.73) for suicide planning among adolescents. For sadness/despair among adolescents, when comparing 'current HTP users' with 'never HTP users', the adjusted OR was 1.29 (95% CI: 1.11-1.50). However, HTP use had no significant association with suicidal ideation among adolescents. Conclusions: Current HTP users were more likely to attempt to commit suicide, and more likely to plan to commit suicide than never HTP users among adolescents. These results may be useful in developing a scientific basis for designing suicide prevention programs targeting adolescents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2069-2085
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• 2021

A Social Networking Service (SNS) platform provides digital footprints to discover users' interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users' relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users' interest and social influence, where the user interest model relies on historical user behavior and the seed users' evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users' past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

• Korean Journal of Human Ecology
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• v.21 no.5
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• pp.957-973
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• 2012

This study analyzed consumer public complaint behaviors and the satisfaction of complaint handling among credit card users who availed of credit card services. Relatively little research has been done in this area, despite the obvious importance of understanding and improving credit card market conditions. The purpose of this study was to examine consumer compliant behaviors with a focus on public actions, such as voice responses and the third party actions among credit card users. With the goal of providing consumers with more positive expectations of credit card companies' complaint handling process, this study investigated the status of public actions and the negative effect of complaints on the overall satisfaction of post-complaint behavior toward credit card services. The responses from 1,000 credit card users were analyzed using descriptive analysis, factor analysis, multi-logit analysis, and Heckman selection estimate. The analysis provided three major results: (1) perceived service quality among credit card users was conceptualized into groups such as responsiveness, innovation, company, additional service, and fee, (2) perceived service qualities, age, residential area, employment status, and subjective economic status had significant effect on public compliant action behaviors, and (3) unidimensional factors resulting from post-complaint behaviors were analyzed and several variables, such as period of credit card use, average amount used, and perceived service quality had significant effects on the degree of satisfaction associated with complaint handling in terms of credit card services. Several implications and directions for further research are discussed.

• Asia pacific journal of information systems
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• v.33 no.2
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• pp.389-417
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• 2023

Social distancing required the use of doorstep delivery for nearly all purchases during the COVID-19 pandemic. Foreign users in Korea are forced to participate in superfluous tasks, leading to an increase in their anxiety and fatigue while online shopping. This study examines how digital shadow work stemming from the language barrier can affect the emotions and behaviors of foreign shoppers that use Korean shopping sites. By interviewing 37 foreign users in Korea, this draft examined their experiences, behaviors, and emotional output, classifying them into 14 codes and seven categories. Using grounded theory, we found that online shoppers' emotions, feelings, experiences, and decision making may be changed in the stages of the pre-use, use, and post-use activities. User responses regarding shadow work and related obstacles can be seen with the continue, discontinue, and optional (occasional use) of Korean online shopping sites. Pleasure and satisfaction come from high efficiency and privileges, whereas anger and disappointment come from poor self-confidence and pessimism. Furthermore, buyer behavior and product orientation are identified as intervening conditions, while the online vs. offline shopping experiences are identified as contextual conditions. In conclusion, language barriers and other factors make online shopping difficult for foreign shoppers, which negatively affects their psychological mechanisms and buying behaviors. The implications from the study findings and future research are also discussed.

• Korean Parent-Child Health Journal
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• v.8 no.2
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• pp.112-122
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• 2005

Purpose: This study was done to investigate the relation to perceived maternal child rearing behaviors and the level of internet addiction in the upper year grade students. Method: Data was collected through self-report questionnaires in which perceived maternal child rearing behaviors and internet addiction. This study population was comprised of 668 students who enrolled 4~6 year-grade in Kwangwon-Do. The data collected was analyzed by the SPSS program. Results: The level of internet addiction of subjects was rather low. Of the children, 88.2% reported being average on-line users, 7.3%, heavy on-line users, and 4.5%, internet addicted. Gender, existence of father, mother's attitude when child overuse on-line, average playing time of on-line per day, frequency of on-line visits per week and purpose of on-line use for average on-line users were different from that of heavy on-line users. The level of perceived maternal child rearing behaviors were abbreviate positively correlated to the level of internet addiction in subjects. Conclusion: We suggest these results be used to develop a internet addiction prevention program.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.533-540
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• 2012

Objective: This paper targets a framework of a hand gesture based interface design. Background: While a modeling of contact-based interfaces has focused on users' ergonomic interface designs and real-time technologies, an implementation of a contactless interface needs error-free classifications as an essential prior condition. These trends made many research studies concentrate on the designs of feature vectors, learning models and their tests. Even though there have been remarkable advances in this field, the ignorance of ergonomics and users' cognitions result in several problems including a user's uneasy behaviors. Method: In order to incorporate compatibilities considering users' comfortable behaviors and device's classification abilities simultaneously, classification-oriented gestures are extracted using the suggested human-hand model and closed-loop classification procedures. Out of the extracted gestures, the compatibility-oriented gestures are acquired though human's ergonomic and cognitive experiments. Then, the obtained hand gestures are converted into a series of hand behaviors - Handycon - which is mapped into several functions in a mobile device. Results: This Handycon model guarantees users' easy behavior and helps fast understandings as well as the high classification rate. Conclusion and Application: The suggested framework contributes to develop a hand gesture-based contactless interface model considering compatibilities between human and device. The suggested procedures can be applied effectively into other contactless interface designs.

• Journal of Communications and Networks
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• v.17 no.6
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• pp.568-581
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• 2015

Predicting locations of users with portable devices such as IP phones, smart-phones, iPads and iPods in public wireless local area networks (WLANs) plays a crucial role in location management and network resource allocation. Many techniques in machine learning and data mining, such as sequential pattern mining and clustering, have been widely used. However, these approaches have two deficiencies. First, because they are based on profiles of individual mobility behaviors, a sequential pattern technique may fail to predict new users or users with movement on novel paths. Second, using similar mobility behaviors in a cluster for predicting the movement of users may cause significant degradation in accuracy owing to indistinguishable regular movement and random movement. In this paper, we propose a novel fusion technique that utilizes mobility rules discovered from multiple similar users by combining clustering and sequential pattern mining. The proposed technique with two algorithms, named the clustering-based-sequential-pattern-mining (CSPM) and sequential-pattern-mining-based-clustering (SPMC), can deal with the lack of information in a personal profile and avoid some noise due to random movements by users. Experimental results show that our approach outperforms existing approaches in terms of efficiency and prediction accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.