• Information Systems Review
• /
• v.19 no.4
• /
• pp.63-85
• /
• 2017

Recent information technology achieves remarkable progress in almost all areas where it can be applied. However, this technology also causes technostress, such as fear and pressure to individuals, due to events, such as the threat of job loss. This technostress is becoming an important factor that can affect user performance and productivity in future society where information technology will be the focus. This kind of stress should be studied considerably in academic and practical applications. The effects of technostress on individual performance remain ambiguous. Therefore, academic research is needed to prove these effects. This study aimed to clarify the direct and indirect effects of technostress on information technology end-users. We developed a research model that integrates innovation resistance and technostress theory through previous studies and analyzed the questionnaire of 317 people. The PLS structural equation model and the study results of Baron and Kenny (1986) indicated that rapid change, connectivity, reliability, and complexity are crucial factors affecting the technostress of information technology. Technostress was analyzed indirectly only through innovation resistance, which affected the performance of end-users. This study will provide new implications for the relationship between technostress and performance or productivity in the IS field.

• The Journal of Engineering Geology
• /
• v.34 no.1
• /
• pp.1-12
• /
• 2024

South Korea's mining industry was actively developed until 1980, but subsequent declining profitability forced many mines to close. Most of the abandoned mines are susceptible to persistent subsidence because of the length of time since mining ceased. Accurate prediction of the locations and times of subsidence is difficult; therefore, this study aims to apply continuum analysis to past cases of subsidence to establish a method of predicting the location and magnitude of future subsidence. The study area is an area of ○○ mining located between the Yangsan fault zone and the Moryang fault zone, in which three subsidence events occurred between 2005 and 2009. Drilling surveys and electrical resistivity surveys were performed at subsidence sites determined the distribution of strata, and through laboratory tests obtained the physico-mechanical properties of the rock. Numerical analysis of the results found that the plastic status area includes the areas of actual subsidence and that continuum analysis can also be used to predict the location and magnitude of subsidence caused by mine goaf.

• Journal of Distribution Research
• /
• v.16 no.1
• /
• pp.95-115
• /
• 2011

Food service is the major part of franchise business in Korea, accounting for 69.9% of the brands in the market. As the food service industry becomes mature, many franchisees have struggled to survive in the market. In general, consumers have higher levels of expectation toward service quality of franchised outlets compared that of (non-franchised) independent ones. They also tend to believe that franchisees deliver standardized service at the uniform food price, regardless of their locations. Such beliefs seem to be important reasons that consumers prefer franchised outlets to independent ones. Nevertheless, few studies examined the impact of qualify features of franchisees on customer satisfaction so far. To this end, this study examined the characteristics of various quality features of franchisees in the food service industry, regarding their relationship with customer satisfaction and dissatisfaction. The quality perception of heavy-users was also compared with that of light-users in order to find insights for developing differentiated marketing strategy for the two segments. Customer satisfaction has been understood as a one-dimensional construct while there are recent studies that insist two-dimensional nature of the construct. In this regard, Kano et al. (1984) suggested to categorize quality features of a product or service into five types, based on their relation to customer satisfaction and dissatisfaction: Must-be quality, Attractive quality, One-dimensional quality, Indifferent quality, and Reverse quality. According to the Kano model, customers are more dissatisfied when Must-be quality(M) are not fulfilled, but their satisfaction does not arise above neutral no matter how fully the quality fulfilled. In comparison, customers are more satisfied with a full provision of Attactive quality(A) but manage to accept its dysfunction. One-dimensional quality(O) results in satisfaction when fulfilled and dissatisfaction when not fulfilled. For Indifferent quality(I), its presence or absence influences neither customer satisfaction nor dissatisfaction. Lastly, Reverse quality(R) refers to the features whose high degree of achievement results in customer dissatisfaction rather than satisfaction. Meanwhile, the basic guidelines of the Kano model have a limitation in that the quality type of each feature is simply determined by calculating the mode statistics. In order to overcome such limitation, the relative importance of each feature on customer satisfaction (Better value; b) and dissatisfaction (Worse value; w) were calculated following the formulas below (Timko, 1993). The Better value indicates how much customer satisfaction is increased by providing the quality feature in question. In contrast, the Worse value indicates how much customer dissatisfaction is decreased by providing the quality feature. Better = (A + O)/(A+O+M+I) Worse = (O+M)/(A+O+M+I)(-1) An on-line survey was performed in order to understand the nature of quality features of franchisees in the food service industry by applying the Kano Model. A total of twenty quality features (refer to the Table 2) were identified as the result of literature review in franchise business and a pre-test with fifty college students in Seoul. The potential respondents of our main survey was limited to the customers who have visited more than two restaurants/stores of the same franchise brand. Survey invitation e-mails were sent out to the panels of a market research company and a total of 257 responses were used for analysis. Following the guidelines of Kano model, each of the twenty quality features was classified into one of the five types based on customers' responses to a set of questions: "(1) how do you feel if the following quality feature is fulfilled in the franchise restaurant that you visit," and "(2) how do you feel if the following quality feature is not fulfilled in the franchise restaurant that you visit." The analyses revealed that customers' dissatisfaction with franchisees is commonly associated with the poor level of cleanliness of the store (w=-0.872), kindness of the staffs(w=-0.890), conveniences such as parking lot and restroom(w=-0.669), and expertise of the staffs(w=-0.492). Such quality features were categorized as Must-be quality in this study. While standardization or uniformity across franchisees has been emphasized in franchise business, this study found that consumers are interested only in uniformity of price across franchisees(w=-0.608), but not interested in standardizations of menu items, interior designs, customer service procedures, and food tastes. Customers appeared to be more satisfied when the franchise brand has promotional events such as giveaways(b=0.767), good accessibility(b=0.699), customer loyalty programs(b=0.659), award winning history(b=0.641), and outlets in the overseas market(b=0.506). The results are summarized in a matrix form in Table 1. Better(b) and Worse(w) index indicate relative importance of each quality feature on customer satisfaction and dissatisfaction, respectively. Meanwhile, there were differences in perceiving the quality features between light users and heavy users of any specific franchise brand in the food service industry. Expertise of the staffs was labeled as Must-be quality for heavy users but Indifferent quality for light users. Light users seemed indifferent to overseas expansion of the brand and offering new menu items on a regular basis, while heavy users appeared to perceive them as Attractive quality. Such difference may come from their different levels of involvement when they eat out. The results are shown in Table 2. The findings of this study help practitioners understand the quality features they need to focus on to strengthen the competitive power in the food service market. Above all, removing the factors that cause customer dissatisfaction seems to be the most critical for franchisees. To retain loyal customers of the franchise brand, it is also recommended for franchisor to invest resources in the development of new menu items as well as training programs for the staffs. Lastly, if resources allow, promotional events, loyalty programs, overseas expansion, award-winning history can be considered as tools for attracting more customers to the business.

• The Korean Journal of Archival Studies
• /
• no.32
• /
• pp.127-176
• /
• 2012

Metadata is well recognized as one of the foundational factors in archiving and long-term preservation of digital resources. There are several metadata standards for records management, archives and preservation, e.g. ISAD(G), EAD, AGRkMs, PREMIS, and OAIS. Consideration is important in selecting appropriate metadata standards in order to design metadata schema that meet the requirements of a particular archival system. Interoperability of metadata with other systems should be considered in schema design. In our previous research, we have presented a feature analysis of metadata standards by identifying the primary resource lifecycle stages where each standard is applied. We have clarified that any single metadata standard cannot cover the whole records lifecycle for archiving and preservation. Through this feature analysis, we analyzed the features of metadata in the whole records lifecycle, and we clarified the relationships between the metadata standards and the stages of the lifecycle. In the previous study, more detailed analysis was left for future study. This paper proposes to analyze the metadata schemas from the viewpoint of tasks performed in the lifecycle. Metadata schemas are primarily defined to describe properties of a resource in accordance with the purposes of description, e.g. finding aids, records management, preservation and so forth. In other words, the metadata standards are resource- and purpose-centric, and the resource lifecycle is not explicitly reflected in the standards. There are no systematic methods for mapping between different metadata standards in accordance with the lifecycle. This paper proposes a method for mapping between metadata standards based on the tasks contained in the resource lifecycle. We first propose a Task Model to clarify tasks applied to resources in each stage of the lifecycle. This model is created as a task-centric model to identify features of metadata standards and to create mappings among elements of those standards. It is important to categorize the elements in order to limit the semantic scope of mapping among elements and decrease the number of combinations of elements for mapping. This paper proposes to use 5W1H (Who, What, Why, When, Where, How) model to categorize the elements. 5W1H categories are generally used for describing events, e.g. news articles. As performing a task on a resource causes an event and metadata elements are used in the event, we consider that the 5W1H categories are adequate to categorize the elements. By using these categories, we determine the features of every element of metadata standards which are AGLS, AGRkMS, PREMIS, EAD, OAIS and an attribute set extracted from DPC decision flow. Then, we perform the element mapping between the standards, and find the relationships between the standards. In this study, we defined a set of terms for each of 5W1H categories, which typically appear in the definition of an element, and used those terms to categorize the elements. For example, if the definition of an element includes the terms such as person and organization that mean a subject which contribute to create, modify a resource the element is categorized into the Who category. A single element can be categorized into one or more 5W1H categories. Thus, we categorized every element of the metadata standards using the 5W1H model, and then, we carried out mapping among the elements in each category. We conclude that the Task Model provides a new viewpoint for metadata schemas and is useful to help us understand the features of metadata standards for records management and archives. The 5W1H model, which is defined based on the Task Model, provides us a core set of categories to semantically classify metadata elements from the viewpoint of an event caused by a task.

• Journal of Life Science
• /
• v.34 no.4
• /
• pp.271-278
• /
• 2024

Redox factor (Ref)-1, a ubiquitously expressed protein, acts as a modulator of redox-sensitive tran- scription factors and as an endonuclease in the repair pathway of damaged DNA. However, the function of Ref-1 in the differentiation of monocytes into macrophages has not been defined. In this study, we investigated the effects of Ref-1 on the monocyte differentiation process using the human monocytic cell line THP-1. The differentiation agent PMA increased cell adhesion over time and showed a sig- nificant increase in phagocytic function but decreased the intracellular amount of Ref-1. Ref-1 inhibitor E3330 and Ref-1 knockdown using the siRNA technique reduced cell adhesion and the expression of differentiation markers, such as CD14, ICAM-1, and CD11b, by PMA stimulation. This means that the role of Ref-1 is absolutely necessary in the initial process of differentiating THP-1 cells stimulated by PMA. Next, the distribution of Ref-1 was examined in the cytoplasm and nucleus of THP-1 cells stimulated with PMA. Surprisingly, PMA stimulation resulted in the rapid translocation of Ref-1 to the nucleus. To prove that movement of Ref-1 to the nucleus is required for monocyte differentiation, a Ref-1 vector with the nuclear localization sequence (NLS) deleted was used. As a result, overexpression of ∆NLS Ref-1, which restricted movement to the nucleus, suppressed the expression of differentiation markers and notably reduced phagocytic function in PMA-stimulated THP-1 cells. In conclusion, these data suggest that the differentiation of monocytic THP-1 cells requires Ref-1 nuclear translocation during the initial process of biochemical events following stimulation from PMA.

• Journal of Life Science
• /
• v.19 no.11
• /
• pp.1529-1537
• /
• 2009

To elucidate further the antitumor effects of a natural L-arginine analogue, L-canavanine, the mechanism underlying apoptogenic activity of L-canavanine and its modulation by protein tyrosine kinase $p56^{lck}$ was investigated in human Jurkat T cells. When the cells were treated with 1.25 to 2.5 mM L-canavanine for 36 h, several apoptotic events including mitochondrial membrane potential (${\Delta\Psi}m$) loss, activation of caspase-9, -3, -8, and -7, poly (ADP-ribose) polymerase (PARP) degradation, and DNA fragmentation were induced without alteration in the levels of Fas or FasL. These apoptotic changes were more significant in $p56^{lck}$-deficient Jurkat clone JCaM1.6 than in $p56^{lck}$-positive Jurkat clone E6.1. The L-canavanine-induced apoptosis observed in $p56^{lck}$-deficient JCaM1.6 cells was significantly reduced by introducing $p56^{lck}$ gene into JCaM1.6 cells by stable transfection. Treatment of JCaM1.6/lck cells with L-canavanine caused a transient 1.6-fold increase in the kinase activity of $p56^{lck}$. Both FADD-positive wild-type Jurkat T cell clone A3 and FADD-deficient Jurkat T cell clone I2.1 exhibited a similar susceptibility to the cytotoxicity of L-canavanine, excluding involvement of Fas/FasL system in triggering L-canavanine-induced apoptosis. The L-canavanine-induced apoptotic sub-$G_1$ peak and activation of caspase-3, -8, and -7 were abrogated by pan-caspase inhibitor (z-VAD-fmk), whereas L-canavanine-induced activation of caspase-9 was not affected. These results demonstrated that L-canavanine caused apoptosis of Jurkat T cells via the loss of ${\Delta\Psi}m$, and the activation of caspase-9, -3, -8, and -7, leading to PARP degradation, and that the $p56^{lck}$ kinase attenuated the ${\Delta\Psi}m$ loss and activation of caspases, and thus contributed as a negative regulator to L-canavanine-induced apoptosis.

• The Journal of the Petrological Society of Korea
• /
• v.14 no.2 s.40
• /
• pp.116-126
• /
• 2005

The Precambrian quartzite and calc-schist layers experienced multi-1310ing events are distributed along the two kinds of U-shaped 1310 (Fold I and II) with $N10^{\circ}E-directed$ fo1d axis in Wollong-myeon, Gwangtan-myeon, Jori-myeon of Paju city, the northeastern part of Gyeonggido. Occurrence of 10 layers of quartzite and 4 layers of calc-schist is not clear whether quartzite and schist layers were deposited sequentially each other or one to two layers of quartzite and schist were distributed repeatedly by isoclinal folding and thrusting, because of lack of sedimentary structures. In this paper, we tried to clarify the correlative relationship among the quartzite beds which are distributed along the U-shaped folds using geochemical tools such as rare earth element (REE) patterns and Nd isotope ratio. Quartzites have characteristics of LREE-flattened, HREE- slightly depleted patterns. They also show Ce negative anomaly whereas there are no Eu anomalies. As a result, quartzite beds occurred along the bilateral sides of fold axis show very similar REE patterns from outer side to inner side of 1314. The Nd model age of quartzite layers shows a trend that the inner part of fold is younger than the outer part of it. Such geochemical characteristics suggest that bilateral quartzite beds occurred along the fold axis were derived from the cogenetic source materials. The REE patterns and trace element geochemistry of mica schist intercalated within quartzite indicate that the quartzite and mica schist may be derived from different source materials. Our results suggest that REE and Nd isotope geochemistries may be very useful in clarifying the relationship of sedimentary deposits which do not show stratigraphical and structural connections in the field.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1117-1132
• /
• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.

• Journal of Intelligence and Information Systems
• /
• v.19 no.2
• /
• pp.1-20
• /
• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.1-23
• /
• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.